Data-cleaning.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "8dcf09e5",
   "metadata": {},
   "source": [
    "# If you are reading this on Github..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b056be39",
   "metadata": {},
   "source": [
    "* Links within notebooks (except for link at the end) **may not work properly**!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4faa22f2",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d61efc",
   "metadata": {},
   "source": [
    "#### Aim: Predicting customer expenditure using customer information ([Source of dataset (Kaggle)](https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis))\n",
    "#### What we going to do in this notebook:\n",
    "1. Clean dataset\n",
    "2. Give justification for every action"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c35c03a2",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2a65af",
   "metadata": {},
   "source": [
    "## (0) Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "229e62d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "sb.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9285961",
   "metadata": {},
   "outputs": [],
   "source": [
    "def histogramCountOld(dataset,x,axes = [],i = 0,j = 0,meta = {\"height\": 4.8, \"width\": 6.4}):\n",
    "    \n",
    "    import seaborn as sb\n",
    "    \n",
    "    f = plt.figure(figsize=(meta[\"height\"],meta[\"width\"]))\n",
    "    \n",
    "    #Cite M2 ExploratoryAnalysis\n",
    "    print(\"Number of\", x ,\":\", len(dataset[x].unique()))\n",
    "    \n",
    "    print(dataset[x].value_counts())\n",
    "    if(len(axes) != 0):\n",
    "        sb.countplot(y=x, data=dataset, ax = axes[i,j], orient = \"h\")\n",
    "    else:\n",
    "        sb.catplot(y = x, data = dataset, kind = \"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "358eb261",
   "metadata": {},
   "outputs": [],
   "source": [
    "customerData = pd.read_csv('dataset\\marketing_campaign.csv',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2070d734",
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "      <td>1981</td>\n",
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       "      <td>9405</td>\n",
       "      <td>1954</td>\n",
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       "<p>2240 rows × 29 columns</p>\n",
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      ],
      "text/plain": [
       "         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \\\n",
       "0      5524        1957  Graduation         Single  58138.0        0   \n",
       "1      2174        1954  Graduation         Single  46344.0        1   \n",
       "2      4141        1965  Graduation       Together  71613.0        0   \n",
       "3      6182        1984  Graduation       Together  26646.0        1   \n",
       "4      5324        1981         PhD        Married  58293.0        1   \n",
       "...     ...         ...         ...            ...      ...      ...   \n",
       "2235  10870        1967  Graduation        Married  61223.0        0   \n",
       "2236   4001        1946         PhD       Together  64014.0        2   \n",
       "2237   7270        1981  Graduation       Divorced  56981.0        0   \n",
       "2238   8235        1956      Master       Together  69245.0        0   \n",
       "2239   9405        1954         PhD        Married  52869.0        1   \n",
       "\n",
       "      Teenhome Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  \\\n",
       "0            0  04-09-2012       58       635  ...                  7   \n",
       "1            1  08-03-2014       38        11  ...                  5   \n",
       "2            0  21-08-2013       26       426  ...                  4   \n",
       "3            0  10-02-2014       26        11  ...                  6   \n",
       "4            0  19-01-2014       94       173  ...                  5   \n",
       "...        ...         ...      ...       ...  ...                ...   \n",
       "2235         1  13-06-2013       46       709  ...                  5   \n",
       "2236         1  10-06-2014       56       406  ...                  7   \n",
       "2237         0  25-01-2014       91       908  ...                  6   \n",
       "2238         1  24-01-2014        8       428  ...                  3   \n",
       "2239         1  15-10-2012       40        84  ...                  7   \n",
       "\n",
       "      AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  \\\n",
       "0                0             0             0             0             0   \n",
       "1                0             0             0             0             0   \n",
       "2                0             0             0             0             0   \n",
       "3                0             0             0             0             0   \n",
       "4                0             0             0             0             0   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "2235             0             0             0             0             0   \n",
       "2236             0             0             0             1             0   \n",
       "2237             0             1             0             0             0   \n",
       "2238             0             0             0             0             0   \n",
       "2239             0             0             0             0             0   \n",
       "\n",
       "      Complain  Z_CostContact  Z_Revenue  Response  \n",
       "0            0              3         11         1  \n",
       "1            0              3         11         0  \n",
       "2            0              3         11         0  \n",
       "3            0              3         11         0  \n",
       "4            0              3         11         0  \n",
       "...        ...            ...        ...       ...  \n",
       "2235         0              3         11         0  \n",
       "2236         0              3         11         0  \n",
       "2237         0              3         11         0  \n",
       "2238         0              3         11         0  \n",
       "2239         0              3         11         1  \n",
       "\n",
       "[2240 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerData"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e27c28a",
   "metadata": {},
   "source": [
    "We copy the dataset before doing cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f1c445f",
   "metadata": {},
   "outputs": [],
   "source": [
    "customerData_Clean = customerData.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153c1602",
   "metadata": {},
   "source": [
    "## (1) Data Cleaning: Removing NULL Data in Income Column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4bd09e1",
   "metadata": {},
   "source": [
    "We realise there are some NULL data in income column, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b398bb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([  10,   27,   43,   48,   58,   71,   90,   91,   92,  128,  133,\n",
      "             312,  319, 1379, 1382, 1383, 1386, 2059, 2061, 2078, 2079, 2081,\n",
      "            2084, 2228],\n",
      "           dtype='int64')\n",
      "24\n"
     ]
    }
   ],
   "source": [
    "null_Income = customerData_Clean[customerData_Clean[\"Income\"].isnull() == True]\n",
    "print(null_Income.index)\n",
    "print(len(null_Income.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df691f8",
   "metadata": {},
   "source": [
    "After checking the number of NULL data, we decide to remove it because there are only a few of NULL Data, and filling median may affect the data as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36254d5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 27, 43, 48, 58, 71, 90, 91, 92, 128, 133, 312, 319, 1379, 1382, 1383, 1386, 2059, 2061, 2078, 2079, 2081, 2084, 2228]\n"
     ]
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       "      <th>Recency</th>\n",
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       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>04-09-2012</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2174</td>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>08-03-2014</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4141</td>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21-08-2013</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6182</td>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10-02-2014</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5324</td>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19-01-2014</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>10870</td>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13-06-2013</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>4001</td>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Together</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10-06-2014</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>7270</td>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-01-2014</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>8235</td>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Together</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24-01-2014</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>9405</td>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15-10-2012</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2216 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \\\n",
       "0      5524        1957  Graduation         Single  58138.0        0   \n",
       "1      2174        1954  Graduation         Single  46344.0        1   \n",
       "2      4141        1965  Graduation       Together  71613.0        0   \n",
       "3      6182        1984  Graduation       Together  26646.0        1   \n",
       "4      5324        1981         PhD        Married  58293.0        1   \n",
       "...     ...         ...         ...            ...      ...      ...   \n",
       "2211  10870        1967  Graduation        Married  61223.0        0   \n",
       "2212   4001        1946         PhD       Together  64014.0        2   \n",
       "2213   7270        1981  Graduation       Divorced  56981.0        0   \n",
       "2214   8235        1956      Master       Together  69245.0        0   \n",
       "2215   9405        1954         PhD        Married  52869.0        1   \n",
       "\n",
       "      Teenhome Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  \\\n",
       "0            0  04-09-2012       58       635  ...                  7   \n",
       "1            1  08-03-2014       38        11  ...                  5   \n",
       "2            0  21-08-2013       26       426  ...                  4   \n",
       "3            0  10-02-2014       26        11  ...                  6   \n",
       "4            0  19-01-2014       94       173  ...                  5   \n",
       "...        ...         ...      ...       ...  ...                ...   \n",
       "2211         1  13-06-2013       46       709  ...                  5   \n",
       "2212         1  10-06-2014       56       406  ...                  7   \n",
       "2213         0  25-01-2014       91       908  ...                  6   \n",
       "2214         1  24-01-2014        8       428  ...                  3   \n",
       "2215         1  15-10-2012       40        84  ...                  7   \n",
       "\n",
       "      AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  \\\n",
       "0                0             0             0             0             0   \n",
       "1                0             0             0             0             0   \n",
       "2                0             0             0             0             0   \n",
       "3                0             0             0             0             0   \n",
       "4                0             0             0             0             0   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "2211             0             0             0             0             0   \n",
       "2212             0             0             0             1             0   \n",
       "2213             0             1             0             0             0   \n",
       "2214             0             0             0             0             0   \n",
       "2215             0             0             0             0             0   \n",
       "\n",
       "      Complain  Z_CostContact  Z_Revenue  Response  \n",
       "0            0              3         11         1  \n",
       "1            0              3         11         0  \n",
       "2            0              3         11         0  \n",
       "3            0              3         11         0  \n",
       "4            0              3         11         0  \n",
       "...        ...            ...        ...       ...  \n",
       "2211         0              3         11         0  \n",
       "2212         0              3         11         0  \n",
       "2213         0              3         11         0  \n",
       "2214         0              3         11         0  \n",
       "2215         0              3         11         1  \n",
       "\n",
       "[2216 rows x 29 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(null_Income.index.tolist())\n",
    "customerData_Clean.drop(index = null_Income.index.tolist(),inplace=True)\n",
    "customerData_Clean.reset_index(drop=True, inplace=True)\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6895ef02",
   "metadata": {},
   "source": [
    "## (2) <a name=\"addingCol\"></a>Data Cleaning: Adding columns - Grocery Products, Total Purchase, AcceptedCmp, Year Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57840a68",
   "metadata": {},
   "source": [
    "As mentioned in [exploratory](Data-exploratory.ipynb) notebook, we decide to introduce 4 new variables to our dataset:\n",
    "\n",
    "#### (1) MntGroceryProducts -  \n",
    "Amount spent on groceries (fruits, meat, fish and sweets) in last 2 years.\n",
    "\n",
    "#### (2) TotalPurchase -  \n",
    "Total number of purchases made by the customers in this store.\n",
    "\n",
    "#### (3) YearRange -  \n",
    "Categorise customer's birth year into every 10 year.  \n",
    "\n",
    "#### (4) TotalChild -  \n",
    "Customers' total number of children.\n",
    "\n",
    "#### (5) HaveChild -  \n",
    "1 if customer have children, 0 otherwise\n",
    "  \n",
    "##### We decide to adding these variables, because  \n",
    "(1) this helps with the training later. (MntGroceryProducts, TotalPurchase)  \n",
    "(2) some columns does not give enough meaning, so we combine them to have a better feeling about what they really mean (AcceptedCmp, YearRange, TotalChild, HaveChild)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87ec3069",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year_Birth</th>\n",
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       "      <th>MntWines</th>\n",
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       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>287</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10-02-2014</td>\n",
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       "      <td>1981</td>\n",
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       "      <th>2211</th>\n",
       "      <td>10870</td>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13-06-2013</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>4001</td>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Together</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10-06-2014</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>7270</td>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-01-2014</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>8235</td>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Together</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24-01-2014</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>9405</td>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15-10-2012</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2216 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \\\n",
       "0      5524        1957  Graduation         Single  58138.0        0   \n",
       "1      2174        1954  Graduation         Single  46344.0        1   \n",
       "2      4141        1965  Graduation       Together  71613.0        0   \n",
       "3      6182        1984  Graduation       Together  26646.0        1   \n",
       "4      5324        1981         PhD        Married  58293.0        1   \n",
       "...     ...         ...         ...            ...      ...      ...   \n",
       "2211  10870        1967  Graduation        Married  61223.0        0   \n",
       "2212   4001        1946         PhD       Together  64014.0        2   \n",
       "2213   7270        1981  Graduation       Divorced  56981.0        0   \n",
       "2214   8235        1956      Master       Together  69245.0        0   \n",
       "2215   9405        1954         PhD        Married  52869.0        1   \n",
       "\n",
       "      Teenhome Dt_Customer  Recency  MntWines  ...  AcceptedCmp3  \\\n",
       "0            0  04-09-2012       58       635  ...             0   \n",
       "1            1  08-03-2014       38        11  ...             0   \n",
       "2            0  21-08-2013       26       426  ...             0   \n",
       "3            0  10-02-2014       26        11  ...             0   \n",
       "4            0  19-01-2014       94       173  ...             0   \n",
       "...        ...         ...      ...       ...  ...           ...   \n",
       "2211         1  13-06-2013       46       709  ...             0   \n",
       "2212         1  10-06-2014       56       406  ...             0   \n",
       "2213         0  25-01-2014       91       908  ...             0   \n",
       "2214         1  24-01-2014        8       428  ...             0   \n",
       "2215         1  15-10-2012       40        84  ...             0   \n",
       "\n",
       "      AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  Complain  \\\n",
       "0                0             0             0             0         0   \n",
       "1                0             0             0             0         0   \n",
       "2                0             0             0             0         0   \n",
       "3                0             0             0             0         0   \n",
       "4                0             0             0             0         0   \n",
       "...            ...           ...           ...           ...       ...   \n",
       "2211             0             0             0             0         0   \n",
       "2212             0             0             1             0         0   \n",
       "2213             1             0             0             0         0   \n",
       "2214             0             0             0             0         0   \n",
       "2215             0             0             0             0         0   \n",
       "\n",
       "      Z_CostContact  Z_Revenue  Response  MntGroceryProducts  \n",
       "0                 3         11         1                 806  \n",
       "1                 3         11         0                   9  \n",
       "2                 3         11         0                 287  \n",
       "3                 3         11         0                  34  \n",
       "4                 3         11         0                 207  \n",
       "...             ...        ...       ...                 ...  \n",
       "2211              3         11         0                 267  \n",
       "2212              3         11         0                  30  \n",
       "2213              3         11         0                 297  \n",
       "2214              3         11         0                 324  \n",
       "2215              3         11         1                  66  \n",
       "\n",
       "[2216 rows x 30 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerData_Clean.loc[:,'MntGroceryProducts'] = customerData_Clean['MntMeatProducts'] + customerData_Clean['MntFishProducts'] + customerData_Clean['MntFruits']\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "416b9fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
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       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>...</th>\n",
       "      <th>AcceptedCmp4</th>\n",
       "      <th>AcceptedCmp5</th>\n",
       "      <th>AcceptedCmp1</th>\n",
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       "      <th>Z_CostContact</th>\n",
       "      <th>Z_Revenue</th>\n",
       "      <th>Response</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5524</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2174</td>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>08-03-2014</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4141</td>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21-08-2013</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>287</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6182</td>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10-02-2014</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5324</td>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19-01-2014</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>10870</td>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13-06-2013</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>267</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>4001</td>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Together</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10-06-2014</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>7270</td>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-01-2014</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>297</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>8235</td>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Together</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24-01-2014</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>9405</td>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15-10-2012</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2216 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \\\n",
       "0      5524        1957  Graduation         Single  58138.0        0   \n",
       "1      2174        1954  Graduation         Single  46344.0        1   \n",
       "2      4141        1965  Graduation       Together  71613.0        0   \n",
       "3      6182        1984  Graduation       Together  26646.0        1   \n",
       "4      5324        1981         PhD        Married  58293.0        1   \n",
       "...     ...         ...         ...            ...      ...      ...   \n",
       "2211  10870        1967  Graduation        Married  61223.0        0   \n",
       "2212   4001        1946         PhD       Together  64014.0        2   \n",
       "2213   7270        1981  Graduation       Divorced  56981.0        0   \n",
       "2214   8235        1956      Master       Together  69245.0        0   \n",
       "2215   9405        1954         PhD        Married  52869.0        1   \n",
       "\n",
       "      Teenhome Dt_Customer  Recency  MntWines  ...  AcceptedCmp4  \\\n",
       "0            0  04-09-2012       58       635  ...             0   \n",
       "1            1  08-03-2014       38        11  ...             0   \n",
       "2            0  21-08-2013       26       426  ...             0   \n",
       "3            0  10-02-2014       26        11  ...             0   \n",
       "4            0  19-01-2014       94       173  ...             0   \n",
       "...        ...         ...      ...       ...  ...           ...   \n",
       "2211         1  13-06-2013       46       709  ...             0   \n",
       "2212         1  10-06-2014       56       406  ...             0   \n",
       "2213         0  25-01-2014       91       908  ...             1   \n",
       "2214         1  24-01-2014        8       428  ...             0   \n",
       "2215         1  15-10-2012       40        84  ...             0   \n",
       "\n",
       "      AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  Complain  Z_CostContact  \\\n",
       "0                0             0             0         0              3   \n",
       "1                0             0             0         0              3   \n",
       "2                0             0             0         0              3   \n",
       "3                0             0             0         0              3   \n",
       "4                0             0             0         0              3   \n",
       "...            ...           ...           ...       ...            ...   \n",
       "2211             0             0             0         0              3   \n",
       "2212             0             1             0         0              3   \n",
       "2213             0             0             0         0              3   \n",
       "2214             0             0             0         0              3   \n",
       "2215             0             0             0         0              3   \n",
       "\n",
       "      Z_Revenue  Response  MntGroceryProducts  TotalPurchase  \n",
       "0            11         1                 806             25  \n",
       "1            11         0                   9              6  \n",
       "2            11         0                 287             21  \n",
       "3            11         0                  34              8  \n",
       "4            11         0                 207             19  \n",
       "...         ...       ...                 ...            ...  \n",
       "2211         11         0                 267             18  \n",
       "2212         11         0                  30             22  \n",
       "2213         11         0                 297             19  \n",
       "2214         11         0                 324             23  \n",
       "2215         11         1                  66             11  \n",
       "\n",
       "[2216 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerData_Clean.loc[:,'TotalPurchase'] = customerData_Clean['NumDealsPurchases'] + customerData_Clean['NumWebPurchases'] + customerData_Clean['NumCatalogPurchases'] + customerData_Clean['NumStorePurchases']\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fe176cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2174</td>\n",
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       "      <td>0</td>\n",
       "      <td>287</td>\n",
       "      <td>21</td>\n",
       "      <td>1960</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6182</td>\n",
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       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>34</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5324</td>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19-01-2014</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
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       "      <td>0</td>\n",
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       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
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       "      <td>1980</td>\n",
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       "      <th>2211</th>\n",
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       "      <td>18</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>22</td>\n",
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       "      <td>7270</td>\n",
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       "      <td>0</td>\n",
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       "      <td>91</td>\n",
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       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "      <td>1950</td>\n",
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       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>9405</td>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15-10-2012</td>\n",
       "      <td>40</td>\n",
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       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>1950</td>\n",
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      ],
      "text/plain": [
       "         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \\\n",
       "0      5524        1957  Graduation         Single  58138.0        0   \n",
       "1      2174        1954  Graduation         Single  46344.0        1   \n",
       "2      4141        1965  Graduation       Together  71613.0        0   \n",
       "3      6182        1984  Graduation       Together  26646.0        1   \n",
       "4      5324        1981         PhD        Married  58293.0        1   \n",
       "...     ...         ...         ...            ...      ...      ...   \n",
       "2211  10870        1967  Graduation        Married  61223.0        0   \n",
       "2212   4001        1946         PhD       Together  64014.0        2   \n",
       "2213   7270        1981  Graduation       Divorced  56981.0        0   \n",
       "2214   8235        1956      Master       Together  69245.0        0   \n",
       "2215   9405        1954         PhD        Married  52869.0        1   \n",
       "\n",
       "      Teenhome Dt_Customer  Recency  MntWines  ...  AcceptedCmp5  \\\n",
       "0            0  04-09-2012       58       635  ...             0   \n",
       "1            1  08-03-2014       38        11  ...             0   \n",
       "2            0  21-08-2013       26       426  ...             0   \n",
       "3            0  10-02-2014       26        11  ...             0   \n",
       "4            0  19-01-2014       94       173  ...             0   \n",
       "...        ...         ...      ...       ...  ...           ...   \n",
       "2211         1  13-06-2013       46       709  ...             0   \n",
       "2212         1  10-06-2014       56       406  ...             0   \n",
       "2213         0  25-01-2014       91       908  ...             0   \n",
       "2214         1  24-01-2014        8       428  ...             0   \n",
       "2215         1  15-10-2012       40        84  ...             0   \n",
       "\n",
       "      AcceptedCmp1  AcceptedCmp2  Complain  Z_CostContact  Z_Revenue  \\\n",
       "0                0             0         0              3         11   \n",
       "1                0             0         0              3         11   \n",
       "2                0             0         0              3         11   \n",
       "3                0             0         0              3         11   \n",
       "4                0             0         0              3         11   \n",
       "...            ...           ...       ...            ...        ...   \n",
       "2211             0             0         0              3         11   \n",
       "2212             1             0         0              3         11   \n",
       "2213             0             0         0              3         11   \n",
       "2214             0             0         0              3         11   \n",
       "2215             0             0         0              3         11   \n",
       "\n",
       "      Response  MntGroceryProducts  TotalPurchase  YearRange  \n",
       "0            1                 806             25       1950  \n",
       "1            0                   9              6       1950  \n",
       "2            0                 287             21       1960  \n",
       "3            0                  34              8       1980  \n",
       "4            0                 207             19       1980  \n",
       "...        ...                 ...            ...        ...  \n",
       "2211         0                 267             18       1960  \n",
       "2212         0                  30             22       1940  \n",
       "2213         0                 297             19       1980  \n",
       "2214         0                 324             23       1950  \n",
       "2215         1                  66             11       1950  \n",
       "\n",
       "[2216 rows x 32 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerData_Clean['YearRange'] = customerData_Clean[\"Year_Birth\"] / 10\n",
    "customerData_Clean['YearRange'] = customerData_Clean['YearRange'].astype(\"int64\")\n",
    "customerData_Clean['YearRange'] = customerData_Clean[\"YearRange\"] * 10\n",
    "customerData_Clean['YearRange'] = customerData_Clean['YearRange'].astype(\"category\")\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1f6fe53",
   "metadata": {},
   "outputs": [
    {
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       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5324</td>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19-01-2014</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>10870</td>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13-06-2013</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>267</td>\n",
       "      <td>18</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>4001</td>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Together</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10-06-2014</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1940</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>7270</td>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-01-2014</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>297</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>8235</td>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Together</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24-01-2014</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>9405</td>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15-10-2012</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2216 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \\\n",
       "0      5524        1957  Graduation         Single  58138.0        0   \n",
       "1      2174        1954  Graduation         Single  46344.0        1   \n",
       "2      4141        1965  Graduation       Together  71613.0        0   \n",
       "3      6182        1984  Graduation       Together  26646.0        1   \n",
       "4      5324        1981         PhD        Married  58293.0        1   \n",
       "...     ...         ...         ...            ...      ...      ...   \n",
       "2211  10870        1967  Graduation        Married  61223.0        0   \n",
       "2212   4001        1946         PhD       Together  64014.0        2   \n",
       "2213   7270        1981  Graduation       Divorced  56981.0        0   \n",
       "2214   8235        1956      Master       Together  69245.0        0   \n",
       "2215   9405        1954         PhD        Married  52869.0        1   \n",
       "\n",
       "      Teenhome Dt_Customer  Recency  MntWines  ...  AcceptedCmp1  \\\n",
       "0            0  04-09-2012       58       635  ...             0   \n",
       "1            1  08-03-2014       38        11  ...             0   \n",
       "2            0  21-08-2013       26       426  ...             0   \n",
       "3            0  10-02-2014       26        11  ...             0   \n",
       "4            0  19-01-2014       94       173  ...             0   \n",
       "...        ...         ...      ...       ...  ...           ...   \n",
       "2211         1  13-06-2013       46       709  ...             0   \n",
       "2212         1  10-06-2014       56       406  ...             1   \n",
       "2213         0  25-01-2014       91       908  ...             0   \n",
       "2214         1  24-01-2014        8       428  ...             0   \n",
       "2215         1  15-10-2012       40        84  ...             0   \n",
       "\n",
       "      AcceptedCmp2  Complain  Z_CostContact  Z_Revenue  Response  \\\n",
       "0                0         0              3         11         1   \n",
       "1                0         0              3         11         0   \n",
       "2                0         0              3         11         0   \n",
       "3                0         0              3         11         0   \n",
       "4                0         0              3         11         0   \n",
       "...            ...       ...            ...        ...       ...   \n",
       "2211             0         0              3         11         0   \n",
       "2212             0         0              3         11         0   \n",
       "2213             0         0              3         11         0   \n",
       "2214             0         0              3         11         0   \n",
       "2215             0         0              3         11         1   \n",
       "\n",
       "      MntGroceryProducts  TotalPurchase  YearRange  TotalChild  \n",
       "0                    806             25       1950           0  \n",
       "1                      9              6       1950           2  \n",
       "2                    287             21       1960           0  \n",
       "3                     34              8       1980           1  \n",
       "4                    207             19       1980           1  \n",
       "...                  ...            ...        ...         ...  \n",
       "2211                 267             18       1960           1  \n",
       "2212                  30             22       1940           3  \n",
       "2213                 297             19       1980           0  \n",
       "2214                 324             23       1950           1  \n",
       "2215                  66             11       1950           2  \n",
       "\n",
       "[2216 rows x 33 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerData_Clean.loc[:,'TotalChild'] = customerData_Clean['Kidhome'].astype(\"int64\") + customerData_Clean['Teenhome'].astype(\"int64\")\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a0252e4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Dt_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>...</th>\n",
       "      <th>AcceptedCmp2</th>\n",
       "      <th>Complain</th>\n",
       "      <th>Z_CostContact</th>\n",
       "      <th>Z_Revenue</th>\n",
       "      <th>Response</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5524</td>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>04-09-2012</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>806</td>\n",
       "      <td>25</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2174</td>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>08-03-2014</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4141</td>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21-08-2013</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>287</td>\n",
       "      <td>21</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6182</td>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10-02-2014</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5324</td>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19-01-2014</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>10870</td>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13-06-2013</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>267</td>\n",
       "      <td>18</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>4001</td>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Together</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10-06-2014</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1940</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>7270</td>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25-01-2014</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>297</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>8235</td>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Together</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24-01-2014</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>9405</td>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15-10-2012</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2216 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \\\n",
       "0      5524        1957  Graduation         Single  58138.0        0   \n",
       "1      2174        1954  Graduation         Single  46344.0        1   \n",
       "2      4141        1965  Graduation       Together  71613.0        0   \n",
       "3      6182        1984  Graduation       Together  26646.0        1   \n",
       "4      5324        1981         PhD        Married  58293.0        1   \n",
       "...     ...         ...         ...            ...      ...      ...   \n",
       "2211  10870        1967  Graduation        Married  61223.0        0   \n",
       "2212   4001        1946         PhD       Together  64014.0        2   \n",
       "2213   7270        1981  Graduation       Divorced  56981.0        0   \n",
       "2214   8235        1956      Master       Together  69245.0        0   \n",
       "2215   9405        1954         PhD        Married  52869.0        1   \n",
       "\n",
       "      Teenhome Dt_Customer  Recency  MntWines  ...  AcceptedCmp2  Complain  \\\n",
       "0            0  04-09-2012       58       635  ...             0         0   \n",
       "1            1  08-03-2014       38        11  ...             0         0   \n",
       "2            0  21-08-2013       26       426  ...             0         0   \n",
       "3            0  10-02-2014       26        11  ...             0         0   \n",
       "4            0  19-01-2014       94       173  ...             0         0   \n",
       "...        ...         ...      ...       ...  ...           ...       ...   \n",
       "2211         1  13-06-2013       46       709  ...             0         0   \n",
       "2212         1  10-06-2014       56       406  ...             0         0   \n",
       "2213         0  25-01-2014       91       908  ...             0         0   \n",
       "2214         1  24-01-2014        8       428  ...             0         0   \n",
       "2215         1  15-10-2012       40        84  ...             0         0   \n",
       "\n",
       "      Z_CostContact  Z_Revenue  Response  MntGroceryProducts  TotalPurchase  \\\n",
       "0                 3         11         1                 806             25   \n",
       "1                 3         11         0                   9              6   \n",
       "2                 3         11         0                 287             21   \n",
       "3                 3         11         0                  34              8   \n",
       "4                 3         11         0                 207             19   \n",
       "...             ...        ...       ...                 ...            ...   \n",
       "2211              3         11         0                 267             18   \n",
       "2212              3         11         0                  30             22   \n",
       "2213              3         11         0                 297             19   \n",
       "2214              3         11         0                 324             23   \n",
       "2215              3         11         1                  66             11   \n",
       "\n",
       "      YearRange  TotalChild  HaveChild  \n",
       "0          1950           0          0  \n",
       "1          1950           2          1  \n",
       "2          1960           0          0  \n",
       "3          1980           1          1  \n",
       "4          1980           1          1  \n",
       "...         ...         ...        ...  \n",
       "2211       1960           1          1  \n",
       "2212       1940           3          1  \n",
       "2213       1980           0          0  \n",
       "2214       1950           1          1  \n",
       "2215       1950           2          1  \n",
       "\n",
       "[2216 rows x 34 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerData_Clean.loc[:,'HaveChild'] = customerData_Clean[\"TotalChild\"]\n",
    "customerData_Clean = customerData_Clean.replace({'HaveChild': {2: 1,3: 1}})\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80ede1b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2216 entries, 0 to 2215\n",
      "Data columns (total 34 columns):\n",
      " #   Column               Non-Null Count  Dtype   \n",
      "---  ------               --------------  -----   \n",
      " 0   ID                   2216 non-null   int64   \n",
      " 1   Year_Birth           2216 non-null   int64   \n",
      " 2   Education            2216 non-null   object  \n",
      " 3   Marital_Status       2216 non-null   object  \n",
      " 4   Income               2216 non-null   float64 \n",
      " 5   Kidhome              2216 non-null   int64   \n",
      " 6   Teenhome             2216 non-null   int64   \n",
      " 7   Dt_Customer          2216 non-null   object  \n",
      " 8   Recency              2216 non-null   int64   \n",
      " 9   MntWines             2216 non-null   int64   \n",
      " 10  MntFruits            2216 non-null   int64   \n",
      " 11  MntMeatProducts      2216 non-null   int64   \n",
      " 12  MntFishProducts      2216 non-null   int64   \n",
      " 13  MntSweetProducts     2216 non-null   int64   \n",
      " 14  MntGoldProds         2216 non-null   int64   \n",
      " 15  NumDealsPurchases    2216 non-null   int64   \n",
      " 16  NumWebPurchases      2216 non-null   int64   \n",
      " 17  NumCatalogPurchases  2216 non-null   int64   \n",
      " 18  NumStorePurchases    2216 non-null   int64   \n",
      " 19  NumWebVisitsMonth    2216 non-null   int64   \n",
      " 20  AcceptedCmp3         2216 non-null   int64   \n",
      " 21  AcceptedCmp4         2216 non-null   int64   \n",
      " 22  AcceptedCmp5         2216 non-null   int64   \n",
      " 23  AcceptedCmp1         2216 non-null   int64   \n",
      " 24  AcceptedCmp2         2216 non-null   int64   \n",
      " 25  Complain             2216 non-null   int64   \n",
      " 26  Z_CostContact        2216 non-null   int64   \n",
      " 27  Z_Revenue            2216 non-null   int64   \n",
      " 28  Response             2216 non-null   int64   \n",
      " 29  MntGroceryProducts   2216 non-null   int64   \n",
      " 30  TotalPurchase        2216 non-null   int64   \n",
      " 31  YearRange            2216 non-null   category\n",
      " 32  TotalChild           2216 non-null   int64   \n",
      " 33  HaveChild            2216 non-null   int64   \n",
      "dtypes: category(1), float64(1), int64(29), object(3)\n",
      "memory usage: 574.0+ KB\n"
     ]
    }
   ],
   "source": [
    "customerData_Clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc04e72",
   "metadata": {},
   "source": [
    "## <a name=\"removeCol\"></a>(3) Data Cleaning: Remove column ID, Z_CostContact, Z_Revenue, Campaign Related Variable, Complain, Dt_Customer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f5cd94",
   "metadata": {},
   "source": [
    "We drop all the variables that does not help with the prediction, such as:  \n",
    "\n",
    "#### (1) ID -  \n",
    "Customers' unique identifier. Does not reflect anything about customers.\n",
    "\n",
    "#### (2) Z_CostContact,Z_Revenue -  \n",
    "No description is given for these variables from the sources. We decide to ignore them as we know little things about these variables.\n",
    "\n",
    "#### (3) Campaign-related variable -  \n",
    "As we do not know what the store did during the offer, it is hard to tell anything from these variables. Hence, we just remove them.\n",
    "\n",
    "#### (4) Complain - \n",
    "The data is too imbalance for us to conclude anything, as shown in [here](Data-exploratory.ipynb#Complain).\n",
    "\n",
    "#### (5) Dt_Customer - \n",
    "This data shows the date of customer's enrollment with the company, which does not mean anything about customers themselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5488f287",
   "metadata": {},
   "outputs": [],
   "source": [
    "dropColumn = [\"ID\",\"Z_CostContact\",\"Z_Revenue\",\"AcceptedCmp1\",\"AcceptedCmp2\",\"AcceptedCmp3\",\"AcceptedCmp4\",\"AcceptedCmp5\",\"Response\",\"Complain\",\"Dt_Customer\"]\n",
    "customerData_Clean.drop(dropColumn,inplace= True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25b9cf43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2216 entries, 0 to 2215\n",
      "Data columns (total 23 columns):\n",
      " #   Column               Non-Null Count  Dtype   \n",
      "---  ------               --------------  -----   \n",
      " 0   Year_Birth           2216 non-null   int64   \n",
      " 1   Education            2216 non-null   object  \n",
      " 2   Marital_Status       2216 non-null   object  \n",
      " 3   Income               2216 non-null   float64 \n",
      " 4   Kidhome              2216 non-null   int64   \n",
      " 5   Teenhome             2216 non-null   int64   \n",
      " 6   Recency              2216 non-null   int64   \n",
      " 7   MntWines             2216 non-null   int64   \n",
      " 8   MntFruits            2216 non-null   int64   \n",
      " 9   MntMeatProducts      2216 non-null   int64   \n",
      " 10  MntFishProducts      2216 non-null   int64   \n",
      " 11  MntSweetProducts     2216 non-null   int64   \n",
      " 12  MntGoldProds         2216 non-null   int64   \n",
      " 13  NumDealsPurchases    2216 non-null   int64   \n",
      " 14  NumWebPurchases      2216 non-null   int64   \n",
      " 15  NumCatalogPurchases  2216 non-null   int64   \n",
      " 16  NumStorePurchases    2216 non-null   int64   \n",
      " 17  NumWebVisitsMonth    2216 non-null   int64   \n",
      " 18  MntGroceryProducts   2216 non-null   int64   \n",
      " 19  TotalPurchase        2216 non-null   int64   \n",
      " 20  YearRange            2216 non-null   category\n",
      " 21  TotalChild           2216 non-null   int64   \n",
      " 22  HaveChild            2216 non-null   int64   \n",
      "dtypes: category(1), float64(1), int64(19), object(2)\n",
      "memory usage: 383.5+ KB\n"
     ]
    }
   ],
   "source": [
    "customerData_Clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2208bfaa",
   "metadata": {},
   "source": [
    "## Converting to Categorical for Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d8c481",
   "metadata": {},
   "source": [
    "Before we continue, we convert some variables' data types to categorical for the ease of visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1119967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column which consider as Numerical Data:\n",
      " ['Year_Birth', 'Income', 'Recency', 'MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth', 'MntGroceryProducts', 'TotalPurchase', 'TotalChild']\n",
      "\n",
      "Column which consider as Categorical Data:\n",
      " ['Education', 'Marital_Status', 'YearRange', 'Kidhome', 'Teenhome', 'HaveChild']\n"
     ]
    }
   ],
   "source": [
    "colOfNum = []\n",
    "colOfCat = []\n",
    "for col in customerData_Clean:\n",
    "    if str(customerData_Clean[col].dtypes) != \"int64\" and str(customerData_Clean[col].dtypes) != \"float64\":\n",
    "        colOfCat.append(str(col))\n",
    "    else:\n",
    "        colOfNum.append(str(col))\n",
    "        \n",
    "colOfCat.extend(['Kidhome','Teenhome','HaveChild'])\n",
    "\n",
    "for element in ['Kidhome','Teenhome','HaveChild']:\n",
    "    colOfNum.remove(element)\n",
    "    \n",
    "print(\"Column which consider as Numerical Data:\\n\", colOfNum)\n",
    "print()\n",
    "print(\"Column which consider as Categorical Data:\\n\",colOfCat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55aa09fc",
   "metadata": {},
   "source": [
    "## (4) Data Cleaning: Cleaning Marital Status "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e44cd9b",
   "metadata": {},
   "source": [
    "As mentioned in [exploratory](Data-exploratory.ipynb#Marital_Status) notebook, some categories are self-explanatory, but some does not make sense, like Absurd and YOLO. At the same time, some categories have the same meaning as others, like Single and Alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a91b9f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Marital_Status : 8\n",
      "Married     857\n",
      "Together    573\n",
      "Single      471\n",
      "Divorced    232\n",
      "Widow        76\n",
      "Alone         3\n",
      "Absurd        2\n",
      "YOLO          2\n",
      "Name: Marital_Status, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 345.6x460.8 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogramCountOld(customerData_Clean, \"Marital_Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1b3088c",
   "metadata": {},
   "source": [
    "Hence, we decide to do two things on this variable. \n",
    "\n",
    "1. Remove Strange Response - Absurd and YOLO \n",
    "2. Combine Alone + Single, Married + Together, Widow + Divorced"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d495f71",
   "metadata": {},
   "source": [
    "#### (a) Remove Strange Response - Absurd and YOLO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ca25e216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2070, 2111, 2154, 2179]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>88</td>\n",
       "      <td>546</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>806</td>\n",
       "      <td>25</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>49</td>\n",
       "      <td>127</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>287</td>\n",
       "      <td>21</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>118</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>207</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>43</td>\n",
       "      <td>182</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>267</td>\n",
       "      <td>18</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2208</th>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Together</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1940</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2209</th>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>48</td>\n",
       "      <td>217</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>297</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2210</th>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Together</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>30</td>\n",
       "      <td>214</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2212 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0           1957  Graduation         Single  58138.0        0         0   \n",
       "1           1954  Graduation         Single  46344.0        1         1   \n",
       "2           1965  Graduation       Together  71613.0        0         0   \n",
       "3           1984  Graduation       Together  26646.0        1         0   \n",
       "4           1981         PhD        Married  58293.0        1         0   \n",
       "...          ...         ...            ...      ...      ...       ...   \n",
       "2207        1967  Graduation        Married  61223.0        0         1   \n",
       "2208        1946         PhD       Together  64014.0        2         1   \n",
       "2209        1981  Graduation       Divorced  56981.0        0         0   \n",
       "2210        1956      Master       Together  69245.0        0         1   \n",
       "2211        1954         PhD        Married  52869.0        1         1   \n",
       "\n",
       "      Recency  MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "0          58       635         88              546  ...                  3   \n",
       "1          38        11          1                6  ...                  2   \n",
       "2          26       426         49              127  ...                  1   \n",
       "3          26        11          4               20  ...                  2   \n",
       "4          94       173         43              118  ...                  5   \n",
       "...       ...       ...        ...              ...  ...                ...   \n",
       "2207       46       709         43              182  ...                  2   \n",
       "2208       56       406          0               30  ...                  7   \n",
       "2209       91       908         48              217  ...                  1   \n",
       "2210        8       428         30              214  ...                  2   \n",
       "2211       40        84          3               61  ...                  3   \n",
       "\n",
       "      NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "0                   8                   10                  4   \n",
       "1                   1                    1                  2   \n",
       "2                   8                    2                 10   \n",
       "3                   2                    0                  4   \n",
       "4                   5                    3                  6   \n",
       "...               ...                  ...                ...   \n",
       "2207                9                    3                  4   \n",
       "2208                8                    2                  5   \n",
       "2209                2                    3                 13   \n",
       "2210                6                    5                 10   \n",
       "2211                3                    1                  4   \n",
       "\n",
       "      NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "0                     7                 806             25       1950   \n",
       "1                     5                   9              6       1950   \n",
       "2                     4                 287             21       1960   \n",
       "3                     6                  34              8       1980   \n",
       "4                     5                 207             19       1980   \n",
       "...                 ...                 ...            ...        ...   \n",
       "2207                  5                 267             18       1960   \n",
       "2208                  7                  30             22       1940   \n",
       "2209                  6                 297             19       1980   \n",
       "2210                  3                 324             23       1950   \n",
       "2211                  7                  66             11       1950   \n",
       "\n",
       "      TotalChild  HaveChild  \n",
       "0              0          0  \n",
       "1              2          1  \n",
       "2              0          0  \n",
       "3              1          1  \n",
       "4              1          1  \n",
       "...          ...        ...  \n",
       "2207           1          1  \n",
       "2208           3          1  \n",
       "2209           0          0  \n",
       "2210           1          1  \n",
       "2211           2          1  \n",
       "\n",
       "[2212 rows x 23 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deleteData = customerData_Clean.loc[(customerData_Clean.Marital_Status == \"Absurd\") | (customerData_Clean.Marital_Status == \"YOLO\")]\n",
    "print(deleteData.index.tolist())\n",
    "customerData_Clean.drop(index = deleteData.index.tolist(),inplace=True)\n",
    "customerData_Clean.reset_index(drop=True, inplace=True)\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bcc845",
   "metadata": {},
   "source": [
    "#### (b) Combine Alone + Single, Married + Together, Widow + Divorced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c5b330b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "customerData_Clean = customerData_Clean.replace({'Marital_Status': {'Alone': 'Single','Together': 'Married','Widow': 'Divorced'}})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b5550f",
   "metadata": {},
   "source": [
    "#### After cleaning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0109c15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Marital_Status : 3\n",
      "Married     1430\n",
      "Single       474\n",
      "Divorced     308\n",
      "Name: Marital_Status, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 345.6x460.8 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogramCountOld(customerData_Clean, \"Marital_Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb6648b",
   "metadata": {},
   "source": [
    "## (5) Data Cleaning: Remove Outliers of Year Range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a582ad1",
   "metadata": {},
   "source": [
    "As shown in the graph below, there are some outliers in this variable.   \n",
    "To help with the training later, we decide to remove 1890 and 1900 as there are not a lot of data in these categories too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "728c3d05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of YearRange : 8\n",
      "1970    731\n",
      "1960    502\n",
      "1950    452\n",
      "1980    358\n",
      "1940    106\n",
      "1990     60\n",
      "1890      2\n",
      "1900      1\n",
      "Name: YearRange, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 345.6x460.8 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogramCountOld(customerData_Clean, \"YearRange\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a825d9d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[181, 228, 326]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>88</td>\n",
       "      <td>546</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>806</td>\n",
       "      <td>25</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>49</td>\n",
       "      <td>127</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>287</td>\n",
       "      <td>21</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>118</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>207</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>43</td>\n",
       "      <td>182</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>267</td>\n",
       "      <td>18</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2205</th>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1940</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2206</th>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>48</td>\n",
       "      <td>217</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>297</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Married</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>30</td>\n",
       "      <td>214</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2208</th>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2209 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0           1957  Graduation         Single  58138.0        0         0   \n",
       "1           1954  Graduation         Single  46344.0        1         1   \n",
       "2           1965  Graduation        Married  71613.0        0         0   \n",
       "3           1984  Graduation        Married  26646.0        1         0   \n",
       "4           1981         PhD        Married  58293.0        1         0   \n",
       "...          ...         ...            ...      ...      ...       ...   \n",
       "2204        1967  Graduation        Married  61223.0        0         1   \n",
       "2205        1946         PhD        Married  64014.0        2         1   \n",
       "2206        1981  Graduation       Divorced  56981.0        0         0   \n",
       "2207        1956      Master        Married  69245.0        0         1   \n",
       "2208        1954         PhD        Married  52869.0        1         1   \n",
       "\n",
       "      Recency  MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "0          58       635         88              546  ...                  3   \n",
       "1          38        11          1                6  ...                  2   \n",
       "2          26       426         49              127  ...                  1   \n",
       "3          26        11          4               20  ...                  2   \n",
       "4          94       173         43              118  ...                  5   \n",
       "...       ...       ...        ...              ...  ...                ...   \n",
       "2204       46       709         43              182  ...                  2   \n",
       "2205       56       406          0               30  ...                  7   \n",
       "2206       91       908         48              217  ...                  1   \n",
       "2207        8       428         30              214  ...                  2   \n",
       "2208       40        84          3               61  ...                  3   \n",
       "\n",
       "      NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "0                   8                   10                  4   \n",
       "1                   1                    1                  2   \n",
       "2                   8                    2                 10   \n",
       "3                   2                    0                  4   \n",
       "4                   5                    3                  6   \n",
       "...               ...                  ...                ...   \n",
       "2204                9                    3                  4   \n",
       "2205                8                    2                  5   \n",
       "2206                2                    3                 13   \n",
       "2207                6                    5                 10   \n",
       "2208                3                    1                  4   \n",
       "\n",
       "      NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "0                     7                 806             25       1950   \n",
       "1                     5                   9              6       1950   \n",
       "2                     4                 287             21       1960   \n",
       "3                     6                  34              8       1980   \n",
       "4                     5                 207             19       1980   \n",
       "...                 ...                 ...            ...        ...   \n",
       "2204                  5                 267             18       1960   \n",
       "2205                  7                  30             22       1940   \n",
       "2206                  6                 297             19       1980   \n",
       "2207                  3                 324             23       1950   \n",
       "2208                  7                  66             11       1950   \n",
       "\n",
       "      TotalChild  HaveChild  \n",
       "0              0          0  \n",
       "1              2          1  \n",
       "2              0          0  \n",
       "3              1          1  \n",
       "4              1          1  \n",
       "...          ...        ...  \n",
       "2204           1          1  \n",
       "2205           3          1  \n",
       "2206           0          0  \n",
       "2207           1          1  \n",
       "2208           2          1  \n",
       "\n",
       "[2209 rows x 23 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deleteData = customerData_Clean.loc[(customerData_Clean.YearRange == 1890) | (customerData_Clean.YearRange == 1900)]\n",
    "print(deleteData.index.tolist())\n",
    "customerData_Clean.drop(index = deleteData.index.tolist(),inplace=True)\n",
    "customerData_Clean.reset_index(drop=True, inplace=True)\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b16efe",
   "metadata": {},
   "source": [
    "#### After cleaning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0cf8f830",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of YearRange : 6\n",
      "1970    731\n",
      "1960    502\n",
      "1950    452\n",
      "1980    358\n",
      "1940    106\n",
      "1990     60\n",
      "1890      0\n",
      "1900      0\n",
      "Name: YearRange, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 345.6x460.8 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogramCountOld(customerData_Clean, \"YearRange\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "903aea1d",
   "metadata": {},
   "source": [
    "## (6) Data Cleaning: Remove Outliers in Income Column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b090b2fe",
   "metadata": {},
   "source": [
    "As shown in the following graph, there are some outliers in the income column.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "177cd30b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x212219afa30>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.displot(customerData_Clean[\"Income\"], kde = True, aspect = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7a165b76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x212223f3c10>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 144x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(2, 6))\n",
    "sb.jointplot(data = customerData_Clean, x = \"Income\", y = \"MntGroceryProducts\", height = 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c262d3f8",
   "metadata": {},
   "source": [
    "We decide to remove top 1% of the customers, as they do not represent most of the customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "84388a5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94464.96"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerData_Clean[\"Income\"].quantile(q=0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d69cfb51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1983</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>101970.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>722</td>\n",
       "      <td>27</td>\n",
       "      <td>102</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>173</td>\n",
       "      <td>27</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>1973</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>157243.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1582</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1585</td>\n",
       "      <td>37</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>1977</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>102160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>763</td>\n",
       "      <td>29</td>\n",
       "      <td>138</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>243</td>\n",
       "      <td>26</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>1974</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>102692.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>168</td>\n",
       "      <td>148</td>\n",
       "      <td>444</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>29</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>1976</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>162397.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1970</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>105471.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>1009</td>\n",
       "      <td>181</td>\n",
       "      <td>104</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>487</td>\n",
       "      <td>30</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>1960</td>\n",
       "      <td>Master</td>\n",
       "      <td>Single</td>\n",
       "      <td>98777.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1000</td>\n",
       "      <td>19</td>\n",
       "      <td>711</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>855</td>\n",
       "      <td>19</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>1975</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>153924.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>1982</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>160803.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>1622</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1655</td>\n",
       "      <td>44</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>1994</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>95529.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>770</td>\n",
       "      <td>29</td>\n",
       "      <td>890</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1169</td>\n",
       "      <td>18</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>1987</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Single</td>\n",
       "      <td>95169.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1285</td>\n",
       "      <td>21</td>\n",
       "      <td>449</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>576</td>\n",
       "      <td>12</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>1960</td>\n",
       "      <td>Master</td>\n",
       "      <td>Single</td>\n",
       "      <td>98777.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1000</td>\n",
       "      <td>19</td>\n",
       "      <td>711</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>855</td>\n",
       "      <td>19</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>1971</td>\n",
       "      <td>Master</td>\n",
       "      <td>Married</td>\n",
       "      <td>157733.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>1969</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>94871.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>99</td>\n",
       "      <td>169</td>\n",
       "      <td>24</td>\n",
       "      <td>553</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>765</td>\n",
       "      <td>18</td>\n",
       "      <td>1960</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>1977</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>157146.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1725</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1727</td>\n",
       "      <td>28</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>1945</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Single</td>\n",
       "      <td>113734.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>1940</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>1990</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>96843.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>448</td>\n",
       "      <td>71</td>\n",
       "      <td>951</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1062</td>\n",
       "      <td>28</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>1951</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>94642.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>879</td>\n",
       "      <td>143</td>\n",
       "      <td>797</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1046</td>\n",
       "      <td>21</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>1958</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>94472.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>1017</td>\n",
       "      <td>33</td>\n",
       "      <td>417</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>558</td>\n",
       "      <td>16</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>1980</td>\n",
       "      <td>2n Cycle</td>\n",
       "      <td>Married</td>\n",
       "      <td>96547.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>448</td>\n",
       "      <td>21</td>\n",
       "      <td>125</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>198</td>\n",
       "      <td>21</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1977</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>96876.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>908</td>\n",
       "      <td>43</td>\n",
       "      <td>735</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>818</td>\n",
       "      <td>24</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105</th>\n",
       "      <td>1949</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>156924.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1940</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2202</th>\n",
       "      <td>1977</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>666666.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>11</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_Birth   Education Marital_Status    Income  Kidhome  Teenhome  \\\n",
       "115         1983  Graduation         Single  101970.0        0         0   \n",
       "153         1973         PhD        Married  157243.0        0         1   \n",
       "191         1977         PhD        Married  102160.0        0         0   \n",
       "239         1974  Graduation       Divorced  102692.0        0         0   \n",
       "601         1976         PhD        Married  162397.0        1         1   \n",
       "630         1970  Graduation        Married  105471.0        0         0   \n",
       "634         1960      Master         Single   98777.0        0         0   \n",
       "639         1975  Graduation       Divorced  153924.0        0         0   \n",
       "671         1982         PhD        Married  160803.0        0         0   \n",
       "898         1994  Graduation         Single   95529.0        0         0   \n",
       "985         1987         PhD         Single   95169.0        0         0   \n",
       "1097        1960      Master         Single   98777.0        0         0   \n",
       "1284        1971      Master        Married  157733.0        1         0   \n",
       "1453        1969         PhD        Married   94871.0        0         2   \n",
       "1633        1977  Graduation        Married  157146.0        0         0   \n",
       "1878        1945         PhD         Single  113734.0        0         0   \n",
       "1938        1990  Graduation         Single   96843.0        0         0   \n",
       "1962        1951  Graduation        Married   94642.0        0         0   \n",
       "1973        1958  Graduation         Single   94472.0        0         1   \n",
       "1975        1980    2n Cycle        Married   96547.0        0         0   \n",
       "1992        1977  Graduation        Married   96876.0        0         0   \n",
       "2105        1949         PhD        Married  156924.0        0         0   \n",
       "2202        1977  Graduation        Married  666666.0        1         0   \n",
       "\n",
       "      Recency  MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "115        69       722         27              102  ...                  0   \n",
       "153        98        20          2             1582  ...                 15   \n",
       "191        54       763         29              138  ...                  0   \n",
       "239         5       168        148              444  ...                  1   \n",
       "601        31        85          1               16  ...                  0   \n",
       "630        36      1009        181              104  ...                  0   \n",
       "634        23      1000         19              711  ...                  0   \n",
       "639        81         1          1                1  ...                  0   \n",
       "671        21        55         16             1622  ...                 15   \n",
       "898        29       770         29              890  ...                  1   \n",
       "985         1      1285         21              449  ...                  1   \n",
       "1097       23      1000         19              711  ...                  0   \n",
       "1284       37        39          1                9  ...                  0   \n",
       "1453       99       169         24              553  ...                  1   \n",
       "1633       13         1          0             1725  ...                  0   \n",
       "1878        9         6          2                3  ...                  0   \n",
       "1938       60       448         71              951  ...                  1   \n",
       "1962       35       879        143              797  ...                  0   \n",
       "1973       75      1017         33              417  ...                  1   \n",
       "1975        4       448         21              125  ...                  0   \n",
       "1992       79       908         43              735  ...                  1   \n",
       "2105       85         2          1                2  ...                  0   \n",
       "2202       23         9         14               18  ...                  4   \n",
       "\n",
       "      NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "115                 6                    8                 13   \n",
       "153                 0                   22                  0   \n",
       "191                 7                    9                 10   \n",
       "239                 6                    9                 13   \n",
       "601                 0                    0                  1   \n",
       "630                 9                    8                 13   \n",
       "634                 4                    6                  9   \n",
       "639                 0                    0                  0   \n",
       "671                 0                   28                  1   \n",
       "898                 7                    3                  7   \n",
       "985                 4                    3                  4   \n",
       "1097                4                    6                  9   \n",
       "1284                1                    0                  1   \n",
       "1453                8                    5                  4   \n",
       "1633                0                   28                  0   \n",
       "1878               27                    0                  0   \n",
       "1938                6                   11                 10   \n",
       "1962                5                    7                  9   \n",
       "1973                5                    5                  5   \n",
       "1975                7                    6                  8   \n",
       "1992                7                    7                  9   \n",
       "2105                0                    0                  0   \n",
       "2202                3                    1                  3   \n",
       "\n",
       "      NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "115                   2                 173             27       1980   \n",
       "153                   0                1585             37       1970   \n",
       "191                   4                 243             26       1970   \n",
       "239                   2                 624             29       1970   \n",
       "601                   1                  19              1       1970   \n",
       "630                   3                 487             30       1970   \n",
       "634                   1                 855             19       1960   \n",
       "639                   0                   3              0       1970   \n",
       "671                   0                1655             44       1980   \n",
       "898                   3                1169             18       1990   \n",
       "985                   1                 576             12       1980   \n",
       "1097                  1                 855             19       1960   \n",
       "1284                  1                  12              2       1970   \n",
       "1453                  7                 765             18       1960   \n",
       "1633                  1                1727             28       1970   \n",
       "1878                  1                   6             27       1940   \n",
       "1938                  2                1062             28       1990   \n",
       "1962                  1                1046             21       1950   \n",
       "1973                  5                 558             16       1950   \n",
       "1975                  2                 198             21       1980   \n",
       "1992                  2                 818             24       1970   \n",
       "2105                  0                   4              0       1940   \n",
       "2202                  6                  40             11       1970   \n",
       "\n",
       "      TotalChild  HaveChild  \n",
       "115            0          0  \n",
       "153            1          1  \n",
       "191            0          0  \n",
       "239            0          0  \n",
       "601            2          1  \n",
       "630            0          0  \n",
       "634            0          0  \n",
       "639            0          0  \n",
       "671            0          0  \n",
       "898            0          0  \n",
       "985            0          0  \n",
       "1097           0          0  \n",
       "1284           1          1  \n",
       "1453           2          1  \n",
       "1633           0          0  \n",
       "1878           0          0  \n",
       "1938           0          0  \n",
       "1962           0          0  \n",
       "1973           1          1  \n",
       "1975           0          0  \n",
       "1992           0          0  \n",
       "2105           0          0  \n",
       "2202           1          1  \n",
       "\n",
       "[23 rows x 23 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_Income = customerData_Clean[customerData_Clean[\"Income\"] >= customerData_Clean[\"Income\"].quantile(q=0.99)]\n",
    "outliers_Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6766daa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[115, 153, 191, 239, 601, 630, 634, 639, 671, 898, 985, 1097, 1284, 1453, 1633, 1878, 1938, 1962, 1973, 1975, 1992, 2105, 2202]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>88</td>\n",
       "      <td>546</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>806</td>\n",
       "      <td>25</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>49</td>\n",
       "      <td>127</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>287</td>\n",
       "      <td>21</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>118</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>207</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>43</td>\n",
       "      <td>182</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>267</td>\n",
       "      <td>18</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2182</th>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1940</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2183</th>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>48</td>\n",
       "      <td>217</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>297</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2184</th>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Married</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>30</td>\n",
       "      <td>214</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2185</th>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2186 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0           1957  Graduation         Single  58138.0        0         0   \n",
       "1           1954  Graduation         Single  46344.0        1         1   \n",
       "2           1965  Graduation        Married  71613.0        0         0   \n",
       "3           1984  Graduation        Married  26646.0        1         0   \n",
       "4           1981         PhD        Married  58293.0        1         0   \n",
       "...          ...         ...            ...      ...      ...       ...   \n",
       "2181        1967  Graduation        Married  61223.0        0         1   \n",
       "2182        1946         PhD        Married  64014.0        2         1   \n",
       "2183        1981  Graduation       Divorced  56981.0        0         0   \n",
       "2184        1956      Master        Married  69245.0        0         1   \n",
       "2185        1954         PhD        Married  52869.0        1         1   \n",
       "\n",
       "      Recency  MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "0          58       635         88              546  ...                  3   \n",
       "1          38        11          1                6  ...                  2   \n",
       "2          26       426         49              127  ...                  1   \n",
       "3          26        11          4               20  ...                  2   \n",
       "4          94       173         43              118  ...                  5   \n",
       "...       ...       ...        ...              ...  ...                ...   \n",
       "2181       46       709         43              182  ...                  2   \n",
       "2182       56       406          0               30  ...                  7   \n",
       "2183       91       908         48              217  ...                  1   \n",
       "2184        8       428         30              214  ...                  2   \n",
       "2185       40        84          3               61  ...                  3   \n",
       "\n",
       "      NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "0                   8                   10                  4   \n",
       "1                   1                    1                  2   \n",
       "2                   8                    2                 10   \n",
       "3                   2                    0                  4   \n",
       "4                   5                    3                  6   \n",
       "...               ...                  ...                ...   \n",
       "2181                9                    3                  4   \n",
       "2182                8                    2                  5   \n",
       "2183                2                    3                 13   \n",
       "2184                6                    5                 10   \n",
       "2185                3                    1                  4   \n",
       "\n",
       "      NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "0                     7                 806             25       1950   \n",
       "1                     5                   9              6       1950   \n",
       "2                     4                 287             21       1960   \n",
       "3                     6                  34              8       1980   \n",
       "4                     5                 207             19       1980   \n",
       "...                 ...                 ...            ...        ...   \n",
       "2181                  5                 267             18       1960   \n",
       "2182                  7                  30             22       1940   \n",
       "2183                  6                 297             19       1980   \n",
       "2184                  3                 324             23       1950   \n",
       "2185                  7                  66             11       1950   \n",
       "\n",
       "      TotalChild  HaveChild  \n",
       "0              0          0  \n",
       "1              2          1  \n",
       "2              0          0  \n",
       "3              1          1  \n",
       "4              1          1  \n",
       "...          ...        ...  \n",
       "2181           1          1  \n",
       "2182           3          1  \n",
       "2183           0          0  \n",
       "2184           1          1  \n",
       "2185           2          1  \n",
       "\n",
       "[2186 rows x 23 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(outliers_Income.index.tolist())\n",
    "customerData_Clean.drop(index = outliers_Income.index.tolist(),inplace=True)\n",
    "customerData_Clean.reset_index(drop=True, inplace=True)\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e162aec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x212221966d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.displot(customerData_Clean[\"Income\"], kde = True, aspect = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f757d9f",
   "metadata": {},
   "source": [
    "## (7) Data Cleaning: Remove Outliers in MntGroceryProducts Column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70837535",
   "metadata": {},
   "source": [
    "As shown in graph, there are one outlier in MntGroceryProducts. This outlier make no sense, as a person which has low income should not be able to purchase more grocery products than those who are rich.  \n",
    "Hence, we just remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49e9ea03",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x212223ede20>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 144x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(2, 6))\n",
    "sb.jointplot(data = customerData_Clean, x = \"Income\", y = \"MntGroceryProducts\", height = 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6d947045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1979</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>2447.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1725</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1727</td>\n",
       "      <td>43</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year_Birth   Education Marital_Status  Income  Kidhome  Teenhome  Recency  \\\n",
       "20        1979  Graduation        Married  2447.0        1         0       42   \n",
       "\n",
       "    MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "20         1          1             1725  ...                 15   \n",
       "\n",
       "    NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "20                0                   28                  0   \n",
       "\n",
       "    NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "20                  1                1727             43       1970   \n",
       "\n",
       "    TotalChild  HaveChild  \n",
       "20           1          1  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_Grocery = pd.DataFrame(customerData_Clean[customerData_Clean[\"MntGroceryProducts\"] >= 1500])\n",
    "outliers_Grocery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e5c3f847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>88</td>\n",
       "      <td>546</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>806</td>\n",
       "      <td>25</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>49</td>\n",
       "      <td>127</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>287</td>\n",
       "      <td>21</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>118</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>207</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>43</td>\n",
       "      <td>182</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>267</td>\n",
       "      <td>18</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1940</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2182</th>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>48</td>\n",
       "      <td>217</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>297</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2183</th>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Married</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>30</td>\n",
       "      <td>214</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2184</th>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2185 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0           1957  Graduation         Single  58138.0        0         0   \n",
       "1           1954  Graduation         Single  46344.0        1         1   \n",
       "2           1965  Graduation        Married  71613.0        0         0   \n",
       "3           1984  Graduation        Married  26646.0        1         0   \n",
       "4           1981         PhD        Married  58293.0        1         0   \n",
       "...          ...         ...            ...      ...      ...       ...   \n",
       "2180        1967  Graduation        Married  61223.0        0         1   \n",
       "2181        1946         PhD        Married  64014.0        2         1   \n",
       "2182        1981  Graduation       Divorced  56981.0        0         0   \n",
       "2183        1956      Master        Married  69245.0        0         1   \n",
       "2184        1954         PhD        Married  52869.0        1         1   \n",
       "\n",
       "      Recency  MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "0          58       635         88              546  ...                  3   \n",
       "1          38        11          1                6  ...                  2   \n",
       "2          26       426         49              127  ...                  1   \n",
       "3          26        11          4               20  ...                  2   \n",
       "4          94       173         43              118  ...                  5   \n",
       "...       ...       ...        ...              ...  ...                ...   \n",
       "2180       46       709         43              182  ...                  2   \n",
       "2181       56       406          0               30  ...                  7   \n",
       "2182       91       908         48              217  ...                  1   \n",
       "2183        8       428         30              214  ...                  2   \n",
       "2184       40        84          3               61  ...                  3   \n",
       "\n",
       "      NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "0                   8                   10                  4   \n",
       "1                   1                    1                  2   \n",
       "2                   8                    2                 10   \n",
       "3                   2                    0                  4   \n",
       "4                   5                    3                  6   \n",
       "...               ...                  ...                ...   \n",
       "2180                9                    3                  4   \n",
       "2181                8                    2                  5   \n",
       "2182                2                    3                 13   \n",
       "2183                6                    5                 10   \n",
       "2184                3                    1                  4   \n",
       "\n",
       "      NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "0                     7                 806             25       1950   \n",
       "1                     5                   9              6       1950   \n",
       "2                     4                 287             21       1960   \n",
       "3                     6                  34              8       1980   \n",
       "4                     5                 207             19       1980   \n",
       "...                 ...                 ...            ...        ...   \n",
       "2180                  5                 267             18       1960   \n",
       "2181                  7                  30             22       1940   \n",
       "2182                  6                 297             19       1980   \n",
       "2183                  3                 324             23       1950   \n",
       "2184                  7                  66             11       1950   \n",
       "\n",
       "      TotalChild  HaveChild  \n",
       "0              0          0  \n",
       "1              2          1  \n",
       "2              0          0  \n",
       "3              1          1  \n",
       "4              1          1  \n",
       "...          ...        ...  \n",
       "2180           1          1  \n",
       "2181           3          1  \n",
       "2182           0          0  \n",
       "2183           1          1  \n",
       "2184           2          1  \n",
       "\n",
       "[2185 rows x 23 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(outliers_Grocery.index.tolist())\n",
    "customerData_Clean.drop(index = outliers_Grocery.index.tolist(),inplace=True)\n",
    "customerData_Clean.reset_index(drop=True, inplace=True)\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "25306f81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x21223a5c760>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.jointplot(data = customerData_Clean, x = \"Income\", y = \"MntGroceryProducts\", height = 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d900c94",
   "metadata": {},
   "source": [
    "## (8) Data Cleaning: Remove Outliers in MntGoldProducts Column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be858e3",
   "metadata": {},
   "source": [
    "As shown in graph, there are some outliers in MntGroldProducts. These outliers make no sense, as a person which has low income should not be able to purchase so many gold products than those who are rich.  \n",
    "Hence, we just remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "13e0674b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2122220b2b0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 144x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(2, 6))\n",
    "sb.jointplot(data = customerData_Clean, x = \"Income\", y = \"MntGoldProds\", height = 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d5052180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>1982</td>\n",
       "      <td>Master</td>\n",
       "      <td>Single</td>\n",
       "      <td>6560.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td>1966</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Single</td>\n",
       "      <td>7144.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>25</td>\n",
       "      <td>1960</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>1969</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>4428.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_Birth   Education Marital_Status  Income  Kidhome  Teenhome  \\\n",
       "1298        1982      Master         Single  6560.0        0         0   \n",
       "1770        1966         PhD         Single  7144.0        0         2   \n",
       "1937        1969  Graduation        Married  4428.0        0         1   \n",
       "\n",
       "      Recency  MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "1298        2        67         11               26  ...                  0   \n",
       "1770       92        81          4               33  ...                  0   \n",
       "1937        0        16          4               12  ...                  0   \n",
       "\n",
       "      NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "1298                1                    0                  1   \n",
       "1770               23                    1                  1   \n",
       "1937               25                    0                  0   \n",
       "\n",
       "      NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "1298                 17                  41              2       1980   \n",
       "1770                  0                  42             25       1960   \n",
       "1937                  1                  18             25       1960   \n",
       "\n",
       "      TotalChild  HaveChild  \n",
       "1298           0          0  \n",
       "1770           2          1  \n",
       "1937           1          1  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_Gold = pd.DataFrame()\n",
    "outliers_Gold = customerData_Clean.loc[(customerData_Clean.MntGoldProds >= 250) & (customerData_Clean.Income <= 20000)]\n",
    "outliers_Gold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bc8225eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1298, 1770, 1937]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>88</td>\n",
       "      <td>546</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>806</td>\n",
       "      <td>25</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>49</td>\n",
       "      <td>127</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>287</td>\n",
       "      <td>21</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>118</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>207</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2177</th>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>43</td>\n",
       "      <td>182</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>267</td>\n",
       "      <td>18</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2178</th>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1940</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2179</th>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>48</td>\n",
       "      <td>217</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>297</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Married</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>30</td>\n",
       "      <td>214</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2182 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0           1957  Graduation         Single  58138.0        0         0   \n",
       "1           1954  Graduation         Single  46344.0        1         1   \n",
       "2           1965  Graduation        Married  71613.0        0         0   \n",
       "3           1984  Graduation        Married  26646.0        1         0   \n",
       "4           1981         PhD        Married  58293.0        1         0   \n",
       "...          ...         ...            ...      ...      ...       ...   \n",
       "2177        1967  Graduation        Married  61223.0        0         1   \n",
       "2178        1946         PhD        Married  64014.0        2         1   \n",
       "2179        1981  Graduation       Divorced  56981.0        0         0   \n",
       "2180        1956      Master        Married  69245.0        0         1   \n",
       "2181        1954         PhD        Married  52869.0        1         1   \n",
       "\n",
       "      Recency  MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "0          58       635         88              546  ...                  3   \n",
       "1          38        11          1                6  ...                  2   \n",
       "2          26       426         49              127  ...                  1   \n",
       "3          26        11          4               20  ...                  2   \n",
       "4          94       173         43              118  ...                  5   \n",
       "...       ...       ...        ...              ...  ...                ...   \n",
       "2177       46       709         43              182  ...                  2   \n",
       "2178       56       406          0               30  ...                  7   \n",
       "2179       91       908         48              217  ...                  1   \n",
       "2180        8       428         30              214  ...                  2   \n",
       "2181       40        84          3               61  ...                  3   \n",
       "\n",
       "      NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "0                   8                   10                  4   \n",
       "1                   1                    1                  2   \n",
       "2                   8                    2                 10   \n",
       "3                   2                    0                  4   \n",
       "4                   5                    3                  6   \n",
       "...               ...                  ...                ...   \n",
       "2177                9                    3                  4   \n",
       "2178                8                    2                  5   \n",
       "2179                2                    3                 13   \n",
       "2180                6                    5                 10   \n",
       "2181                3                    1                  4   \n",
       "\n",
       "      NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "0                     7                 806             25       1950   \n",
       "1                     5                   9              6       1950   \n",
       "2                     4                 287             21       1960   \n",
       "3                     6                  34              8       1980   \n",
       "4                     5                 207             19       1980   \n",
       "...                 ...                 ...            ...        ...   \n",
       "2177                  5                 267             18       1960   \n",
       "2178                  7                  30             22       1940   \n",
       "2179                  6                 297             19       1980   \n",
       "2180                  3                 324             23       1950   \n",
       "2181                  7                  66             11       1950   \n",
       "\n",
       "      TotalChild  HaveChild  \n",
       "0              0          0  \n",
       "1              2          1  \n",
       "2              0          0  \n",
       "3              1          1  \n",
       "4              1          1  \n",
       "...          ...        ...  \n",
       "2177           1          1  \n",
       "2178           3          1  \n",
       "2179           0          0  \n",
       "2180           1          1  \n",
       "2181           2          1  \n",
       "\n",
       "[2182 rows x 23 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(outliers_Gold.index.tolist())\n",
    "customerData_Clean.drop(index = outliers_Gold.index.tolist(),inplace=True)\n",
    "customerData_Clean.reset_index(drop=True, inplace=True)\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "97f56a73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x212243186a0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 144x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yE74zUF2TN7qMZ5got1rKrAsaNlKSiYWqrAI0xFQuystNSX0+L/ZxioimW/LcEASRGqhnTdLENUzUD1iMGtQON8NilG7Y7G/uxL3PfIKH//w57t3wCfY3d8LWR0NMidyGjBuCIIgsJdUTwsWqrNq7hROUMz6ZnCAkQsYNQRBElpLqxGyxKiutpoASwomchowbgiCILCXVidliVVYGnZoSwomchhKKiaQgXcoH6VI+8kqXqUzMjtbZODBw3qEWIzRK6lGUCJRQnDqi6ZbGLxAEQWQzqRx74AfqakqxdvkMwSor5rzl5Ua6yRI5BRk3BMGFeUp2eFBs0sJcqKKnVSK/CVVZWYwa9t9EdkDjFxKHjBuCYIinpwiRO5DBmj3QtYgLGr+QOGTcEEQIsZ4ij6ycORASIHILMlizB7oWRBqhaimCCJHqniJE+omrCR6RUuhaEOmEjBuCCEHDHvMPMlgTJGyeldRK1GjQtSDSCYWlCCIE01Mk3G2ezplIhLwwBiv3pkoGawwEwke3XTUFtSNMSX0P6FoQ6YQ8NwTBEAAmVBXhkZUzsWbZNDyyciblA+Q4CTfBkzCJO18RCh89/te9SYePaFI8kU7Ic0MQXFLZU4RIPxyDVXITvEGe+BotfJRUYn0i14IgEoQ8NwRB5DdxTicf7ImvKc09o0nxRJog44YgCILDYE98FQof3XbVFAofETkFhaUIgiA4DPrEV4Hw0eiRJbBaHZmWjCAkQ54bgiAIDpT4iojwkVI5iDKqswgav5A45LkhCILgQomvRJZA4xcSh4wbgiCIcKhqjiByGgpLEQRBZBu53Gcnl2Un8gby3BAEQWQTudxnJ5dlJ/IK8twQBEFkEbncZyeXZSfyCzJuCIIgsohc7rOTy7IT+QUZNwRBEFlELk+nz2XZifyCjBuCIIgsIpf77OSy7ER+QQnFBEEQ2UQu99nJZdmJvIKMG4IgBgeKYMJrt8ODYpMW5kJV9t50w/vsIFhenVLZ5dIP9QgisgAybgiCyH9yuUQ5HbLnsn7yGK/PD3NRIWw9fZkWJeegnBuCIPKenClRFmiAlw7Zc0Y/g4xH//wvaDXkg0gE0hpBEHlPtBJlbugno4h4T4oM6pTLnhP6IYg4IOOGIIi8hylR5t7As61EWcx7snb5DAyz6HFB/Uh2lMFH+07KKnvS+smlfCZiUEDGDUEQeQ9TohzuFcmmSh4x74nT5cWiObXYsOUgK/uK+ZNgNqgBvzznTko/lK9DZCFk3BAEkf/kQImymPfEoFPjoRc/53l0Nmw5iEdWzpQvZJSEfsQ8TrLKRxBxQgnFBEEMDkIlylVlhuBNN4sMG0C8AZ7b40vPSIME9UMjF4hsJKWem/Xr1+Ptt98GAMyePRt33XUXdu3ahYceeghutxs/+tGPcNtttwEADh8+jN/85jdwOp04//zz8cADD0ClIscSQRBZQDpySkS8J7Y+X2L5MErAavPAanPBUqSDxaSRLYzFJRfymYjBR8o8N7t27cLHH3+MrVu34vXXX8eXX36JN954A6tXr8ZTTz2Ft956C4cOHcIHH3wAALjzzjvx29/+Fu+88w4CgQA2b96cKtEIgiCkE8opufupXVjz3Ge4+8l/4vCJHja5V1YEvCcJjTRQAvubO3HvM5/g4T9/jns3fIL9zZ0p+cWnkQtENpIy10h5eTl+/etfQ6MJWu81NTU4duwYqqurUVlZCQC44oorsH37dowZMwYulwv19fUAgPnz52PdunVYunRpqsQjCCLV5EkFTcZzShLIh7HaPGwCMiPzhi0HsXb5DFiMMntUciCfiRh8pMy4GTt2LPvfx44dw9tvv42rr74a5eXl7OsVFRU4e/Ys2traeK+Xl5fj7NmzcZ3PYjHG9f7yclNc7yfEIV3KRyxd+v0BtHY40WnrQ6m5EMPKDFAqU+FCSA6/P4BPDrbi8b/uZStobrtqCmZMGpY2eeXal2e+aRfMKen19qOmqlSWc0ihPPZbWL4+c1pQ5i6HG+NHWxI7fwx9xiMfIQ2VOniLpt/Y+El5UsvXX3+N5cuX46677kJBQQGOHTvG/i0QCEChUMDv90OhUES8Hg9WqwN+v7RHhfJyE9rb7XEdnxCGdCkfMXWZQyW3tj4va9gAwRvr43/di6El6fF2yLkv9VqVYE6JXl2QtXu/VCQPpsSoTUhm+p4nTjKGic/rAwDSvQjRdJvSaqk9e/bg+uuvx69+9SvMmzcPQ4cORXt7O/v39vZ2VFRURLze0dGBioqKVIpGEDlHLrXIz6cKmlzMKbGYNFgxfxJP5hXzJ8FijqMpH2cMhNQHR0JeVt8wHW6PL9Ni5CQp89y0trbi5z//OR5//HHMmDEDAFBXV4ejR4/i+PHjGDlyJN544w1ceeWVGDFiBLRaLfbs2YOGhgZs27YNs2bNSpVoBJGT5FKL/LyqoMnFnBI/UFdTirXLZwSrpcy6oGEjpVpKwEN421VTUDvClN1rzlNoaGZipMy4ee655+B2u/Hwww+zry1ZsgQPP/wwfvGLX8DtdmP27Nm45JJLAACPPfYY7r33XjgcDkycOBHXXnttqkQjiJwkYwZDAonBae8IHCajRW5PQ6iKiTUikz18OpKt/YDFqBlIIJZYBi7kIXz8r3upKR+RUygCgUBe2OKUc5MZSJfykZU5N8mcM3QD77C5odOqYNarYdQWyC9rrnkaxHRaXQSbM/PVZSfanVjz3GcRr69ZNg1VZYb0C5TjJJsMTL+v4kTTLXXJI4hcIQPhkWTLoE919KbcGMs1T4OQvC9tP4ylF4/H+lf2ZzxZPK9CisSghcYvEEQukeYRAskkBqcrATrXkpeF5L2gfiRr2ACZTRYXSqC+7aopWZ1ATRDhkOeGIAhRknmKT1cCdK55GoTkVSqRPcniAh7C0SNLYLU60isHQSQBeW4IghAlmTJo5ibOJRVGR655GoTknTCqNC26kkyYhzAbG0USRDQooZhICtKlfGStLpnKnnjzfNKZAB0mY1yeBrEBk6msaArXqUGNw8ezt0Fj1u7NHIASilMHJRQTRD6QqVlNiZZBpzMBOkxGyZ6G0IBJZg4T0+yubkwpDh+LYWwkcz0CYD1L3Q4PoFBgQnWO9dKRQgHQ1u1GZ8hwLC/SAv2xP0YQyULGDUHkAjk0eoGH3P1hZEZswOQDN02PXiWW7PWI8vls1VXcFABffN2JZ7YOGI7L501C/dhSMnCIlEM5NwSRA+TS6IVcwmpzCSbydoq8zlRgJXs9xD5vtXuAXExvCRvXAEXQY8MYNkBwjc9sPYi2bneGhc0dvD4/ystNMBcVZlqUnIM8NwSRA+TS6IVcwlKkE6y0KjULv84k+CZ7PcQ+v+dIGyorTNnvkeMi4oVSiFSAWXtcqDBrc2d9GeTRP/8LXXY3Hr11dqZFyTnIc0MQOUC6Ko8GG2IDJiuKtVGrxJK9HmKf9/uRcx45MS8UYyBy0aoLYCxU59T6iNyEjBuCyAFycTJ1TsAZMPnra8/H2uUzUFcTzAlhkqHXLJuGR1bO5HlTkr0eQp9f3FiL9/acyOoGhEKIeaGUygCWz+MbjjfNm4S/f9ScU+sjchMKSxFELpCLk6lzBbEBk9GSoZO9HqHPr10+A3uOtMHvB97adRQd3a6c88iJNVHUKAtQW12E1ddPha3XA7Neg79/1IyDzZ24+uLxGZSYGAyQcUMQ8SLWFyXVZHnl0aAj2esRCIbFKitM6ZueLiecXj2rr5+KDVsOoNXaG+HFOt7rxfrN+3NvfUROQ8YNQcSDWF+UmtL0GDhEfpGrHjmBJOJVC+tQYtLAqFPz1pCT6yNyHsq5IYg4EOuLYrVRDgGRIGkehioHQknE61/ZHzRswteQg+sjch/y3BBEHIj1RbHaXAM5GwQRL5nqPp0gTBJxWbEOFzVUsb15HC4vtSYgsgIybggiDsT6oljMugxKReQ0Odh9utikxTCLHo3TqrFpRxMrd2WFEcNL9VkrNzF4oLAUQcSBWF8Ui5m8NkRi5GL3aXOhCivmT2YNG2AgNJXNchODB/LcEEQ8cPqiWG0uWMy6oGFDycREgmS8+3QiIbEAEAgEsqNrdo6F9OJh9Q3TAQBujy/DkuQeZNwQRLyI9UUZrMhdGp/HNyshxPrEFBs0ietC6ueSCIkVG4XlVigUwRycdFyzHAzpxcODz3+K1TdMh62nL9Oi5Bxk3BAEkThyl8bn+c1KCKZbcUSvG4Mah48noIs4dCgWEmOnn8eQe9XCOqx/ZaCHzeLGWmzYcgB3X92QFu9NMvIT+Q3l3BAEkTByl8bnYv5J0gSERz3YnOK6CJ/AzSUeHUYLiUmRu8SowdxZNVg0pxZzZ9XgrV1H0WrtTdt4haTkJ/Ia8twQBJEwcpfGZzz/JFMIdDsW08Upay/WbRL3ysSjw6ghMQkY9Rps+7A54c8nS7LyE/kLeW4IgkgYpjSeSzKl8SmZfq5AVE9HtiKmi5azjqhemXh0mIoBoOkc6Jrp8xPZC3luCIJIGKY0PjznJtEKMtH8k0Rb9udwDo+QLm6+cjJe2n6Y975wr0xcOpRpAGjGxitk+vxE1kLGDUEQiSN3abzMN6ucTjgV0IWyQAm7k587E+GViVeHMgwAzehA10yfn8hKKCxFEERyhErja4ebg3k2yZbGyziLKGsSThMNjYXpwqgtkBaGoXlOxCCHPDcEQeQXCuBUmwNnOpww6NWZTziVMzRGYRiCkAQZNwRB5A9hhsQwiz4iJyipHJ4EkD00RmEYgogJGTcEkUoGWbfdlBCHDsMNiVZrLzbvbMLa5TPgdHkz4unImvJ22os5x+obpsPr88NcVEhdiuOEjBuCSBU5XKmTNcSpQyFDotXaC6fLi6oyQ/CFNOte1l4siRooSqClvReHj3XCHwA+2ncSV18ygfZilvPg85+iy+7Go7fOzrQoOQclFBNEihiU3XZlJl4dpqRPTpLI1oslZOjd/dQurHnuM9z95D9x+ERP7ORkBfDlsW48+MJubNzRhG0fNKNxWjVe2n6Y9iKRt5DnhiBSRNaEI9KJzEM0o+pQr47wYsjeJ0cOZEoC5hp6ZcU6XNRQhZY2OypKCoN6jhKqY+Y/AUH9bdrRhLmzaqLvRY6XyBNQQKMEeXmInIGMG4JIEYOuNbzcQzQRXYdi4aoJVUV44vYLccbqyJ5qIhmSgBlDr6xYh0tnjsamHU1we/ux9f3muEN1bm8/lEqI78VEQqqU00NkERSWIogUMdhaw8s9RBMQ16FSqRAPVwWAERXGvOvxwhh6FzVUsYYNkHiobsKoUtG9GHdINdGQGUGkCPLcEESqGGQ9SeQeoglAVIcn2pzxhfzS5VVINiwXRU7G0Gtps8e1dqFQ3aqFdags14vKFm9IVbZy90x6f2QOqRKZhYwbgkglg6gnCTNEMzyElOgQTRYBHcYV8ktX1VqyYblYcoYMvYqSQmx9P45J3GJGdhSZ4g2pypJflsnqwhSEVInMQmEpgiBkgRmiyQ0hsUM04yXGuIJ4Qn7pqlpLNiwnSc5AUM9xhzvjHMcQb0hVjiq1TFYXpiKkSmQW8twQBCEPcg3RlPIEHyPk5/P5YXUEQwxFRi2qhhrxdUsPe4pUVK112pMLy0n2fqQj3Bl2jqEWIzTKgOg55KhSy2R1YUpCqkRGIeOGIAj5CA3RZG8ICbj0JedviIX8lMD7e09iw5YD7I32Z3PPBXCMNXBkr1pTAFq1KqmwXFyhoHSEOznnKC83or3dHvW9yRpcmawuTFlIlcgYFJYiCCKrSHaSdzDEcIBnHP1x2yHMmz0WQPCmdfOVk6EsUMpWzWPr9eKZrQewuLGWF8q5ecFkyWG5nK+uS3ISeSbXL2tIVUZW3zAdj946G26PL6Ny5CLkuSEIIqtI9gleLMSgUAJ3Xt2AlrMOvLT9MOxOr2wJq90OD1qtvXhr11HMnVUTNJoCQJlZK917FQAmVBdh7fIZ6LS7UGzUwuvrh63PNzh6xmSyulCukKrMPPj8p1h9w3SaK5UAZNwQBJFVJJu/IRZiKDFpcf//fMp7Panp3BwYg6yj24XN7zax55x57hDpB1EAh4/zc40WN9Zix2fHB88cqExWF8oQUiWyBwpLEQSRXXCe4Ncsm4ZHVs6M68YeDDFMjggxBPz+pMJd0ZAjpCKUa7RpRxMuqB9JM8kIIk7Ic0MQRPaRzBO8H7hwykiMLDfwQgy2Xl/qElZlCKmI5RpBIVI1JFfDOxqbQOQhZNwQBJF3qFTKiBBDyodqJhlSEcs1QkDACJOr4V0mG+cRRAqhsBRBEIODJMNdsiLQpFAotLW4sRYffXEyIsQlV8O7TDbOI4hUQp4bgiAGD9kwDiOKt4Qb2jLo1HB7fZgytiHCuyRXw7tMNs4jiFRCnhuCIIg0EtVbwukVYzFqMLxEL9gzRo5xB3IehyCyDTJuCIIgpBBj3pVUkm1SCMjX8C6tjfNk0h9BSIHCUgRBELGQMfFWljEDcjW8S1fjPEpcJtIMeW4IgshPEvUUCHxOzsRb2bwlSY47kP04UaDE5cRYfcN0eH39sd9IRECeG4Ig8g6/P5CYp0DEw2AoVCWXeBvWS2ZCdYbGDGQISlxODGb8AhE/5LkhCCLvaO1wJuQpEPMwGHTqxBNvQwbT3U/twprnPsPdT/4Th4/3wKxPrbckm6DEZSLdkHFDEETe0WnrSyhpV8zD4Pb6Eg4lxR2SycPE25yfeE7kHBSWIggi7yg1F8aftKsECnUqwc8ZdWoML9UnFEqKKySTr4m3mZz4TQxKyHNDEETeMazMEJ+nQAnsb+7Ef2/ch8WNtcKfSzDxNp6QTF4n3qYhcZkgGMhzQxBE3qFUKuLyFFhtHmzYchBubz/e2nUUc2fVQKkE6mvLUWHWJnUjjmemVdoSb2lYJpHnkHFDEER+EseoBavNxRoVHd0ubH63CQAwapgZFSZt0nJINbRk6YETi3wNfREEBwpLEQQx6LEU6QRDRxazTvxD8ST+SgzJpCPxNq9DXwQRgjw3BEEMeiwmDVbMn8SGprTqAqyYPwkWswbwC3wgVd6PNCTeUs8ZYjBAxg1BEIQfqKspxdrlM2C1uWAx68QNG4h7Px5ZOTN5AyHFk8vTEvoiiAxDYSmCIHIbufrC+AGLUYPa4WZYjOKGDSDP8MtMQT1ncofg+AU/zEWFmRYl5yDPDUEQuUtYeGiYRY8V8yejtcsFg06VsiqgmN4PZbACy2pzwVKkg8UU3VgSJFUVTdRzJmd48PlP0WV349FbZ2dalJyDjBuCIHIWbniorFiHxmnVePCF3SmvAopa3q0I9swJz9+pqymVbuCkuqIpxaEvgsg0FJYiCDnIw5b5uQA3PHRRQxU27Wji5cG8tP0wrHZPYteFc00dnn7+9cWA92PNsml4ZOVM1vDg9sxh5Niw5SCsNukhK6poEoC+Y0QckOeGIJJlsPUNyYYGcCEZfP4AljSOw87dxwEFeGEixpNz7zOfsNdl1cI6TBxVHNuDwrmmJoMal80cjY0hw4l7fYW8H9yeOQxub38wRGWUlrQrltNzytoLc2We7qtoDLbvGJE05LkhiCQZVE/ZQhOuT/Sk9ymaI8PaP/0LW9//BpfNHA2dRsnrVSPkyVn/yn60tPfGlJd7TS9qqGING+Y40a5vQj1zwhAb2dBy1pGf+yoGg+o7RsgCGTcEkSS5XDkTL9lwkxGSYeOOJkwYbcGqhXWsUaBUQvC6HD7WGVNe3jVVCB9H7PoyPXO41UhszxyJmAtVuPnKybxjLG6sxc7dx8X3VR6HbQbTd4yQh5SGpRwOB5YsWYINGzZg5MiRuOeee7Bnzx4UFgbL2latWoXGxkYcPnwYv/nNb+B0OnH++efjgQcegEpFETMiNxhMfUOyoQFct1NYBrfbh4nVxXhk5Uz0evuhViqx9f3miOvi9yOmvOHXNK7rG2fPHEECQM1IM25dfB763D4Uaguw9YNvYHd6hc+b52GbwfQdI+QhZZ6b/fv346qrrsKxY8fY1w4dOoSXXnoJ27Ztw7Zt29DY2AgAuPPOO/Hb3/4W77zzDgKBADZv3pwqsQhCdgZT35B4JlynikKtSlAGnVbFVgFNqimHxaTheXIY78dHX5yMKS/3mr73+QksEZsULkYcPXMEUQLNJ214YtM+/L9XvsATm77AzEnDccfSKYLnzQaPWioZTN8xQh5S5h7ZvHkz7r//ftx1110AgL6+Ppw+fRqrV6/G2bNn0djYiFWrVqG1tRUulwv19fUAgPnz52PdunVYunRpqkQjCHkZDH1DQgm8jj4vVi2sw/pX9seccJ0qHH1eLG6sZfNpGKPF2ecFuEMuA8DEUcVYff1UHD7WCb8f2PHZcVx9yQRJ8o4o0+POqxtQqFXBbFDjvLFl8l3fGEnZVpsHT792ICL0tnbFDMHzZoNHLaUMhu8YISspM25+//vf8/7d0dGB6dOn4/7774fJZMLy5cvx6quvYuzYsSgvL2ffV15ejrNnz8Z9PovFGNf7y8tNcZ+DEIZ0GaQ89ltiHyMLden3B/DJwVY8/te9bKO83/z0uygoUMBiLsSwMgOUyvQleHT1+fDstkOYO6smmFcSCBotd1x9Pk9/zH9bSowYUmpAp70PF51fGVPe8PVq1QW47aopmDFpmCzrlHL8r8+cFjRWuuxujB9liTimJ6AQDNsMtRhRXh7fb6MY2bA35fiO5RIqtQpqdfCaZoP+c4m0JbZUVlbiySefZP99zTXX4PXXX0dNTQ0UioEfjEAgwPu3VKxWB/x+aWZ8ebkJ7e32uM9BREK6lI9s1aWtz8veiAGg1dqL3//pX3hk5UxoFAFYrY60ylNiUGHRnNqIJnklBhWrv3BdahTAULMOQGx5w9fr9vbj8b/uxdASGeZGSTx+qUiOSYlRK7hHNEoINhXUKAOy7Kls3Zu5QDJGyV3XnA8A8Pr60d3VK5dIeUM03abNuDly5AiOHTuGiy++GEDQiFGpVBg6dCja29vZ93V0dKCioiJdYhEEEYOsC3nIkbAbhVSvV8rx455SHitsEx4G06tgc6awV1E29ELKA2j8QuKkzbgJBAJ48MEHMX36dOj1emzatAnz5s3DiBEjoNVqsWfPHjQ0NGDbtm2YNWtWusQiCCIGWVmpEkrYZZviyWTYAKlfr6TjJ2LAiY1UEKikWjF/EjbvbEKrtVf+yqo8r9wicoO09bkZP348brrpJlx11VW47LLLMGHCBFx++eUAgMceewwPPfQQLrnkEvT29uLaa69Nl1gEQcRgsFWqxL1eqf1lQu9z9HoiqrgEj59sxVUIoUqqDVsO4oL6key/5aysyvfKLSI3kOS5aW5uxt69e7FgwQLcdtttOHToENauXYvp06fH/Ox7773H/ve///u/49///d8j3jN+/Hi8+uqrcYhNEHmGAjjV5sCZDmd2uPHDwgoTqvO8UiXR9Ur1UghML199/VQEAoGU61MsDMY1wiLCbmFTzUtKpFtWWRfGJAYlkoyb+++/H4sWLcL777+Ps2fP4ve//z3+8Ic/YNOmTamWjyDyn2xz40eRJy+nSCexXjEvxSMr+cnH4e9rtfbiwRd2D7wvhfoUC4Nxz8kLiymFpppPRl1NiSTvUVaGMYlBh6SwlNvtxo9//GN8/PHH+NGPfoRp06bB6yUXI5Fm8rS9fFrc+HHobrCFFWKuN4rupI4FyOT4AKEw24r5k/DRFyfZf3PDYsJTzQ9Inmo+2MKYRHYiyXPj8XjQ0dGB999/H8888ww6OjrgdrtTLRtBDJBt3g0ZSbkbP07dDbawQtT16tVRdcf1UpQV63BRQxWUSsCgU7M9eIAMezOEKqkMaowe2iAYdkt6qjk13COyAEmem8WLF+P73/8+GhoaMGbMGCxYsADXXXddqmUjCJZ89iakeqRBvLrLhhEL6STaemPpjvFSDLPocenM0dj2YTM27mjCvc98wpuWnnFvRqiSqqrMEDRQ/WH/5hgeckw1jzgfGTZEmpFk3CxduhT79+/Hf/7nfwIAtm7dikWLFqVUMILgks9TgVN944tXdym/ETNhng4nrA4PTnf2pj7MGCW0FG29MXUX8lLcftUUdhwE8x6eAcnxZqxZNg2PrJyZtV5H4anmk+Oaak4QmSZqWGr9+vVRP7xq1SpZhSEIMfI6STF043vi9gtxxuqQ3Y0ft+5SGVYQCJEtbqxlZz6l5IYfKywXZb2SdBcAnH3e2KE8sT402YZAj50xlSXo6nJmWjKCkExUz01XVxe6urqwZ88evPrqq+ju7obD4cC2bdvw1VdfpUtGgsi8Wz/VBIARFcaUuPET0l2KwgpCYZ5NO5pwQf3IlIUZJYXlRNYbU3cKwOH2QaMtkCeUly1J82E9dlSqtLVEIzisvmE6Hr11NtweX6ZFyTmiem7uu+8+AMC1116LLVu2oLS0FABw8803Y+XKlamXjiAYKEkxcbJId9F6rqQqaTmpBOloulMAzaftONnuwD8+Ox4xqVzStPSw/jrtXX147OW9wh4mYtDx4POfYvUN02Hr6cu0KDmHpGqp9vZ21rABALPZDKvVmjKhCEKQXHHrZyNZortoPVdSFWZMOqQpojtbrxfNp23Y+v43cHv78dauo5g7qwZKJVBfW44KszamYRMeLlvSWAuTQQ13d79ozxyCIGIjydc4btw43HPPPfj000/xySef4I477kBdXV2qZSMIQgrZEsqQgFCYZ3FjLT764mTyYUaOHk61OWJWKimViqR01u3wwB8IsEZTR7cLm99twsYdTei2u2MakELhso07mnBRQxX7nnxJmieIdCPJc7N27VqsW7cOv//976FQKHDBBRfgF7/4RaplIwgiFrnW/ycszGPQqeH2+jBlbENyobIYeggPLbV3u3Dn+n8mpbNikxZKhULQKySlbFrKWIS0JM0nOsGbJn8TWYwk48ZoNOLWW2/FD37wA/T392Py5MnQ6eLoeUAQREqQ2v4/qwgP80DDvp4oMfXAOaetz8vmtQi+VyLmQhVqhpuxpLEWGzm5NjdfOTn2BG+Ih8uUCgX735LydpIhUeM414xqYtAhybg5cOAAVq5cibKyMvT39+Ps2bPYsGEDpkyZkmr5CIKIQt53E5boHYhHD7LpLADUDDdhSGkhxlQWo9flQ6lZC4tJ2gRvJlwWbiBUVhgwvro4LYnfiRrHOWlUE4MKScbNI488gscee4ydAv7JJ5/g4YcfxubNm1MqHEEQ0cnr/j9xeAfi0YOsOgsARq0KRi3np1TqAO0olVjs8VLsBUnU0Mt7o5rIeSQlFDudTtawAYAZM2agr49K0wgi0+Rz/594xkbEo4es0lmGxxQkOmpjsI3oIHIPSZ4bhUKBU6dOYcSIEQCAkydPoqCgIManCIJIOZnsYZPihNK4vANhehhqMUKjDAjLE0tngyhRViw0FmsPJfo5gkgXkoybn//851i8eDFmzJgBhUKBjz/+GPfff3+qZSMIQgrp7GHD3PidHigUCmzYcgCt1t6UJJQmMjaC0UN5uRHt7Xbxg0ckNQfLyNOxLsmkysgKO+6E6rDqNY8Ptj5f9PNlUWNIghBCknFTX1+PP//5z/j000/h9/uxfPly1NTUpFo2giCyCZG5UG/tOoqObpfsCaVp8w6keV2JyiSLkSV23OoinOroxUMvfi79fFnSGJIghFAEAoGYW/KSSy7B9u3b0yFPwlitDvj90r5d5eWm6E91hGRIl/KR7bq09Xlx91O7Ijwpc2fVYPO7TQCANcumoarMIN9JOZ6iYoMGZoMaNqeIN4PjkRhaFiUslQ3rAgAlYLV5gsMpi3S8KisxmWIaWTG8PdzjlhXrcFFDFZRK4Lzacvz3xn1otfYKni/b92Y2U15uSurzXp8fapUy6FGjMQw8oulWkudmxIgR2Lt3L+rr66FU0gA1ghiMxGo6l5KEUq53QAEcPi7izUDino6MrEsJ7G/uxIYtB1l5V8yfhLqaUsCfYDWSBG8Pc9yyYh0unTmanYW19f1mnrdK0vmIlPPg85+iy+4GADx66+wMS5NbSLJUmpubsXTpUkyePBlTpkzBeeedRz1uCGKQIVYhw8yFEq04kmk8RLTqqXgqq2RbVxJYbR7WsGHk3bDlIKw2T1SZohlZUnTAHPeihirWsGHeuyls9ANVPxG5jCTPzV/+8pdUy0EQRJYjlAOzamEdSkwazDx3iHAuTDRvAuJLmI3mzUAACfdd4a6raqgR82aPRQABlJp1uKB+GAyaAtnzSaw2l6C8VpsLFqMmoXwjKd4e5rgtbXbB9zKOeap+InKdmMbNX/7yFxw9ehTTp0/HnDlz0iETQRDZSKwKGYGbYLROtqc6euMKI0WtnhKZ8STJ8xBa12O3/Bu+bunBE5v2sTItnzcJ9WNLgf7Yh4kHS5Eu+kyqBKqRJFWXhY5bUVKIre83R7y3YVwFxleXUPUTkfNEDUs99NBD+Pvf/w6tVos//OEPeOGFF9IkFkEQWUmcTefEvAkdNnfcYSSh5nurFtbBbFAn35gvADhdPjyzlR8qembrQbR1u6UdIw4sRRrcfe35WNJYi0VzajHMoseK+ZOCM6k4MkXoOkqIT7IOAoDFpBF8r8WkiX1tc2gKPTF4ieq52bVrF7Zu3QqVSoVrr70WK1euxPXXX58m0QiCyHXEvAk6rSr+MFIAmFBdhNXXT8XhY53w+4GX3/kKV18yAROqiqQ38ROhUyRU1GlzocKkDb4gR+8ZJbD/a34y8coFkzGppgTwRflcrITheLw9Yu9FqN+P2PrCZAgaZZMRCARQbMzvhodEbhHVuFGpVFCpgm8ZMmQIvN7YyXkEQRAM0XJHEgkj2ZxePPjCbt7nuH1oJDfxE0AsVFTKhIpk6j0jlEz81KsHsHb5DFiM8ScM88rD4+k9I9DIMFp+VLgMZcU6NE6rZq8HTQYnsom46rpp5AJBEHHB8RCsWTYNj6yciQlVRTBqCxIKI0VNKhYijhBKeZEWy+dN4sm0fN4kVBQHvTbJVGRxZWnv6cPc2TUoK9bx1mC1uaJ+PO61x4mU9XFlEKq4iksfBJFConpuXC4X/u///g9Mn7/wf0+cODH1EhIEkduIeBMkh1A4oSCDXo1hFn1EszlBj0+8npZ+oH5sKR64aTo6bS6UmnVBwyZkTyQ1CTtGF2ReMrEIqZ4AL8V44smgSLxCjSBSTVTjxu12Y9WqVbzXmH8rFAq8++67qZOMIIj8RkoIRcAoWDF/EjbvbOLNfhIyjCSFccLpBypM2oEcG869OxnjQkiWTTuaMHdWDbZ92DyQTOwXP0aqx1FIWR9XBubvqTK2CCIZJI1fyAVo/EJmIF3KR07pMk2Ts8XGEKxdPgNOl1fU41NebsKe/zuDNc99FnHMNcumoarcEL/8SeTcnGh3Cspy1zUNKC8qjGnYcGXgjaOQathIuV5R1ldextmboWM5XF502T1Y/8r+5JKL83wKe7LjFxho/EIkCY9feP3116Me+Cc/+Uki8hAEkcukaqijAGKhEqfLOzDrKYG+OAnJn8QkbDFZhpfqg14kKYZNSIa4h1VKvV5S18eRYXipHo+snMkaOnEnF6dxL+UizPiFR2+dTYZNnERNKN6+fTu2b9+O//3f/8UDDzyAd955B++++y4efPBBbN26NV0yEgQhJ0n2KYmZeMo5vsPTD6vTg6bTNlidnjhLGBIbQ8Ag1vdFqVQknhgcZ5+fWLJI7sMj5ZqJvCeuROh41xd6v1GnZj04Mc/BIekkbYIQIarnZsOGDQCAm266CY8//jiqqoJzR06fPo377rsv9dIRBJEYYq5+GZ6UoybW6tXs8U0GNS6bORobQxU14cMhpZBUnomIJ+JEm1PeRFgpYZUkvD6SrlmU9ySVCC2RRM+RDtmIwYmk2VKtra2sYQMAw4cPx5kzZ1ImFEEQSRDlRpdQkm0Y0cI93OPPbahhDRvmXBu2HIzZz4VHMkZB6PPhYRxZq47iMRYTCSlBWmJ0tPekusoKSFyn6ZCNGJxIchKXl5dj3bp1aGlpQUtLCx577DFUVlamWjaCIBIgmqs/2jgEqWGqaCEW3vFFSoVj9XOJIMFQUCLyx0s6wipSSrSje0DkW68YiZ4jHbIRgxNJnpuHH34YDzzwAObOnQuFQoFZs2bhwQcfTLVsBEEkQLQbXbFJi2EWPS6oH8kaMR/tO4lvTvZg444j0sJUUbwp4U/iUYdDZopkvUEcku19I6VKSIp3I+p7ZFyvKJxzdNjc0GlV0gyUdMhGDEokGTcVFRV48sknUy0LQRAyEO1GZ9arsGhOLW+u0c/mnot3PjsGII4wlUiIhZsj897nJ7CksTYi50Zy2XMqSTBEFE7CYZU4wllS8o5ivkem9cYi3knvjCzpkI0YXETtc3PFFVdE/fDf//532QVKFOpzkxlIl/Ihmy5j5NwI9Y2ZO6sGm99tYl9bs2zaQKl1AudnerGUmrRw+/yw9rhgMevSZtikbV9ydG0yqDFnajUqhxgxoswQtV+LWP8eUaNSSn+bRHvgiKyL61UaPaIYVqsj6kfiXtMgIZk+N9xScPqdjSThPjdUEUUQcZAtzciiuPrFwijcPJukEzrDnsSNWsDCHC+WYZNqHcp9/JCuH131PXzbasfTrx2Q5LWIO5wlxbshlwdEwDi+7aopqB1hinpMqnwisomoxs13v/td9r/379+Pjz76CF6vF9/73vd4fyOIQU+2NSMTudGJhVGUCgX733K29I+LVOswVccPAH5/gDVsgNjhvWyuEhJKkn78r3tjemCyeU3E4ENStdTrr7+OW265BT09PXA6nfjVr36FzZs3p1o2gsgZZK+aSbLRnhhi1Skzzx3Cm9rN9MRJhQxipLryKJXHj3didzZXCSU6fTyb1wQg7ftZDlbfMB2P3jobXp8f5qLCTIuTU0hKKH7hhRfwyiuvoKKiAgDws5/9DDfeeCMWLVqUUuEIIleQ1SWfSg9GlJCVUati35MWT1RYiKjbmdqwRirDJnF7LbK4SihhD0wq15RsODHbPKsSYXJuAODRW2dnWJrcQpLnxu/3s4YNAAwZMgRKZZx91Akij0lmTEA4Ke+dIqFvTMplCN1s7n5qF9Y89xnufvKfUCgUsulQCDmvUTgJeS1k7t8jF0Jrue2qKZJLu2Vfk8BeOXyiJy7PC415GHxI8twUFxdj586dmDNnDgBg586dKCoqSqlgBJFLJDUmIIxsSMyMS4YEnqqFbjYbthzAqoV17IwiWfN/lIC3349fLKrHyTYHdu4+DrvTi1UL6+Do8wIKRULeAO66J1RnpycmbgQ8MKNHlsSslkoVcnTVzobvFJFeJBk39913H1auXInf/e53AAC1Wk19bwiCi4wu+WxIzJQsQ4LufqGbTau1FyUmjfwGghLY39zJ6+2zYv5klJfo8OQr+9Fq7Y0/TBFl3XnRryUsIV2pzFyCihyGSTZ8p4j0Iim2NHbsWGzfvh3PP/88/vjHP2L79u0YN25cqmUjiNyC65LXq2HrTSx5USgswHgY0pUIKTXMkqi7XyxEZNSpZQ9rWG0e1rBhZNyw5QAONXei1dobl9wMaQ9z5GAyrFzIEU7M+mRnQnZiem7OnDmD3t5enHPOOfjb3/4Gp9OJgoIC3H777dBqtemQkch3sqU/jFwkm7wY5gVSKBTYsOVAYh6GRJHoiUr0qVrOMF4srDaXoIz+sP6l8XgDUh22C/98LibDyoUseyWLE7iJ1BDVuDlw4ACWL1+Oe+65B+eccw7efvtt/PjHP8bevXvxl7/8BTfccEO65CTylTz84ZYjR4DxAgHgdX1N6FiJIqEpXFZW1oRhKdJF7e0Tl9whUh224yLLfspl5NorNOZhUBE1LPXEE0/g8ccfx49//GMAgMFgwKpVq/C73/0Ob775ZloEJPKbfKxiSLRPSKqPlQqScvenqVrIYtJgxfxJPBlvvnIyaoabEw5TyBq2UwJWhwdNp22wOj0Rv8px7YFsCF+lQoZ490o26IHIKFE9Ny0tLZg+fTr7b2YM1YgRI9DT05NayYhBQT5WMciZvJj1iZC54O73A3U1pVi7fAasNs6MqwASl1uusJ1gsvMk1NWUsqMq0uklShqSgcgSonpuNBr+l+cvf/kL+99mszk1EhGDilT2HskUciYv5kQiZJb2a+HhByxGDWqHm2ExhoZ3Jiu3hM/H2t/Cyc4HYbUNeGVSndwtJyQDkS1E9dzo9XqcOXMGQ4cOBRAMSwFAa2srdDpd6qUj8p50JpamDTm9GULHMqhhc+ZRAna2IWOCe6z9LZbsbLW5gkYYwNsDDpcXWrUKTpcXtj4fT7Zs8IKSDPKy+oaByAkzgsHW05dBiXKHqMbNokWL8Ktf/Qrr1q2DxWIBAPT09OCee+7B0qVL0yIgkefkQlgjEeRMXuQeSwEcPk4u95Qhd0gjxv4WS3a2mHURxzHr1TjV0Yt1m3cLyiZ7CJNj5HkCCmiUiKkDMRlUqgLYXHxjTNYqSc6xDHo1hln0bJk/I0MueoO54xcAGsEQD1HDUgsWLEBDQwN+8IMf4Morr8SCBQvw/e9/H/X19bj88svTJSOR7+RCWCNLIJd7akmJfqPsb6Fk5xXzJwVzguKUTdYQZtjIg1v/8L6kkQdCMixprMV/b9zLH5sgw0gFMVnv3fAJFs2pxTCLnpUh60K5RMqJ2efm9ttvx3XXXYd9+/YBACZPnsybM0UQOUGe9NJJq8s9T3QWD2kPaYglO/sTkE1GL2jC5eccGTpsbnxzsgdv7jqKjm4XALDHYP5bjvJ24VEeB7F2+Qw4Xd788QYTcSFp/MJ//dd/4cEHH+S9dsstt2DdunUpEYogZCWPqifSVj2VRzqLh4xUp4WSndkcGwHDRrJsMoVDkzLyQjJ0OzzYuOOI4DEQgGxGpJisTpcXVWUGViZicBHVuLn//vtx9uxZ7NmzB52dnezrPp8PLS0tKReOIOQgn5qgpSsBW5LO8tCzE5d+lcFqJ6vNBUuRDhaTsMcFgCy6SmfyvRxGXtRjhCbAD4p2CURGiGrcLFiwAF9//TWOHDmCiy++mH29oKAA9fX1qZaNGAyk4QaZT9UT6UrAjqmzeD072WoICcjF1a9Bp4avvx9WuycY4jCGZFfE7k/DPYcsXjA5r32M68EYUi9tP4wL6kdCqQQmjrZAqVTgRLtT0jWMaowBshlqeVlxSSRNVONm0qRJmDRpEmbOnMmWgxOEbKQp9JF3T3ZpaCMfS2dxecOyNcQVbbJ3qDLpma0H0TitGpt2NPHeM9SiF+xPs3b5jIHwUghZPYdyXHsp1yMATKguwtKLx2P9K/vZ9y1prMWbu47C7vTGvoYxjDE5jXSNSol5F46BPxCAUqGARiVpJjSRx0jaAa2trbjmmmvw4x//GFdccQX7P4JIhnRV/uREI7xkSEGr+Vg6i2ckQMzrnKFW+dHkYv52Qf1I1rDhvsfp8vLWX1asw6I5Y2G1u9Da7YLD08+uI+kRGuHjGVTRxzWIEtLzt2ccaGlzwGRQR6ybpx+nlzVsmPdt3NGEixqqpF/DaJWQMlVJ2nq9eOzlvdi44wg272zCxh1H8NjLe6mCcJAjKaH4t7/9LebPn4/vfOc7UCjS9MtD5D1pCxflay8dQPwpfFQRrD0S80GEiKGzeLxhUa+zXo3DJ3p44Y8Jo0pRWa4fkDdFIa2oRgeT8KoQTnx1e/3s+suKdbji387By+8c4Xk4RpYbUTPclJznMGw8wzCLHgt/UItntkoIh3ER2CeLG2vxVqiSSeh7J6YfxnAJv4aZ8szlVdiZkA1Jxo1KpcJPf/rTVMtCDDLSGi7K04nAQt6HNz5uhsc3Slo+SDSi6CyePIdo19nW68VL2w9HhH5WLazDxOpiAKkLaUlJeGVeC39PeZEWK+ZPwoYtB3FRQxVr2AADHo55F45BebEOZr064ZyQ8PEMF9SPZA0b5lxi4TAuQvtk044mzJ1Vg83vNgl+78T0w8jMvYaZTNjPu7AzIQuSHJpjx47FkSNHYr+RIOIg78NFaUDoqfXyC2pizitKGo5nZ82yaXhk5UxRgyPade6wuQVDP+tf2c8LD6UidBlNLuZvH+07icWNtZHvKVSz/WlGDTcJeg78gQDrBZKqq3AixjOIeJKsNlfU40Tzwoh978Qa8r235wTvM5meXJ/PvyOrb5iOR2+dzf7P7fFlWqScQZLnpqWlBVdeeSWGDx8OrVbLvv73v/89ZYIRg4B8DhelCaGnVrenX/QGGO3pPm6kesOiXOdCrQpKpXjPEzn7ocQjFxD8291XN8Dh8go3hAsE+9OoVUpBz4FSoRjwHiToORQbzxD+74hxDWGIeTcmjynDzIlDhL93HP30evuhVxdAqVRg9HAzTw8Z95zk8e8IM37h0Vtno73dnmlxcoqoxk13dzcA4PrrrwcAyrch5CdPw0XpQig8NKRUn9ANMKWIXGeTXo3Rw4vS0g8lHrmE/sYdZMlF6BowOTfJ3mSZ8QyMJ+6jfSexfN6kiJwbsa7G0WS8ZVE9hpfoWENNkJAOaqpK2ZurUavi6SErSrHpd4QIQxEIBES3wfjx41mDJvxtCoUChw8fTq10cWC1OuD3S9vR5eUmsoJlgnQpHwnrkkm4ZZ5ajWrs/0ZiD5ZMowBOtDnR3uPCs9sORebVILGcm7Tvy9A16LC5odOqYC5UwaiTqZcPt1mgWQdLsQbWbk/McQ1iMibi3eDpUyjBG4kfO98pLzcl/Fny3EQnmm6jGje//vWvsW/fPlx00UW48sorMWbMmJQIKAdk3GQG0qV8SNZl6Obi6PNCq1Hxm8sxX4HwG6LUG2AmUAAOlw+2Ph9cbh/KzFr+zTGBm3Ja92X4zV6vgs2ZhQ0LEyG0tl5PP/RaFcx6Veyp9NnasDFDkHGTOqLpNmpY6uGHH0ZfXx/+8Y9/4Pe//z16e3vZXjdms1l2QQmCiEGopFeowoh3k5E4rygrCARDHWy4I/Qa97+zNuQQVmI9zKLHojm1PK9ZVjQsTASB8vHV10+NXhmVrQ0biUFHzGqpwsJCzJ07F3/605/wxBNPwOFw4Nprr8Uvf/nLNIhHEASXWM3lqHFZkoQ3zIvxCxlezXVB/ciISrVcvS5ClWqHj3VGrYxKV2NOgohFXD2qOzs70dnZia6uLtjtsV1kDocDl19+OU6ePAkA2LVrF6644gr88Ic/xOOPP86+7/Dhw5g/fz4uvvhi/OY3v4HPR+VuBCEEW3YrUhKcrvLbvCTUMO/eZz7Bw3/+HPdu+AT7mzuj/kpGlEHn0XURKvH2B8CWXDNwE7zFysJPWXvT1nmaIAAJxk1rayueeeYZXHrppbj77rtRVlaGzZs347nnnov6uf379+Oqq67CsWPHAAAulwurV6/GU089hbfeeguHDh3CBx98AAC488478dvf/hbvvPMOAoEANm/enPzKCCIPYcpugeg3GVnI0FiETMkS3jBPSn8g7vVgSPl1kUqSOhNa20f7TmLVwjreHly1sA6OPi9sLp+oPlrOOsh7Q6SVqDk311xzDY4ePYpLL70Ujz32GL7zne9IPvDmzZtx//3346677gIAHDhwANXV1aisrAQAXHHFFdi+fTvGjBkDl8vFThmfP38+1q1bh6VLlya4JILIYbiJwAJjE7jTmhc31kbk3MhWpZJNuRNyyCKW5MrRt16nhsmghrub0zMoRn+g8DLoj/ad5JVuZ2xCdZJT25UKoNvhxurrp2LDlgNotfZCqy7A1ZdMwITqgZ4yCoWC9/c7lk7BygWT8dSrByLGPIyvLqZxCETaiGrc7N69G1qtFq+88gpeffVV9vVAIACFQoG9e/eKfvb3v/89799tbW0oLy9n/11RUYGzZ89GvF5eXo6zZ8/GvRCCyHV8Pj9vjpBgCXdAQnM5Gch0S31ZZYkyfyu8ZJ6Zet3RHez4G7M/kFADOYM64w3lkp3azp3+vWphHSoshdAqlcG1+MEe4+6ndvHO8djLe/HgzTPZCd0IAG+FjkPjEIh0EtW4effdd2U7kd/v5zUBZAwksdfjxWIxxvX+ZMrzCD6kS3loOtElGBZ5cOX3UFtVwr7P7w/A43ei19MPk0GL2upSKJXyxmnOfNMumDvR6+1HTVWprOdKlSzMvjzV5hC80f9u+YwIfTMzoTbuOBIyLidjTGUJVKroEfzy8H9b4lykzMSjMyH9bOTMnVr/yn48cfuFGFHB/40VO4fL249Rw8x4/K97WWPptqumYPTIEtn3ab6z+obpAACvz4/ychO8Pj/UMfYiESSqcTNixAjZTjR06FC0t7ez/25vb0dFRUXE6x0dHaioqIj7+NTnJjOQLuWjo6dP8GbR3tWLklCjtHSFi/RalWBnYL26ILnrHSPsJpcs3H15psMpqFcxfY8ebsavrz2f7Q/U1eVMYKFJIEOfmGg6az7RyTt2R1cv5s6qYXNy3vv8RNBzxZn+3Wnvg0YRkHQOg6YAw4p1Ed4rq9WRkDpyHTn63DBQvxs+0XSbNhOwrq4OR48exfHjx9Hf34833ngDs2bNwogRI6DVarFnzx4AwLZt2zBr1qx0iUUQWUNZUaFgMiY3LJKuUtuUDCNMoBpJDlnEklzF9F1q1qJ2uDmYZ5Pu/kAh4/Xup3ZhzXOf4e4n/4nDJ3riTgYW01l7t4t37ObTdnQ5PNj2YTM272zCtg+acenM0Rhm0fOmf5eaCiPOoVQAS8KGii5prA16Z0K9iarKDMEQFvW4IdKMpMGZcqDVavHwww/jF7/4BdxuN2bPno1LLrkEAPDYY4/h3nvvhcPhwMSJE3HttdemSyyCyBrOGV4UkYwaPjco2gRmWXNhUjCMUKwaae3yGeIDPUNejCKDOuH8IrHZRzqNEksaa7GRk5S9pLEW2gy6/WXLdRK4fkqlAneu/yfv2M2nbdj6/je81zbtaMJtV03Bs387yOpqWJkhwvPSaXPjzV1HB7w+AeDNXUcxeriZ35AxnSTgGSTyk5TvwPfee4/97xkzZuBvf/tbxHvGjx/PS1gmiMGISqVEXU0p1i6fITo2Ia0TmGXuDGy1uaJPKxcYYyDU6r+q3BCfLCKG2ok2p7w352jhJIk3XVmN17Drd6I9MjznDwQEz6dWKbBqQR2rK6FcmWKTFnanF5vfbWJfS3gvyjGyIeQZzImZakTKyZB5TRCEIDHGJmTFBOYEsRTpxKeVC+QSrVpYh5ff+Uqeii0BQ03um7NoLpRC+k03lcar0LGVIlPXVQVKVJbpo04Ml20vypRHlpBnkMhbKO2aIHIJjhdizbJpeGTlzJyZ22MxabBi/iRejgYTdhMKx6x/ZT8uqB/JO4ac3X7lzCuKlgsVT3NAIZm4TfKSaV4odOya4eaIpnyLG2uxYcuB2HlcMu1FufLIonkGicEHeW4IItfI5kGS0fBDNOwmFo5Rhj1+yRqCkzGvKFo4yeXpjx6OiyJTeJO8pCrjRNZ72trLC829FerzIykUJsNelCsUF9UzSAw6yLghCCI6cuRDMIiE3cTCMRNGlbKvpyQEF+3mHMe6o4WTvIUB9m9lxTpc1FAFpRIwGzVwePph1BZETkE3qOHy+XH//3wqbyNFgfUa9Rps+7A5PXlcAsgVimM8g9zw380LJsNSpAH6Y3+eyC/IuCEIQhwp+RAKwOHywdbng8vtQ1mRLm4DSCx/o7Jcj0dXfY93bDnWFNVoCa3n21Y7nn7tgPi6Jchv1qsBBbBi/iRs3tmExmnV7MiMre83Y0ljLUaWG1Ez3MRLPt7f3Imznb3JeTQkGmeSc2fkNHITOX8s/EDdmFLcc91UfHW8E34/sOkfR6C+ZELOhG4J+VAEAoG8uOTUxC8zkC7lIxt1aevz8lrsA8GnatZ7oACaT9txst3BK6lOKHzC3Dy5ISIklmwqqstYxlro7y1tDl6JdMS6pcrPMVjaetw8TwxzzHkXjsH071Swx7U6PLj3mU8wd3YNtn0Q6VGR5LlJdLaUSHjOYjHi4y9Opa55ZIzzSyXmfs0A1MQvdWRFEz+CIBJA7mnYcR4vWj4EELwhNZ+2sYYN8/eEGgsKNH4LTzY1GdRoaXPg2zOOAfnD16QMjhQQWmOs4zlcPqzb/IVoiXTUZOZojev8gMvlEzymPxBAh93Nyskkxr73+QksDmuSJzXhOe4k3RhN91o7nKltHilT079Y+zXXWH3DdDx662z2f16fH+aiyIaKRCQUliKIbEXuUQthxxtm0WPF/MkIBAIoNgqHGWLlQ3Q7PFENgWSflrk3q7JiHS6dOTpiErpGpcRjLw/MMWJCQEJJuLGOd/OVk2EyqNl1ypmHIqZLpUKBb1p64Hb3Y0JVEZsYCwTtnVsW18Pt6cfICiPKTBpJ117uZo+dNuFRFbI3j0yStPaBSgPhnhsg6L0hYkOeG4LIUuQetcA9XlmxDo3TqvHgC7ux5lnxNv+xyqWLTVq2VwoXuXuzAMBFDVWsIQIM6KP5tC2izJopIQ/XWazjPf3aAcyZWp2U10QMIV0uaayF2aDGzt3HWTktJg1uWVyHy2aOxsYdTXj0pT14ZutBtHf1JaQ3hmSuSalZeFRFthkNKRkbQuQk5LkhCDmJlXQZR3v4bqe8T9/cp3kxQyEiNyFGubS5UIWa4eaIMQZyVTVxk02hgGhYJ/w1rpHm9vajw+YOdtpVADdfORlPv3ZA9HjVQ02YM7UaCgVw6+LzoFAAw0oLE1tP2PWeMCqoy7YeF7TqAtidHrR197LnZq7tiDID1m3an3CllNzNHoeVGXKjeWQKxoYQuQkZNwQhF7HCSPG0h1cCalWBrC525mneZFCjepgJc2fXABiYAi1qOEUrlw4ANcNNGFJaiLFVJcGKJrM2uRtKmIE4oTp4s3K6+wUTbEcNNaOsWBecZB16jXturboAOq2KvTYmgxrzLhyDCaNKhCdn61RsMnFSN3Gx6z2mFC3tTvzXXwZCaTf+eCL63D6UmrQAgG57koatzDd5pVIReTyDGjan/NVTSZOrfaAIWaGwFEHIRKwwkuROtQrgy2PdWP/KFxGhkVUL6xJ2sZsLVbhj6RRcNnM01m36gjcFuqxYl7jhFACMWhWGF+twzhBjclOghaZiH++BWa/GsBJdRMhhcWMt/vft/8NlnDX8bO65+OiLk+x7ljTWolBTwF6bjm4XNu44gg1bDkR05121sA4bthyQJRQodr3but1Y/wrfK/Pc375En7sfLW1OQCFTWEnuydzc4+nVOHw8+enlBJEqyHNDEDIRK4kz5uDIELZeL3vzeys02FGpBCqHmFBkSOImFQDKi3Vs8i1z/k07mjDvwjGorDBm3IUfayp2ZYUBq6+fisPHgn1MmG66G3c04d4bvguTToVuhwezp1TCHwhAqVBgZLkRfS5vhO5brb0oMWl43ghHnxet1l7e+xINBYpd706R1/2BwMBa9eqsDgPJNr2cIFIEGTcEIROxKjWktofnGkkd3S52sOOSxlqM+M6QpGQUM8DGjCzCOUONGb9xxjIQO21uWG1ubNzRFPEeZ58XI0qCuTFDy4w4Y3Ww4Rhbn09Q90admh/CEBkkadCpcaLdKT38ogBKRPZDqVl4HyDAWatejRFletx5dQMKtSqYClUw6mKcN0VN9oSQVI2VRnkIIhwKSxGETMSq1Ig2OJKLWEhiwqjSpKs+xI5dZtZmxY0nVjim2KRFoU4l+B7WSAwAIyqMvHCM1CoaofetmD8Jf/jrXunhl1Bo7YU3vsSyuedGHKuiWCsYXntvz4mg8WPSsqG5tX/6F/7juc+C4apoCIXzUhgmihk2S7M8BBEOdSgmkiIluszlJ75YnVa51TOcwZEAR5cCicmrFtZh4qhi0cqqeOSTtXeO3EjoIHyizYmOHhf+uO3QQH+aBZMx+ZwSdoaQ4L6UOiaCcw0NOjX+8Ne9vFBVrI63tj4vHnlpDy6oH4kigxojh5jQZXdjqEWPiiJt8BqGznHK2ouWsw7s3H0cdqcXtyyqx4gyfdxddlPdmTdCnzGuUzZ2Cs4UcnYoBqhLMZdouqWwFJFdZPvNNxaxKjVEBkeGH0Ow0iVZwybasbNFt7HkCwBVFQaUlerw6+vOx5HjXXHNEGppc8beW5xreKLdGXcOjqPPy5shxXhmyhjDhnuOyiKMsOgxvrqYXeuJNmfclVJyN+2LSYzrlHZ5CCIMMm6IrIISFUOkspw120tlJcjX19ePh1/8nHcDjbVPEtlbiXS81WpUET2ENu1owtrlMyStNZFzprUzb1jvnqoKQ9Bo41yn0iIdljSOY3sQvff5CdidXr48mfLQ5pBnePUN0yNe8/poxLkUyLghsgp64stT4rmhRHsvO9jSLrhPOuxu0bwksb11urMX3uJCwYaKiTTDcwpUZrm9/XC6vLyqODESOacsTfukXCMpvZoUQMtZB69XEDP9nJUnUx7aHPMM0/iFxCHjhsgq8m02DIH4biixcjlC3pe5s2sE9wkzo8lSaowQQ2xvHTttxxMbvxBuqJhAGK/YmOQeTiR0mGy4UeI1Euvds3b5DNZwE/KQbdzRhEdWzmSPlSkPLXmGBw9ULUVkFTQbJgsJTd0+3dkLq8ODEx3xTShnbihVQ4248+oG3PSTSVAoFXB6It3rsRohMt4XodlPixtr2RlNrR2R1UVCe4upUhJtqAjE3QxPlj2cTAO+AABFfGVJUueYRevVxCBlMnempnfn29RwQhzy3BDZRbYnvA42Qk/0L20/HJEkK9Wd3+3woGqoET+cNgrrNg14BpbPn4T6ME9JrLAk433p6HZFNDj80xtfsiMYOu19GBrWP4i7t0539uLYaTvbBJA5T3hDRTGdRA3fpHMPh/JfbL0eOF0+PBlq/ihUZRZNZqnhYCm9moQ8ZMMsel6voEx5aMkzPHggzw2RfcjdNp5IGOaJ/oL6kYKDNqWMJSg2aTFv9lg8GyrdZj7/jICnJFb/FK5XpKPbhW0fNkOrLuAZNsFeMYXCwoT2VnlxIbZ92Mx+hvlceEPFCKT2b0nHHg7lv9z7zCc4crybNWyAsOsjQWap4x6k9GoK91wNs+ixaE4t7n3mE/b87V19GfHQkmd48ECeG4LIFTJQ5cE+0YtM0JaS6G0uVOEUApJGT8RMjA15RR5d9T22X41WXQB1QfA5jXn/sDIDrFaHqEzMTTo8MdZSpIHNIa7juHI2Uny9ePkvnOtTVqzDZTNHo6JUjzPdLrj7/Xhp+2FhmfVq2Hq9cPR5sWphHTv2g9W7UQ1rjwdfnzmNUpMWFpMGdTWlWLt8hmCvJgARniuDTo17n/mEd/7HXt6LR1d9T9y7lSrdkWd40EDGDUHkAnJXeUi8eXCf6BN25weAipJCSaMnpN58wvvVrFpYhxKTJjhOQa+GUhkj58SPyJt0kQaHj0XXseRqvjRU5YTnv2jVwYnv8y8cA7ennxcCXNxYi7d2HQUAXNRQxTY0bO92sbPGhln0WH39VAQCgaDejWrs/0a4MiqiV5PAfjIXBq/Ft2ccgjrrtLsHPFtAREVcwrqTEDbM6lYIhCxQWIogcgCpCZ+SiKM1PuNJ+WjfyYgE3njc+VJHTwCIGdIR0sX6V/azc6Ik36xCDRVrh5thMQY9NrF0LDV8I+v1EoHJfwHAJljPmVoNm9OLjQJ9dn40YxQunTka2z5sxuadTfjd8//CyXYHTIbgNWy19uLBF3YHDZvCoMdG6hR7wf2kDL7+zckeSTqTRXc09oEIQcYNQeQAclZ5xHXzCHlS7r66AbWVRVi7fAbWLJuGR1bOjM8LwfGU/Pra87F2+YzIsutYhKq2znT1xa+L0GdPtItXeknRMTdno6w42KjulsX1weokRXzHikc2IbgGY0e3Czs+O47qYSb4A8IhwBKTLiJvauOOpqAnR0BGKZVRgPh+sto8WLf5C+zcfTwuwziZvZ4Oo5LIDSgsRRA5gJxVHnE3Sgxz47PhiHjd+VJGT4jBCVWI9bgR1YXEMIckHXNyfr5ttePp1w4IHlPy9UomBCMQWoMCON5qFzx3WXGh4HXnGlNcGROZYs89LmMcubv72co2KIDJY8owvEQnur5k9jo1ASUYyHNDEDmAnFUeUkMraSeKB4P7RC7U4yaaLqQ+zQvpeNXCOvT0etDa7YLDEzIEAoDfH2ANG6FjSr1eSXsauKE1kwY6TQFGDjFgSZh+br5yMgyFwtPUlaGeOOEyJjvF3mIeCJt1dLuw+d0mbPugGUZtQVTDLZm9nrV7m0g7NBWcSArSpXzE1GWsieNSUQIt7b04fKwT/gDw0b6TuFrC0MmUEkOmE+1OrHnuM/btZcU6XNRQhXNGmDG0pDBCF+XlJrR32GHrDYaxHv7z5xGnXLNsGqrKDPwXOTpWKBTYsOUAWq29vBECNcNNONHGl0fwmBKuF7OusZVF+MnsMXC5+1GoK0CJSYMhZpFydiE4HqCqoUYs/sE4aLUF6HP7UKhVociggVFfgAPNXXj61QFv081XTsY5w0zotLujTrHvcrhRYtSyhg13tpTFLJKIXV2Ew8f5r99z7fkwFKoHPisw8kKq7mLpIVWJ3PGSzFRwIby+fnR39cZ+4yCApoITRD4gR5WHAhE3nFUL6zChukieqeOJoAC+PNbNK0Ne3FiLl7Yfxt1XN/Ca9zEeDqbHDVuCHaYLvz+QWBgrpGMAuPupXRH5KfMuHIPyYp3kEFas61Vs0mJCdTEumlrNq25aMX8ShhQXSr4mXA/Q1y092PD6AVw2czSbWMwc8x+fHmMbH46vLkXVED3QDxi1KmEZQ56h8aMtrOEtOFtqTKngFHtu5ZulSIdvTvbgoT9/HlF9FbHORPd6npV602ypxKGwFEEMIsQqjWxOb0JJrXLJtJ7TgM5kUMPj9WPBRbVwuHw43dkLpVIRV6iitcMZNYy1amFd1DCHWO6GPxAI5W/IEyY0F6pw9Y++wzY4LCvWYe6sGpzt7EVbj1vyL3S4vBc1VEVUTG3YchCTxlRg87tN2LijCQ+9uBs2R3yJtmKzpaw9HuEKN07lm8vdL636Kpx4E66pCSgB8twQg50MNMbLJGI37VPWXp7nIJ2ufK5MZcU6XDpzNG/Mw+LGWuz47DhuuHyi5CfyTlsfz8vDJLQOsehx1tqLEpMm+vBLEc+MUqEIemfk8hAEAHuvhzVsuGvf+n6zuGdDRF6TQY2LGqowpFQfM3k4kUTbaBVUscZWJPTZLAwzEbkBeW6Iwcsg7IkhlnDZctbBe6JOZ/ksV6aLGqoiypU37WjCBfUj8djLewFA0hN5qbmQt04mjHXW2ottHzbDqIvdVTncM7OksRY1w80D3hmZPARMVZLQ2iV5NkLy3rF0Ci4L9bE529UreJ25MiaSaMvtrcM9TsyxFQl+lkq7iUQh44YYtAzGH06hm/bNV07Gzt3Hee9LqIdOrPCByN95MomMeWBelyrTsDKD4ATwj744KTl8VFlhwH03TsNvfvpd3L9sGhrGlaNmuEl2jwFTlaRUCq89vK+MoB4VgMmogdvbj7mza7DvyNmIUNyK+ZPw0Rcn2X8nEkaLqxmjDJ+lKd5EolBYihi05FVPDKnhNYFwirJACXWBEot+UMsaHB/tOxnfU32s8EGMvzMyOd392PZBc0Q4CIH4PA1KpSJivpHb68OUsQ2xw0cisg4tFu/NkhShfjVtZQZsfb+ZDS1BASgVCpQXc6qmBGS7Y+kUON0+Xs+dxY21+OTgacydVTNQUWZQY/TQhuTCaEJjK8JnS0X77JhSPHDTdHTaXCg161BRrAX6xT9CU7yJRCHjhhi0iP1wKphus7kS0483LyG8EkUJLJpTG1EBYzaoE6rWASIHSsYcOMnIpFdHDM5kcm54AzSlEL5OSGs+GNdwTLnwAxVFWtyyuA5tnX28KqfKCiNKDMFrKSRb82kbtr7/TUQob+6sGn5FmV+mmUqJNmNUIObsrnBiDlIlCBEoLEUMWsyFKqxaWBcRutiw5UBOhaaSDa/ZnF7BKpZ4KqhihQ8khxc4Xpw1y6Zh7fIZqK0Mjn/IRIJzVFnlxg+MKDNEVDmx11IJ9Hr8uOknk3DXNQ0YW1kU/JjIuAWlEgk3ekwFCe3TsP0Q99gPYtBCnhti8BIASowati18oaYA/kAAF5w3Ek53f848HSYbXpOjgipW+CCu8EK4x0Whga3XixNtztRVtHHCega9GsMsenj7/bzwkEmvwenOXmg1KjhdXhQbw2SRofKu2y58LRwuL46esfO8a8vmngvgGAq1BYK6nTKuAmUxqsLSScL7lKZ4EwlAxg0xqDHqNdj2YTDP4dKZo7F559dwe4N5H7lScppsXoLY54UqqMRCM7HCB0oFsKSxlhduWdJYC6UyRmlaOkqBBc5x85WT0efy4oU3D7OvlZi0cHl8ePmdI5GyQB45xa6FSlUQ4V17dtsh3Lr4PJiNakHd6tTKrNq7lD9DpBMav0AkRc7rMnRja2lz8PIWgOAPryx5FhKf6BPWZbIGgMjN/c1/fovzaoew4aj3Pj+BVQvqIkcWhK+TSVRWKtBpc6PYpEW3w431r+5nPSEIAO/tiXE8BKuCuJ2CAWnXJR5dip1j3oVjsHHHEfa1JY3jRPcIEOxoHJ4MPPPcIQPdf6Ugci1VKgUeejFyhMTd156PQk1BQrqNB1m+54O0Zw2NX0gdNH6BIMQIxfS1moLEXOaxSMcPerIN5QQ+X6BWYuak4RHegFKTNupxmKTg8DWvvn4q7E4vNr/bxL5dylN7yivaFMApa6/gOfxhz31iuS3dTg8QAOv94zYgHGrRY/LokqTHB1gdHkGvR5lZB7VKmZBu006ejUZIBzR+IXEooZhIPfG2T083AaBMpMFYsjeItPXSSbahXNjn+73+iMTWjTuaJHlHhda8YcuBiORtKcmuqZ7ybOv1ouWsQ/AczLRsBqVCISpLsUmLOVOrI5rwPf1aAsnpAtcyWo8YOSfGJ4zU77jYPs323wgi5yDPDZFacsQVHTVnBIkniuZqL51k5Bb6bGto5EG8T+2pLgXudniwc/dxLG6s5XlcVi6YDL1GxXpLtOoCFBnUWHrxuIicG2aPVA4xwmRQY25DDS+Ux9NZoknHMfrLZNQjEvYdH2bRY8X8yQgEApFJ1xI+n62/EURuQcYNkVIy0jMkEcRc5kjuhzdXkyiTkVvss0adOv6qlxSHMopNWtidXnb2FJMrM3qoCUadit/sUKmArdeDtctnBKulwmSprDBGTOLmhfKSvYlH6y+TwYoi7ne8rFiHxmnVePCF3ZLXmDO/EUROQWEpIqXkVPt0AZd5wmGlkJu92+HG6uunYphFDwAYZtFj9fVT0e30pMb9LpN7P5lQR9yfjSVzrJCb2Ocl6IKRlclZ2fZBMyorjDDqVBHnNWpVGF6qh7pAEeq6zD+gvz96KE9sL1ntHvnCMUnoItoxT7U5RD/L/Y4LzceK9X3Jqd8IImcgzw2RUnLVc8GQUHhG4Al91cI6lBVr0dHtjuupNi7kdO/H4zEJD7XoVexcJpfHhzKTNupn5a70umVRPUqKDdKOy1mnw+WFVh3sYWPr8w2EUzhVYAqFAhu2HIC33485U6tROcSIEWUGmAtVMfeK2N/3HGnDxh1Nye8HMV1WF+Hw8QR1LOH68L7jIrPBon1fcv03gshOyHNDpJSsSHZMgkQSWoWe0Ne/sh8qZQHWv7I/ZcnFsicvS0lSFpisvr+5Ew/9+XPcu2EXHv3fPTjVIV62mnR3ZZHPf3u6R/pxA4BZr0aPw4t7n/kEa57lTIhXctb37Gd48IXduGTGKMy/cAy2vv8NHn1pD/veWHtF7O/+UHgp2esl6hmyeRLWsZTrI/QdD19jtO9Lrv9GENkJGTdEamDc4G1OVA4xprd9uoyVF4n88Io9oVttLvnc7wJrTIt7P+y8DpdPoDLqIC6oH4myYh3mzqpBS5sdVrtH8Dp0OzwwGdRY9INaLJoT/J/JoI4uM0cGp7sfJgP/Wri9/bD29MWli3gMg5ffOQKb0xvxXqUCUfeK0F5a3FiL9/ackCRjND3YXD50O+Xfd5L2FMf7NbmmNP6quERHLFCFFREFCksR8hPLlZ1iw0bWyosEElrF3OwWs04e97vIGiuHGFPr3hdp9mcyqOHuHjin29uPQl0Br+fL1veFOz6XFulwxb/V4OV3vmKPufTi8eL9dARkWNJYizd3HUVHt4tds6WoMC5dxGuQhvfAcXv70Wl3R98rYXvJoFPjD3/dCwDsRHalQhG9l1AUPay+fqrs+y5ax2SbKxS6UwBWmydYxVWkw8TR+vgTwDk9kiSN2qAKKyIG5LkhZCdtvV3Sde44e8iIeXssZo0s7nexNfr7/Sl17wud9+nXDmDO1Gre+7TqAlSU6CUllvr9ftawYd738jtfRRgP0WTYuKOJlYFZ8znDi+LShVjIyCLS/yi8Bw5rLMTaK5y/W0wa3HD5RFw2czS2fdiMzTubsPX9b9DS5ozphYinl1Ay+05oLy9prMV/b9yLu5/8J060ObG/uRP3PvMJHv7z57h3wyfY/00nzIYEei4JhDgPn+gR1EUmf2OI3IA8N4TsZLK3S1b0lRHz9vijeIFCFSlnOmIPh4y2xlSWTYudl+sxYprLnbE6JV2Hjh634Ps6etww6yKvl5gMY0YWYc2yaeyaVSplXLoQ66ejVSkj5jYtvXg81CoFb823X3UeAOBEexzDPQNAebEOj728N+ImHasMOq5eQtH2XSxCe/mJ2y/EyTY7vjnZw/OS9br7BSfKr10+Y6BkXSLxlIRnxfc8Day+YXrEa16fH+XlJrg9Pth6+jIgVW5Axg0hO5msfsiayguxviNCr8fpYo+6xhT2OxE77whLWBjCoIbVJjwuIPw6FGpVgu/TicxjEpOhzKyVpmsxRAzSE21OvMnpgVM91IQ/vfElAPCmybu8/ex8qnhCJInepOPuJZTMvggAIyqMONPh4M3aAoA+t080nBevcROPLrLme55ihMYvMNAYhuhQWIqQnUxWP+Ri5UW8LnZZ1qgErA4Pmk7bYHV64PT2x0zMjHZeXijGHxwXIEVGkz440To87GEuFDZuZL2+oYTUb8860NrtgsPdD7OeH05hmvxtfrcJm3c24fgZO+xOLzq6Xexrbq8fT716IPb1E0iATXS8hKgeDOr4k2zD9eDpF/yckKyFOpVwOM+sk3Di2McX00Uufs+J9EJTwYmkENVl2ITodLeDz9i5E+BEuxNrnvss4vU1y6ZJnsAd1xqVwP7mTjacoFUX4Gdzz8U7nx3DiTOO6F6HeM4r5b0KoPm0Hc2nbfAHAlAqFKgZbkbNcBN/7lBYHx2bM/pxY37HRRKTR5YbI84dPlpg0Zxanu5+sagej/1lT8QpeNcvRT1oePo1qOM7Vujzp6y9aDnrwM7dx2F3egX1UF5uQnuHPWIN91x7Procbp4+VsyfhLqaUn4HZSnEmyScI9/zZKaCx/LcDPb7WDTdknFDJAXpMnlsLh/ufvKfES72VLWftzo8uPeZTyLOd8viejz60p6UnluQaDepBKtiYu1LW5+XDSMxaNUFmHfhGEz/TgV/7QJGhM05YBRo1Aps2vF11Osndr5HVs5kK4SSvUlHPUf4tRTQ6+LGWry16yjsTm+EHlh9Cl0rbrVU2MyruMkRgyUeyLhJHdF0S2Epgsgw6Xaxi5U3uzwDYRWHy5u+HiJRKowSqooRGhcgsSdMeUkhHK6wY4fLF7pxr9v0BTbuOIK3dh3D4rDQWvj1i9ovJs5qPLE1d9iEk7Mj+tkoAKs9sn/Pph1NuKihii11F+yDIyRraOZV7XBzMM8mUcNG7PgEkQCUUEwQmYZTkXLG6pAe7klgSjkAtrw5IolXE7w5D7Po0WX34HfPD4yJuPnKyThnuBlGbQH/XEnKEgvJSaYhORx9XnQ5PGwnaMbQ0KiUbFXSMIseqxbWC+qgvasPGpUSw0v1UdfBlauj28UO3jxnhBlDSwojrl9KE2BDXpiWNofgOUpN2tCcs+A16ra70WkXNoSgGCh1z7fkXGJwQZ4bgsgGQhUpMZ9Y4+gFIobFpMGK+ZN4noafzT0Xr3/wTShnYnLEmIinXzuAXQfP8M8lgyyxkJRkypHjQHOn4IiL5tM2uL0DU6vXv/IFll48jqeDxY212Ln7ONa/sj9mv5RwuTq6Xdj2YXPQsBG4fqn0zjHerZ27j0d4kO5YOgUtbU7eNeroccHe6xHt37OksRY1w82UnEvkNOS5IYgcIp5eIKL4gboxpXjgpunoDOVJGPQqlBWPR7FBIxqy8QcCvHMlLEsc3h6x/jNczwhPDpHBjUxTQO7U6r9//C1+edV5OHraBgSAtzj9WzpsbuE1KIP5JZ12F+674btoaumGy9PPJkKLetwEuhP3ur1o7XLBpFdHesTigPEiubv7WQ8SFMC555SiUKvCvRs+4V2jP247hEVzxmJxYy2rC8Y7N8Sih0FTwE5FBxDZg4lJ6HZ4YCnRweXqZ7sTW0xJhqUSJcUeRCL3IOOGIHIIWZqXKYDDx6Ik6SoUguENBISnXJcV63BRQxXrsXG4vJImppsM6ojJ2kIdfWM1oAvXiZDsbEdhjvHT0e3C0dM2bPugWVqfHU6VmcmgxmWc8RKMDplzCN5oQwM6T3X04qEXP49epRXtOGFwQ15MifqE6mJMHF2KM529gvvF7fXjvc9PsIbQpBoLRpQW8vrihF8vt7cfE6qLcfHM0Xj61QOoGmrExdNG4Y/bDiVfKZUMAsnRdyydgvJiHRk7gxgKSxFEDpFoXxQusZJ0ow145J6r2KTFMIsel3LGB2z7oBldIkMyuec2GdS4dOboiMnagp+LkWTK1cl7n58QTO6tGW7mvcbw3ucnJPfZsdo8bMnzRQ1VbNdirg4dLl/UUJ3Y+Ijm0zZ+KCyOkF/49ZpQXYw500bhwRd2o+WsQ3C/IADWENr2QTNMOuGbP1fesmId5s4ei6dDPX1+MnsMa9gwa9mw5SCsNhkHtUogXKcmgxon2x0pDZcS2Q95bojcIxMu6Cxxe0sJ08QipveH4y1hyp2ZEmHuucyFKqyYPxkPvrCbd4Nb/8p+0dAUc+65DTWCs6cSKUHn6qSj24Udnx3HbVdNwck2O3z9AVRWGGDUqfDIyplwuLyorDCyeTl2pxcVpYVY3FjLhpdGlhv5YZkQvCozkfCXrS9ySjp3XWK6Z6qT2NJxgRt2S5sDWk0Byop0/P3HuV5nuvpQqFWx14Qx9rgephXzJ2HzziYAiLl/uPJe1FCFY6097L9d7n7ZuhPzkPJd47xHrS7gDW8VMzzT2t5AJoTGLzAwYxgA0CgGAci4IXKLTEwDluucchhIEsI0sZBUucO0668swgiLHuOriwWnXAcCgbjCZKyXRcQ4SGg2UEgn9904DQe+6QACwLN/O4iObhfGVhZh8pgynLb2wlKkw3CLHsNLI8dFjLAY0O30wFKkg8vdj6ZTtmAOiVkDmyN4zUrMQU9Vq7WX1Vm4Dl0i4wiYdYnpXqlQwKBTs7OpuHlPZcU63oR1wbCLQQVvfwCBAH8kAreSa9RwE4aX6mE2qDF6aIOk/cPIazKoUT3MBJe7H0sax2Hn7uMo1BUI7yOTNuglibYnxb4LUr5rsSbDy7m3Mky0PjdcaBRDJGTcEDmFLAm1mTinnEZZkvOj4vL+xDhXsTGOEmcF0N7VhyWNtXB7/fKWRgcAo07Fy58ZW1mEi6eNwgPPfhqRE8Jbkz+0RoOa17l5mEWPhT+oxTNbB7rvLp83Ca+828SGszaG5dxo1SI3/NC6hHS/pLEW5SWFeP7vh3D4eDe06gKsvn4qexxuEjQwEHZhStu5crVae7GkcRxPBqaSa+3yGWyfHqn7x1yowh1Lp+BkuwPrNvFlfm/3Cfxs7rm8nJtlc8/Fi298icv/rSZqZ2Gx74KU75pYaG/ehWOwcccRKEVyxqi0fXBBxg2RU6R0GrDI06Qc58yEUSaKDN4fhngMJVuvF4+9vJdNxr3xxxPx3N++TDi8FkuWebPH4olN+yJyQngTq5UD3XWLTVr849NjbJLtqKEm/O/bh3mff2brQay+fio6elww69W4+kfj4ezzomFcBbRqJR768+cRYaBVC+sG1hUAqoYacfvSKfD1+1GoVaG1w4E3P/4Wl19Qg0ljgx1nN/7jK9x+1Xk42mrHkFI9b/8JhV2e2XoQc2fVYPO7Tdi5+3iE4bVi/qTEOgcHhCeXb9zRhFsX16OkSIsHbpqO0+1OaDUFeP2Db/B1Sw++PS2+t229Xry0/TCrZwB4afth3HPt+XC4+zF3dg2AYD5UR3cwFMg0VhQKQzEyMZPhS01aVFYYkwrdErkPGTdETpGyZmhRniblOGdKjbJESNL7wz2OVEOJW7L84luHgwmqURrfJSNLr7cfPXZhnbM5IWEztoZZ9Jj//bF4luOJYEYSMCXibm8/vjzayeasMIyvLoGzD2i19vLKsREASkwaXkil+ZQNT792gD3HdZdNwPRJw3mekaUXj4MfAXywtwXXXT6Rv/9Ewi6ModDR7cKbu47iNz/9Lnpd3qRHIojtXY26AEPMOpxoc2Ld5i8i/i62tx19XjROq+YZgEsvHoeTHU48sTFyHIS6QBnRVJIXhkLkZHi5jHcid6FqKSKnYNzkSxrHYdGcWixpHIc7lk5JuuFYtAoiORqwRatyYsYCnGpzJFfRITB1OqH3xHuuPl/ENG0h4m18lxAho21STTnKinWCOmcmVlvtA9VPAHBB/UjWsAH4Iwm4n2fLyjmvFRs07Pq4E8O3fdgMo25gn9h6vaxhw5zD5vTi5XeO8F57+Z0jON7qwAX1I/HiG1/yKsCYsEu4DFz92Z1eFGoKZBmJILZ3y8xadnJ6PBV8Wo0qIpn85XeO4MQZR4Tu50ytFmwquTH0N+ZcEd9HGuMw6CHPDZFzeHx+bH3/G56HJVlieVYingRDwxOlJgeLhW/au1283ImE83ASTMRMNDFa6DiVFQZ02tyi+pAUwoqzUiaa7plOzOETqy3moCflbGcf/5qLeESUoUdArboAP/vJuVAoBpKJw2+ssdYntM/8IknZ/kAAUER6g7RqJW6+cjLP+8Pk3DByrpg/CTptAZugnHB1nwJQKoCVCybjqVcPCK4r3go+p8srvt6w18aMLBJNWmfCUDzPTJZUNRKZJyPGzTXXXIPOzk6oVMHT/8d//AecTiceeughuN1u/OhHP8Jtt92WCdGILCdVuSsxQ0/cMI4COHw8TiNBIHyjVCpw5/p/yrKWRBMxEzmf2HGYhE5RfcQKYcltoPmBuppSrF0+I2Jita3Pi5Mis5jC/105xIRFc2qBAPDae1/jF4vqsXb5DDhd3og1xAqHCO0zsQRYpUIBfyDA8wYxf3t01ff45zGqUVkxhV2ns8+LO9Z9LJsRazKoMe/CMcGGixZ9RNVcPGEgsSR0IY9YmVkr2lSSG4aSXGlFDBrSHpYKBAI4duwYtm3bxv5v3LhxWL16NZ566im89dZbOHToED744IN0i5Za5AoHDHKiTldOgnhCTwlNqgYiXOWdIlOcBSdyKwGrw4Om0zZYnZ6Ib64UvTj6vJg7qwaL5tRi0ZxalBXrEtJdtF4tEfoQ2/ehTshcpOg1bt2LTKzudngiZjF9tO8kls/jz9xa3FiLP73xJTbvbMLmUDWS0+WDxawJXkd9cAwFuz5ED4cI7bOa4WbBvVcz3IyP9p0UbEpo1Kn45+kfWKe6QIGH/vx5fPtT4Dpxdd3R7cLGHUewbtMXA9eiT/q6w3WwamEdb01LLx6HIoNa8Psn9buZ8PeSyEvS7rn59ttvAQA33HADuru7sWjRItTW1qK6uhqVlZUAgCuuuALbt2/H7Nl5UrtPTxSywX3yZdr+K5WAQaeO3VsDiNoaP97EWC6JJAcLPcULTeS+ZVE9oAhg3aaBSdfhbe6ZbsEX1I9kDYiP9p0c8DwpgC6HB9s+bOYlbO747HjcydhiXi6urhh9nOroZff9MIsei+bU8sJE3O+BkF5NBjUc7v6BidYic69Y3Yeu75lv2qHXqvhhCc61N+jVsDu9vHCPUqHA2Er+/Kc//HUvm7TKrLP5ZA8cvR7UjSmNPsZCCLF9BkSEPZtP2TF7SiUUCuDWxedBoQCGlQokXoftaUefcNhHdH+K/D4VGdSixjf3usb9exYASowaXtL13z8O3hfuu3EaEPBDq1bB6fIG87kKVZK+m1mXtE9klLQbNzabDTNmzMB9990Hr9eLa6+9FsuWLUN5eTn7noqKCpw9ezbdoqWMrCoDznGYp7iXth/mVVxsfb859g9sLCNTYgWRXBVbQrkKQh1/mZAP97XwkmazXhVhOKyYPwlmQ7Cvia3XG5GUuWlHE1ZfPzXuShKxXi1v7jrK04dBp2ZnKAHBhF1uAm/49yBcr2XFOlw2czR+99xn7Hm4/V+451IoFIAySrgQ/Gs/zKJn83E2v9vEvtegKQDUBayhdPUlE3jHYyp47E4vHrhpemLfa5F9xn3N5vTyyq+ZdT6ycmaEYRO+p1ctrOM1G2Q+K7Y/xX6f1i6fIahrrVqFdZsj92g8v2dGvYY1tLnHNheq0NLmZI/PvYaxvpspq6QkcpK0GzfnnXcezjvvPPbfCxYswLp169DQ0MC+FggEgj9WcWCxGON6P9O2Oh2c+aZd8Imi19uPmqrStMmRKmLp0u8PoLXDiU5bH0rNhRhWZoBSmXhczlJqxNAyA1Y/FZmv8sTtF2JEhfBeONXmEPwRj/YZwfP7A7jtqil4/K8DicC3XTUFo0eWQKlUxLVeS6kRNSOL0WnvQ6mpENaePsG9IpRs2eVwY/xoCwCg5aw9wnDYsOUguzaxPahSKVFeFv93oaTYgKFlBlh7+lBWVIguuxt2Z9D9r1UXYOWCyXB7wrr1iiTsMt8Diz+A3/x0Kvrc/fD1+1Fk1LKGzcCaDuDnC+vw5Cv7eQbHhi0HcMfV57P5IXMbgl6BljYHxlQWIxAA79q3WnuxeWcTHlz5Pbi9PpSahK+TpdQIk1GDQ81WIMCfHC4WVuz19mP0yBK0djhh7emDTquCr78fRQad5L0v5TfD7w/gm5PdEXt6/Sv7ce8N0/Dlt1b4A4GQR6qY3Z9Sz+Xp9+O2q6bgxTe/xAX1I6FUAt8ZbYHH50/690zsO6RSqxL+jgod865rzodGq8aZHpcsvz3pJtr4BS7MKAavzw+1ioqggQwYN59//jm8Xi9mzJgBIGjIjBgxAu3t7ex72tvbUVFREddxrVYH/H5pj5/l5Sa0t9vjOn4y6LUqwScKvbogrXLwkKmqIKYuUxSSszuEbyxnrA5oFMIHPtPhjPszEYT0ptMosXb5DLi9Ppj1Gvj9Aez76iyKTVq0d/VJr4BSAC6XFx53P2ACDDrhvSKUbGk2aNDeEdT90dM9UdcmtgcNGs4elLonRKYwP/LzmTjVEZxF9b9vH8YV/3YOljSOYw0znUYZ/XugDBoMTBVQsJMxf02t1l4YdCpeSIMxONq7etmBnNweKkMtepSZtYLHsjvdqCozAAjAanUILBYo1BQEh0sa1Oz0c6VCgfJinWAo0FSoxhdNbTh8rBP+QPC1xmnV2PHZcVx9yQRJez/mb0boGrS02QWvu83pjqgotHY6BM8bbW8MK9Fh6cXjWa9fNO9ZvL9ntSNMEaGmE23JfUe5xyw1adHS5sRtj3+Q0XSAZB6kpY5fYHj01tmZu6dkgGi6TbuJZ7fb8Z//+Z9wu91wOBzYunUrbr/9dhw9ehTHjx9Hf38/3njjDcyaNSvdoqUMOfqkyEocE4eTJVVJfolMx056ojZXb89+hnuf+QR9rn60tDl5ujzZ7oDJELy2Udcbdh1u/cP7aO/qE9wrlUMMvNeYNveHT/TA1usVnf4c3vZfdA/GsSeEruljL++F1xfAuk1fYOOOI+x7t77/DTstvFCjws1XThaVwWrz8HrAaNRKwTWZDRp2Cvnmd5vQ0e2CVh3sXzNnanVED5WnXzsArUaV8LW3mDS4ZXEdLuNMP9/6/jdo6+rD1T8az5uIvrhxHE63O/HgC7uxcUfwNcawuaB+pOS9H+t6MdfAH4DgulrOOiR/56Kdy+aMDGdu2HIgIiF4SWMt2kNznSQj0Ism6e8o55h+f4ASjAcxaffcfP/738f+/fvxk5/8BH6/H0uXLsV5552Hhx9+GL/4xS/gdrsxe/ZsXHLJJekWLXXEWSqZatKZAyR7kl/Iu+Do82LVwjreE2WsFuvJTtQW0lvzaRv7hMy8tnFHE9sKP9p6xYyEiDJffTD/Q6zN/Z1XN7CVP1yPxc1XTua1/Y+2B6PtCQA8b47YNeVOzb6ooSqiMd0Lbx4WXltIBu7ny4p1qKwwYdnciXh228CIhiWNtdCqlILX0WLWoHKIUVA2p8ub+LX3AyPKDGxCN6ufTZG5UE+/diDitU07mnDL4nq0dfXy90I0T1mM68VcA6Gp3zdfORkvbT8coQPR71yUcwld61ZrL8yh0nB/IAAEgDdDeUjxzlsLX78cU+8ZKMF4cJORPje//OUv8ctf/pL32owZM/C3v/0tE+KkB4nJqukgnV960SQ/YwJJforIpNDV109FIBCQZjAmaWTG04CN+wQr9uQpdh067e6Bp9mQ3ABg7XEJtrkv1KoEK3/OGWaK6DMjtgfFZDll7eWNBQh6kYyC19Ri1g28LpJfI7Y2ALAUBT/PhJYe+8tetr/KkFI9Om19eHPXUYwebha+jiEjRCyptKrckNi1VwCnOnoF1yOUCyX0WstZO0rNwTBWsUEjLVwb5XpxuyEz112pRHDGlaaAzX8K14EoIucS+/4WFBTwPHQMkn9DoqxfrgdBSjAe3FDm0SCEKRte9INatucJ+6MrM/3+QPBpOyyk0u3wxN3vJ9y70GrtxYMv7IZBp5b+AyjgCpeKkMtcrBU+kyMTLQQZ1QUf1nfE4fKJhp5Moaddu9OLze8GQyGVFUYYddKfXcRkEQpv+Pv9gmEMi1kT8brQ2iL63oTW6vb4sHLBZF5oiemvsmHLAbg9ftidwcZ5YtdRNMRiUA94CYzauG6Y0cJ+o4aaUVas470Wnh81zKJH5RATOm0u3LL4PJiN6qTDtdx1MmMsKitMsJg0MGoLZAuDRwtZhf+GTKguhk6riuzFJNCjKer6k/iOSpWdB/Ugy0sUgUAggz4E+cjmhOKsI2xgoFYd2TdFKrF02XTahmf/dggXNVRBo1ZiZIUJL775JVqtvQNPa9VFkkYZnGh3Ys1zn0W8vqSxFkMtBpwz3AyjtiB1XjGRRFqPzy88isDujv7kKfb0Wl0UUdJ885WT8eY/v8WMScMjQhCTR5cACLn4xZ52YyULC8jChDe4fV4AYM2yaagqNwifjwkburzosnsiwoYalRLPvzFQfTNhVCkC/gDbdG6YRY/rLvsOHv7z5xHq+unlEzBqWLAdf7ExesKzx6/AGauD7RkTd0dpDifanVj/2v6IROUbfzwRW9//Bj+cVs2GZZg1MgnlQsM4V8yfhBKTFmuejdzL9/70u1ApFdKS/BVRrnm0v0VDaJ9gQJ+lJi38/gAcLi86bG48HRrJMMyix8If1OKZrWG/KWNLsf/ryN+aoaWFuOfpTyJOz9tbcoxQiKWHNPQgo4Ti1BFNt2TcDEJsfV7c/dSuCHdtIjk3sXRpdXpw74ZP4Pb2Y9EPaiN6Wwyz6COqMcR+XGwuH+5+8p8Rcs+dVYNtHzYH28NXGFNbDSH0Y4kEbyRhxxtqMUKjDMDWK3x95l04Bjt3H+dV7Mw8dwiM2hgeGqk/4GFrUxYocef/+zjxfRJ+PGWwc274ROhlc8/Fpp1HWCNqSeM4Xh4TENwnV108nlcCHu0mxN2Xye53Zt+FV0tp1Uq8+NZhaNUFuO/GaTBqCwaSo+0e7DnShsohJjasx13L8nmT8dCLuwWvcdQRFqkkyj4pLzOhvcPO/p35zjHyC323teoCrPnZdKz546cRrz9w03Tc/z+Rrz+66nuhPjfpaXgq52+hGGTcpI6sqpYiMk+qRhgIwQwv1KoLBPMwLqgfGVGNIeaeF3IzL26sxXt7TrC5DimvhhBymSfjRud8dkSFEQiIj0moHGJkQ08f7TuJCaNK0Gl3x3SlO1w+tLQ5MHd28JgmgxrrNn+B012uiJEI3HUkHd7gHk+vRlu3CxfUj4yoZnp22yHe5O2du49jxXx+VdWK+ZNZw4b5nNRrzez3smIdG0aZO7sGDpfEEJBBhd/eOA1Lfzgeo4abcPDrNmjUSrZpodvbDwSCLs8TbcEp6RazBpUVJrScjSzV/uG0anx7ugcr5k/GksZx7PTyZXPPxc7dx1FWrMPcWTVoabPDavekdro7h/BQkcmgRkubA9+ecaDpRBdaOpxoaQtVAoZ/l0VyrLrsLuHfGrtbcG9JqnCSce3p/C0k0gtNBc804W5gvSquadOJkO5Eu1HDzPjtsmno7/djW9h5lUrhH0XBxMRQQvDa5TOw50gb/P6BHidadTAcJUtitDJYlmy1uWAp0sFiGphJJEt/oGjHiDImYYRFj0dWzmRDPkwn46hPtwrg21Y7r+cJ02X3wDcd2PZBWGfnMNmGlxvwm59Ohdvrx1BLIZx9Pnx1sgeWIh3Ki7RAP4QJO057Vx+aT9tErzf3BmV3etFtd2HehWNQM8KMYqOWV0nF/VyHzR3zWjA5ZuEeo8oKI4aX6qNfPyWw/xt+WOWmeZPwyYFTrKdpmEUPq82NJ189wIbbxleXYsLoIpQV67D1/QGPRlmxDka9mlcBduOPJ8LZ54Wjz4MSkxZXfr8Wx1p74A8Af/jr3sjeOCkKpXBv9GXFuogw3OLGWnywtwWXzhzNm4zOIPTvUpNO8HWLOfi9ktrnhlthFtlpenLsUCVD2L6kpOP8hYybTCLwRY02e0cu5Cy3jEpYbk/QHT+JF5efMKo0vh+XQNAbVFlhEmyLn/QPU7R8pIAMN5VoNyZIH5PAzJ5i3iNWym/r9fJ6xzDHm3fhGPj9Af5n9eoI2ZixCjXDzZh27nDetVs+bxLqx5ZGGjgCa1zSWIs9X53BlRfVCl5vbgL2Ys4oB606WDU1d3aN4Oe+OdkTM4xjLlQJjrVY/8r+mOEHq80T0fn5f7YexK2Lz8PnX7VDqy7AdZdNxItvfhlhPN28YDIm15SwYx7c3n7MmVrNGjbM8Z7725eYd+EYFGoL0Pjdal6H3cWNtXhp+2HcfXWD7NPdw+He6C9qqIrwsG0KtTjYtKMJi+aM5ZWgM0NHw3NuKkq1vPUzrzPT2cMrtGIZG9y1lxXr0DitWpqRD4jmzKXlt5BIO5Rzk0HC471iceuUzKBKNOEwjGi6tDo8uPeZTyLyDW5dch5cbl9yyZ4h+U9Zgx1xd+4+ziZ0JmMMCsmsVReE5uwosevQWbbU973PT8Tu7RH2pKhUAE9vOYDLL6iBy90Po14Na08fKkr0GFFuxOkOB373/L8iDrNm2bRQJ13xxGqhZMxuh1swcfXWxefhrV3f4rzaIYACqBtTBlOhCneuF85pmnhOKc84YP72wE3ToVMpeetr63bhm5M92Ln7OOvdYI6zr+ksGr9bjef+9iXveldWGHDa2otvT9nw3p4T6Oh28b4PQp4ExvDinoO5FuH78kSHU1APv772fAwt1Ys+8TedtgkmN9997flQFyjxzckeqAoU8Hj9gvlkzF7XaVWwOdzw9gfw6Et7Io5359UNMOs1WPunfwnqv7aqGOcMCY4f4F5/Zngscw2HleiiJ5JH8wxzbv5zZ9dg886mCDkXzanF5p1N+MWieuz87BiW/HD8QCsGoxrWnpDH06xjDRieJ5T7uhAxvFLctcf7eymWX/Poqu/B7w+krAdZOkf9iI1fcHt8sPX0pU2OdBFNt+S5SRcCoYiIeK9I3FqW/jOCDbNS23dHKJTQau1Ft92N2uHm4Av+gd4zHTY3dFqVeE5HxA+1GmZ9EUZY9BhfXSzLD5NY+MPW64G91ysY3oln2vIvr6rHxTNG83rHLGmsxYatB2B3enH70ikxPVnRnm7DzyfWKt/R5+FVXm37oBk3XzkZJoMa7u6B9zIhI5vINO5OmwsnzjjYGUZFBjVee/8b2J1eVj8d3S72OF+39KDLHvQcjRlZhDLzQFn20FI9Xnzz/9ib9ZASPXtObj+XUcNNKDbq8Kc3DvEqudjvil6NU20OnOlwsjd0RahkP1wP356y4fG/7hM1ipneO+GfKzPr4OzzYuOOI7j20vER4TbGq8AkzTIei/LSQsHjVZQUwikyzVupBHScpHHm+oePmogIMSqBL49185L1V8yfhM07m3jVipUVBnTa3Cg2aTGhOvhddLqDxwuXE4Hg/7d39eHS752DyvJgWM/W68WJM0F9144wB8/PGDB+wGLUsENeo1ZkKoChFj0bCi0v0vLy2Hh7P87fy3j7SmUD8SYUi/HorbNlkCa3oITidCDS2r409MPJRejfScd/0zhugYtFZH0Wsy7ivac6evHoS3tw74ZduPP/fRwpn9gaIE9PjFgyGwrVguGdOVOr45q2fLzVEXGcjTuacFFDFdzefrzwxpe48ccToybxivXvUCoVEecTapW/Yv4k+P0BwTEFc6ZWR6wdAcBs1AjqxVioYUcsbH3/G7g8/bhs5mhWP0yiMDf0ZHd6MaLcgHOGGvk9akKTzZlxBu3dfbxzMv1cjp2244FnP8XMScMjeswwBt6tf3if3SdfHuvGxn98hcVh/Za4yehiycm8hHiO/ixmDZvLo9OoMLayhCerUFhnw5aDaDrejeXz+MdbevE4/OHlvawBFq7jUcOK2JJs7vUXGjXBrkMBtLT3CoxOOBichcV5/65DZwe+U8d7gj1sSnSCyfsffXESSy8eh527j2P9K/thc3rl+20JhYRXP70Lv/2fT/HYS3twtNXOO5bQ3g/Xl9j3MenRDkROQcZNGhCLkYc3Q/to38mIH1I5ZlClar5TLERvDMWaiAZ1seRj1mAyqLHoB8Fql5Y2BxwuX1pkFvNcVA4xRl6fAqDN7sYpay/mzq7h3YBjdTRutfbC6RqolrrvxmkD+TiMzvp87BP2mmXT8MjKmZhQVSQ4pbrV2ouSUOIm8966mlKMKBceUzCkVM9b+5KQAfDR3hYsD9PL8vmT8Nd3DkcYahWlet66mBu4Vq0MVirNqkGJSRNZ6u/08vJbdu4+HtEAkmuQbAwZl8zfxAy89a/sx6QxFazn5xeL6jF3Vg1vwjfzxB+BH6irKcXa5TPw62vPx9rlM9h+UEwuz4tvHsbbu77lVXiJJU67PP145d0m/HbZNNxz3VT89Irv4O8ff4tWa6+gIbps7rnQqZUwFqoirv+YkUWingtbrxeHj3VG3WvMv5kwK+97FwAqKwyYd+EYXH3JeNyyuB4KBTB7SiX8/gDrjeuwuWX7bRHKb9qw5SCsNs51CQx4eifXlEboK9rvZdbN+CNSCoWl0kC0csOIVuMGtewzqDI2Y8UP1I0pxW9vnIaznb3Qagrw4d4WAOAlGIqFQ7jydTs8ohOfJ48ukc+VzLmZWW0umPQavPT2/2HS2ArBcMIIS1i1TQHwxdedvMRKbnhGKRIeYY6hVRegz9WPze82QasuwMyJQwBIqxARC1cZdeqBEKQCcPT5oNepsKSxFv5AMHeIqTjrsrvYEQ5atRJKpQIrr6yDo8+Lt//5LW5ZXA+3px8VpXooFH4cPt7NU5/b2w+Xu5899/jqEky+cRpef/9rfP5VO/v6zHOHRKieucZzG2rYG/Cug6dx343T4Oj14NtTtgiDZMzIIqxZNk202obJSRlq0eOi86vw3ucncNn3RkOpVOCS6aMwaqgJvgDQ2+eFIaSfiL0UANQFCrbj84k2J6v3QMhY/fyrdvQ4PbhlcT1cnn4MLzPwqqSYdWvVSrRae7H/647QoM1a9u9cQ5QN0RaqYNSr8OXR7oheUCPKxUdNdDs87FBNsb3G/Lt6qAmL5tSy+4D53nXa3YIjFhbNqR1Yj2bg+Nz8H6e7X9pvFyfUrCxQCP4OdNhcsJg1/FwhfXA/Dy/VS/+9DCQ3foXILci4SQNRKwACYRUDAhUEKT1/irE5vPiP5z5jz73oB7URT2fMsEHuD6lQnonYxGfZE65DOQLqAgWbgNje44oYUChUVdHW7WYNG0ZGpspk24fNKDKosfTicexQSW5ibPh/s5OZJVaIxKyCUwDNp+042e7AxrAS3x2fHcfixnHoc3kj1lhq1OCRUAfhw8f3sNcnmGQdua+6HS52LU+9th92pxc/m3suepwenDjjEK1GKS3S4bKZo3myLWmsDd7gdSo8/td9ArkvWtFqG6Ek5KUXj4NOo8LGf3wJk0Edcb6I3JtQOPSl7YcjqqHCDYyvW3rw6Et7wCSphl+LJY210GoKMCxkEHP3BmPM8gzR0Plb2iLDS+s2fyF4Dla3CgU+2ncyYs8yOTeM/pY01uJPb3zJ5kjt+Ow4O/5DLE+JybtZMX8Svj3VIy3/RwiRqrrwJPGAPxiuCs8VYo4d1+9lvO8nchYybtJA2kqvs/D8UpOmucMYheQzF6pEJz7L7oEKPU2e6erD3Nk17BMtE9Y4Z4QZQ0sKI0YOdDs8cPn6BWUcNdyER1bORHu3C8+/8SU76PA7o0tRbNRg9HAzKkoN8Af6Ma66FD0OdzCcpeDrUCiXg1sGHGvyt9AU8007mrDmZ9OxbtM+ePv9vOGbTLKp0JrcXl/Evlq5YDIKlArMu3AM7yb1x22HcP+y6dBrlKL7zt/vZw0NINhEzu31o7WrD0adGrdfdR7+EDJwxPYwd68L6erld46wU7vnNtSw52O8Di1tdlSUFAZ7GwUGwqFMCbRUA8NYqEJ5aaHg5OzbrpqCZ/92kD0OE7pjZ2A5+dV13PAS1zti54Qnu50eGHTqYFVMnw9mQzBkdqy1B7csrscZqxNjRhajskKP0UMb0GFz45uTPbxrxG05YOv1YsOWAxHG0c8X1qHUpMGU8eV4YmNwv1x32QQUG3V4YtM+0X0phFC4fOOOJty6+Dw8/8Yh2J1eLJt7LrZ+8DVOnHGwRqCUYxMEGTfpINPu0AyeX8xrJBTeiSpfIPrEZ9kQeJrkhpW2fdg88KMaiHz/ksZxgjKWmnXBJ0a9Gndf3RCxTmOZCiUlBry/tyWiJ8iYyqKBY8aqEInyZBoMVQjn/HTZXWi19gIANr87UAI8vro4argrIixgUOPrU/aIcIbb2w+b042hRWbRfReridzSi8fjvhu/C5/PL76HQ3v9idsvxLHWHsG1slO7Q7oMP9fW9we8DqxMUaacC4WWDx/vQUubHRt3RJZTn2yz8zwTk8eUYebEIYJtEW6+cjI0amV070h1EU519OKhFwfmc4X3y1q1sC5Y2dQf3B/dDo/gNQqEDLFuhwet1l7epHkEgFKTBj0OL1raOtFq7UVZsQ5enx8nztrifvAQC5efOGvD0h+Oh0mvxuZ3m/B1Sw97vaQemyAooThdBOSt6smV85sLVbhj6RQsaRyHRXNqYdCpcPOV/Nb6qxbWwWxQw2xQw6BT40xXH6wOT8TuTEdCoNDTJFP1I3S+8PcLJcEunzcJFcXa4AfCrwMAp7cfZ3pc+L9jVsGESmefD3eESsSZY3KRauBZSnSYOLpU8POlZuEqsWKDJrreA4BZr2bzPGy9PnacQPixhKrkWBSAoVCNJY3B8Qg/mjFKwOvyFQ41d4ZkEhgMykm4HVZmwFBOcjRXDu7Ubq26QNQbZuv18ipsRPUedk1tTi9e2n4YlUNM7HqYpHKtugC+/gBPj8NLdOznwvfe068dQIFSicWNtaLVUVabh/e5C+pHRuwjprKJgVkXdyTFksZxKDVpeX/v6HZh87tN2LyzCds+bIZWreKt7aeXT8TL7xxh83sE9SOCWPWS3w88s/Ugvm7pYQ0boVwhqnIiokGem3xGjlEBMuDx+Xn9Ye5YOgX3/vS7+PKoFX4/8PI7X2H5Tyahy+GOPqk8DR4osafJc0aY2S6+4d4Q7vs7ul14c9dR3HvDd2Hv9aDUrAsaNvxDBlEEk1M7elz447ZDuOknkwTPfbrdCVOhGr9bPgO2Xg/W/Gw62rp6oVYp8fcPm3H5v9XE1kMB8PXRHryysyki1HDzlZNRUayNGrrk6p0X/tCrBJsw3rK4Dus28fur8Jq3CYxnYCZpB98/WdTrEjFyAZHetpULJuOcEWb8fGFdxLBNjSroCXnv8xNY0lgbCrEJex2qyg24ZVE9Xtp+mNWbyaDGnKnVqBxiBBSKgSRkRXCOV6fDg8Zp1bxeRkw+y6I5tageahroy8QJQ6lD3hluQq3JoMaoYWYca+0RDctG9GbieJm4YSxbnxfKAmVwwGfooSM8/2pEuQElRg3MRuH94Ovv562N0d97n5+QlJPGRShcvrixFp8cPI25s2owNOSB+mjfSbZ5IBBpXBOEEGTc5Cspmj8TL0KekMde3htKIB5w2fe6+wW9FmuXzxho/gWkPCFQLAQztKRQ0OMl9H670wuTToURJYXBF4QMGwR10+vuxx+3HYLb249CXYHgufU6FU62O/D8G5Et/lfMn4QJo4rEZzyFaOt245mQfplQg1IJ1I0tx5CQ8RXVcAx5aLjhD6062CRQcBTAz2eyFWcRXWlFEkmZG7vb2x+srhPQhU4TOXJhRJk+QoanXg0mqRcZ1Fg0Zyy8Pj9qq0owaqgB8INdp0mvCRnfkZVNjFdmQlUR7r66AQ6XFw/ePBMtbQO9itjvVXURmk8Fk7WHlBoERxes+dl0DCkJ6rqYmZN0XDyhtqw4mGDNJI+LhTwtZuFGgxFhrNDxR5YbUTPchPJiHWtQMnKuf2U/m/x+x9IprJ6YifVWu4e3NsZjw81JUyqBhnEVbN6SKIHIWXGfHDzNay7JeHYnji7G6KGR4VyCEIOMm3SRZi9KqubPxCRsnd0i/WH8YVM/+tw+0adSnnGTQlmDXZvjS75OJlm72+HhrXvr+99g2dxz8WzI2NGqg31OumwubOTM9YlpAAqsq5PzdM+EGgCgaqgJhZoCXpO4oBcisgsbt9cQU67tdHlRNdTIjnEAgqXl3Q4PqsoMQbkU4CXJIsCf/MwkDl/VOB5nu3rx3ucnsHP3cSybO5E3YHJJYy2qh5nx1Gv72fWv2/wF7ry6QXSPvfDmYV410gM3TYfL5UOxSYuqckNoUKkXK+ZPxoYtB4SvIWNQ69Ww2j04Y3XyEs3Xbf4Ca5fPYJO1F/5grKA8bo8P6NdG6JN7PTeG5n5t3HEEV/zbOehz92Pu7BoAwJ6vzmBJY21EdZfFrOHtwYNft+HmBZNxpqM3Yr8wxy8v1ol6KRnPz2Mv78UjK2eiqsyA8nIj2tvtcLr4XZS5HhsmJ+2WRfWxDRvOXuPOihPa49wZYIOtymn1DdNlOY7X52dHFeTrKIZwyLhJBxnwomSkt43AOsXa/yvDbp6FOpXoU2m6ZGWuSVyhL6mhMoFJ48UmLXo9/bxSYuAYbl18HvwBPxQKBV7/4BucN24I76bDJcIAFFlXWYnw030gANz95D9xx9Ip8Pj8UfeoWK+hn809F6/9f1+zZbpLGmvZ3A1BeRbX88ImQtOnd3x2HI5eLy+Z9c1dR3H5v41mwyxA8OZaqBXeO0y5NfNeJsS3bvMXbHjU6fbh6dcOwGRQY96FYzCywoCR5QJ5aTESza02F5us7fH5hRPfw4x05jvKDR0BwIRRxXho5Qy4PH4cOd4FINjgs3FaNdv3x+fr54W1igxqrF0+A26vD112D15+5yssuKhW1OhzuIJ9fcT0xryXCQF6AgpoCgCDTh3RH2nHZ8exdvkMOF1eFBs0UCoVwV5AsR7iOEZ45RAjHlk5E2e6+oRDs529cMeaRB92TNHzZ0m4XgpyjV/gMlhGMZBxkwYy4UXJRG8boXUyXVfDG5AxeQ/Ma3pNQdTpwemQlXtNZO2dITZpfEwpuu1B44AJTZ0444Db64Ov3896LRrGD+UltUYzAMXW9V+//LeIqc3L5p6L1z8I5kIJlYiH71GxXkN/3HaIV6a7cUcTzhtbJipPy1kHuw6x6dN3Xd2AZ/92iK3gYtY6tNTIm5q9pLEWZoNaMHeDmRLPbZCo1RSw5+Gu2d3dz4a61i6fEdk9WSTRnAnhWMw6HGu1s7k8Qr2MTnU4Ya4qZvczM74hPNRYM/w82Jxe3neGMfhmT6kM5syE2gSEh7VWXz+V/dzZLvHQXtAAiiz1ZvTGvJcbAgyfS8XIdPUlE2AxaWAxRc42E32IEzHCxRoTHjttxxMbvxCfRB/lmEJ9izIdridSDxk3aSATXpSU9rbhPPl4AgpolGDLR8PXye26yi2VtTm9uPPqBhRqVTCFmrRVDTGI52mEnTfZJy5ZromAN0bIEBNrK792+QxUVRhgKdbh/mXT0BNa12vvNeHYGTvrtTDpVeyNRagpm6VIA5sjqBefX7jUu9vuxY7PjuGWxfXskMvXP/iGrUYRKxHn6sNcqMLICuGkVrEyXSE979x9HDdfORlPv3ZA1BvlcHlx9Y/G85KSVy2sw4tvfhkRZjlvbBnrPWOmxL8V6inDbYrIGHMMYmsWCoWK7RelEli5YDJsvR5MHmtBiUmL5//+Jfz+gGCPG24IUakArrtsImusMcc82moX7EUUHBpqDo6JUCgiwntubz+vJ46QkbVs7kRUDjHhiY37eKXewZ5LFjz92n62WzWjOyAYOjzb2YcFF9WyocNNO5qC62F6AvVJf4gTM8KF+gYxBpfb249nth7EAzdNR4VJi3CkPERmLFxPpB0ybtJARjoEp6qyKMqTD/MkekH9SEABFGqDYQ+Xxw9joSKY44DIp032ySna9GCZn7iSviZi3piaUrb5G2OEddqFJ41bbS5YTBr0+/zwePoxengRXG4vDjYHb1BMXoxWXYDHbvk33H7VFNj7vFjzs+mwOT0oNWuhVSuDPVXOOrBz93HMmVrNJpMyoQ6lQoH+/n4cPt6Nw8f3sBUoF9SPxHnjgmMQCrXCycw8fQSAISJTrcXKdMUSrnsc7uDNephJ8HjddjdGDTPjrmsaoNMEDWCb08Pz5DB6dLiCJc7dDg8qK4wYYdFjXFUxTnc4UFGix1U/HI+yIh02/uOrgZ4pgOgoDKFQqNh+YQwCxptx8/zJ+I/l02HtceE//3dPxHG4hlOnzY2TbXZeiO6ihiqUFxcK7hetRoljp20DydSc8B4rk6YASxrHsTltH+8/hWsvHY9ikw4tZx3YtLMpYmI7s8+uvmQ8LqgfCa1GiXFVpfj6ZBcuOr8K+46cjUjyHTA4fLD1KkSrvcQeGKJN6GZ+t0539uLYaXvEyI1Om0vQuJHywJKxUTRE2iHjJg0oFYhIBFzSWAulMsVjuVNQWRT1ycegZpuHMa3tw0tDKysMCT05yf3Elew1EfPGPHjzTJyx9vKMsHuuE847sph1EQbbHUunCHrcDJoCGNQF7I1xSJFWMAfkk4Oncf1lE+Dy9PNLfBfWYZhFj1ZrL/YdOYv53x/LS1xetWAyrv7ReLz09ldRPX3MYFGuUbd83iS88q5wma6QB3FJYy3+/vG3bEVQ+HW47rIJ8Pr8ESMmuF2sGYZZ9Oiye/C75/nvnTl5OHpdPjz2l6BXhGls9+3pAblHDzPhpp+ci/95fUAPP5t7LnRafu8VQHgdy+aeyxo2zB54essBrF0+A+XFwkYg13AqNmnh6w9EVDbNnV0j+Nlx1aX474172XNxw3tA0DhSKpTY+j5/T1cNMWPtn/7FOx537ANzfI/Xj4++OIn53x+L3z3/GXuM266aEuFd2rQj6EUM173Q+AShBwYpI2ncRTo8sfGLiPeUiuThSXlgyeQoGiK9KAKBQF5EGq1WB/x+aUspLzehvd2eYokGONHuxPrX9g8kDQaA9/acwKoFdagqM6RNDknECP2caHdizXOfRXxs7fLp0KpVwZLOQHDo4uadX0f8iNx34zTcu2FXxOfXLJsWVRdi5431ObG1+fwBbNh6ABc1VEGjVmJYmQFtXb2orSzBsBJdzCREq82F3z3/L7Zct6JUD4/Xj6EWPZpOdMLp6mcTLodZ9Fj8w3F4+tWBapzl8ydh9DAz/vN/P4/IK3l01ffg9weietxsfV527hX3s3Nn1UCrEdb96uun4sEXdrN5IuF/X9JYiz53v3ApL2f9pUW6oOepJxiO02kK0OP0wu3xwazXwO3xwajXDOwd5rNODxRKBf7f5i/Ymx8QvCn/YlE9vvy2Mzi3SET+B26aDrvTg+ZTNuzcfRx2p5ddU/h7n7j9QmiUAfa8vJ4yoX87+rx48e3D+MnsMXB5+qHTFOD1D77Bv188HlXlBvazTF+fIrMWLlc/OnpcKDJp0e/rx33/82nENrvrmgaUFxei2+7CY3/ZxxpXN82bBI+3H6VmHatbZtaX2zvQC0osybqywojfv7Cbp7erL5nAlqUvaRzHC2cxurjj6gb8/k//ipCTMSq16gKsXFAHQ6EKhRoVbxYc933h/Pq6qXicU0rOnI+p9kok54b3XoEhtAnl3FQX8ar1wnsqpTrnhqlSSoRUJRSn8/6XSqLpljw3aaDYpIXd6eW1tc/KpwUJPzhCTz7M0/P6Vwae4MSasLk8voSenJJ+4hIYk2B3evHenhO4dOZoXsM1KUmIP19YhwnVxbjgvJFwe/ojGrZ9tO8kLp05Gm/tOhpsU2/WYu3yGWjr7oMCCnZeDjc8wOio0+7mdTAW+tEVK7HXqJXsf4f/LRAIRK1I6fMMhMLGV5fErMCaUF3EhhhjDaFkeuQolArYOZ1ygWCYSqcuwLYPggbXojnCVT5fNLVDoy7AB3tbcPUlE3DOMBM67cJzrzrtfRjKjLwQG0qrUODEGQcefWkgfKRVF0ClUkas97rLJsB72o6X3/mKtwcYbxj380zy64r5k/Dwz7+HLrsbNqcHD3P6AzEhzJoRJhQWqtDDCZdwe8YMsehx1tqLHZ8dx4r5kyP0ds4wExt69vVH5hCZDGooFcKJ6JVDgtPAEQAsZg2MhRp8+uWZiGOITRc3FqoFdR8+qV3QaJASNu8H6seW4oGbpqPT5oreEFPsmAIjLW5ZVI9HV30PnXY39czJY2j8QhpIx9gAORAL/dh6B25GQmtZMX9yxNRipgkbF626AGUmLW8cw5LGcbhj6ZSYukhWh2JjEsRa2tt6vWxX19Ndrgi9PPnKfvz7j74Dm9PLG/bIuOwvqB/JG9tg1KmhLlBg3aYv8J8vfY6vW3rY917UUMXTEc9g444VcPnYxF2DTi2o31FDzfjOaIvg33RaFbodHliKhMcjmPRqLPpBLZY01sKgU7PnEtsXVrsHLW0OzJ1dg59ePjFCD9y9wxxj6/tfY9ncc8P2zySUhzokc18Pl8/vD4ZTrv7RBFh7+tDn6YdCASxpHMeON2DeW2oqFNehMvjvbocb91w3NTilO/S5xY21aDrRHbFem9PLGjbcPXDTvEk8mRc31uK9PSfYMKXP54epUM16V5jPbthyEF0OL6w2Dw41W6FQ8NfM9Iw5a+3Ftg+bsfAHtSjgvIfZ/0adih39UCZwXedMrcaz2w5hcdhIkBXzJ6GtK2iUffTFydA0chUmjIocz/HRvpP4hch3T/A7btZKG/MiZSRMP1Bh0mL8iKJgnk20MnCBYwqNtFi3+Qv4/YHMjcIh0gJ5btJBGsYGyIGkZDsFMNSix+qfToXb04+yokLYnJFPz0JN2Jipx6c6ennjGG5ZVB9buJAOH131Pdj6fHC5fSgrkt4DR2hMwq6Dp3HNj74DfyDYJI0JI7m9wSTVUx3B3Jm5s2sE9eLoFR9EyVQBKZXAivmTYOvzwO31C75XqQyGGCJa+kPck+b2+CIqp667bAIcbi+e+/shwRLfI8c78fePj+LKC8dE5LksvXg8igwavPXPoKeJNzxSxEt0tquPvY5MG/7w94RXTX3d0oMiw2msvn4qbL0elBUVsl17me+Iw+VFZYUxohSaqZhpOWtnwymLG2vxwd4WXDZzNFuRdMuiegwrM8BqdQh6nVbMnwSb04O/f/wt7E4vbvzxRDhdXvS5gt2bLzq/KmItWo1yoOcOd694+rF2xQy0d/cJJr9abQP/zcVkUOPEWQfbOHCYRR/RwHHlgjoYdAV44GfTUVGqBfwQriYMhf0cfd6ItgtDSvWCAzBtTjdefPMwq48ClRIn2pwoLdJh1aI6rN88cIwfTquGQafCIz+fGQztML9fQOoqMmUi/HvPJG2f6eoDFIqs7nFDJAcZN+kixWMD5CBm6EegQuhnc8/FMIFp3XanF+cMjzToxJ6kpCYGt7Q5E6qYCl9bWbEOMycNx+9DiZbcG6jd6YVWrcK6zQO5HEJ6Mek1ohU3CAy4/l96+zAuqB8JpVL4vQ3jKzDUYoho6S80VoDRlVGvwY7PjvNuWC5PP1588zDcXv6IBa4MFzVU4YU3Dwe7DIc+q1Qo4Pf78eSrB3j9aphzqVXClVQnzzrY18RCF4qQocbo32RQ4zvnlEUkC7Phq9B3ZHipnteWnzEaGA8Oow8mMXbjjibcurgeOq0Ko4Ya2MRw4d5LBzHvwjFs2PC5v33JS64Nv6ZlxToYCzXY+I9DvL2y47Pj8PsBtVKB8uJCweRXiznYjyZcN3OmVrOGDRBsmbDl//satyyuxxmrEzUjivHM1gNsFRbTGyoiV4QTGmSMpF9fNxXfnuqG2+NHl70PWnUBryqKyc0K1weTJ3P7VedhcWMtXJ5+Xhn76uunBiseQ12bgex/aON+74VymajHTf5CCcX5BucpTqtRBbuGGiX2hImRc2N1eHDvM59E/IDf+e8N6PP4Ihr1sT8aYYm8awWSG6UkBosl0cY0jELnZ3qgMCXTQsmX8y4cg8oKIwyFKqx5NpjALPSjuGphHd7657dszk14tY/L048Skw5d9j68tesYLjq/Cu99fkIwL6WywoA71/8zonx7bGURm7T8oxmjUGzUoVBXgGKTFkNLdfjy226ezlfMn4wnNu2LWP6Sxlpo1AX45OBp/GT2GBw/Y0ehtgB+fwBub9BSKNQW4MU3D7MDCpkn3NHDzVCrFDjV7uT1S7nxxxOx+d0m1kvBTJd+7m9f8gyA3V+24oYrzoXT5YVCocCxVht7nPBrCCDqQMxhFj2uu2wiTrbZ4fH5We8JI/OiObXY9kEz1i6fgfGjLWhvt4smojPvXTRnLP737a9wy6J6vPyPr2B3eiO6NYsl6t521RS0Wh0YPcyMqgqDeGsARD4U/GJRHR77y15Wd8x1HzXMBLNBg9899y/BvblxxxHea2uXzxD8Tt597fl45M+fC+ZChed5AcD1l0/AC28cZj/PNfa4+2jmuUODye7MddKreMm6Eb8zEntByQ43iV2hwIYtB3BB/UjBRPpU97hJJqE4FXh9fqhVyrwYw0AJxYOFkHHy0vbDEV1PmZtop93NVn/wKlqAmOGziOnDCD759fR6MKREJ9ioz9HnRZfDw96ExYb/CSYGS5xT1e30BL1CQlVeAgbbygWToyZDnjPUCFsfP/FZq1bi7mvPh9fnR6lZC58v2DemvceFy2aOxi2L6+H1+jG8woATZ+ysB0WrDlYhAQMTw29ZXI+Ws3ZMGFWKynI9Tnf0BvNcCtU84+DX156PYRY9LpkxKqLbbWdPIYaVFfI8N8xTerhu62vL8eIbX2LGpOFYt2kg+ZepSGJuxBOqi4GAsDF3/WUTsGjO2KAxFAjOAuMmBnd0u9Dn5o9LYIYgMjdfrTo4gVyoF0prZx/aunpRVlyITrsb5cWFGFFWiAmjivDATdPh6PPC2efjdSdmvCeMl4wZt2Dr9eBUmwNnOpww6MVHDLi9/Sgv0eOnl38HXl8/fr6gDn5/ABazDma9it3PXp9wOPFUux1eXyC4d/1AXU2paBPKujEDibEWs44dGSE0zmLlAmEdhc9jY8JeQmEXj7cf/3HTdLi9PhRq1bivahpcHh+MhWo8sXEfz7DRqgtQWWHG2MoiNhdMqRw43mUzR2N4uRFA8AHD2edDy1k7fP0BVA4x4KW3v+J5mNiHmpCnd/POYA7asVYbxlWVoHqoIXbuTDIIfOdXLayDXqeKHXbPMlJRLcWQ72MYyLjJIxgXvNDwuXWbv+C5nrmt03lu2SjhMyYRNfxGUahRhRISQ5/jtIUPLztmEnnDvRcR7uw45lQVG8TbvguFJZ569QB+e+M0wWOVmbW8/iwvbT+MS2aMwp/fGqiSWdJYi+FlBrZS5sW3Bp54166YgT+G+qYw59u4owmL5owFEAzXHW+1s4McH131PXQ5gvk4G3fwu+/+z+sHcdO8yXj4xd0Rx5t34RhYigp5uh1bWcQb5cAYLRXFWiz54Xg2FDS3oSYi+XfD/9/elwZGUaVdn16q9+7sK1lYQzAEIhBZRsQlERExEiAJiDur48Dr+zpuozM64zjj8o3jNoo7IyKggBlA0SCCKAyyCjJAQoAskD3p9N5d3V3fj+qqVHVVJ0EQnHjPr6S7695bt+6teupZzll3GA/dPgYfbTkuK4nw3qajmH71YKzZEiodnpErYb/NTI7CXwRjLbkuS9LOa2sPyXogNBTrSRJWnd1XMhJKBfDS6u8l64gLSd0/exSWbzrCeyNS4gxwevxY+rdtvLcnnJdHKM1wtsUJADDqKbyx/rDkIW3RU7B55Cv8+iWYoVKga+1GIqFUAEdPS9fnA3NGofqsTTJH//iYlUX4Z2hdcf2F67FxYa/uwi4ss3XXeT0wZxRmT87Gq2H5TG+VH8bcKcPw3Ip90FIqZGfGIiXOgGkTB4L2M/jbyv2yXqCywixcPzYTyz89KgplWvQU2mw+rNlSKXnR+vWskRgeMqR/Csjt+Vc++h5PLRzf+xcrgv96kGqpi4UIVS8XEnzyXARKe+7Nj3swTMxLk1RDdQeOwI2rkOBybgxalahqSXRzCRsLl8j76J35eOi2MfjjgnGItWhgc4vnJJJO1b0zR4r6X1KSB6VSEbHKK1KS9In6Dtw3S9oWfx4hL9b/zh4lCqNwxsWpBjsWFY+QHO9007L9MYy4mob73OZmw3lqlUJyXEObC15aXi09yDCwOX14YM4ofgyjs5Ox9qsqFF01CCUFWWxoYUslHC4/VColZl03BA/OHY2UeKNsm5W1Hbh72nAM7GeR/T4hWo+ywizcP3sUNn57Chu+Ocn3taQ0D1qNQlTxpFTKr8OUeINo3u6dORI1jTaJwfXKmu9R1+Tsdk0zTBCTRqXzuVLzb8nlH9zcHK7ZUomnFo3HA7eOxvSrB/O/LS3MwpY9NQgyDN785AeWWTts/QDylXr3zhyBfgkGDEo19/iQjlRtlhCtizjXqfEmydoalGqRfMYpgmsplaxR+vq6w6Lzen7lfhi0atEa4egKPL4uwysjyYD7ZuWJqsSuHZ0huUarKiqRGGsQjd3q9AFgPb1c1aDwmFc/+r7X95wfg0h73umh/yuqVgkuDIjn5mLgIom1cclzgHxyp7Av4QOj125Zgeu91eZBlFGL+Ggd1GBEbYffXMKTMyfkpooSSuW8SJF0qqx2Nx6+Ywx8dBCxZi3iLBrUcg9AAbjzipQk7XQHMKifpsdkyM4IoTC1SgF/IChNmHbLv+UPSovCnMlD8c33Z0T5M26vH2YjhbSkLhkCLscmLkoPs14j255SocCpszb0SzDi+SVXoq3TA3+AwaqK43yuBBdSqD5r55NXtZQK94cMovA2g0HA6aGRHGsQyWgAbDlwUpwBGUkm/O3D/Whoc2FIehQyU8zw+gIwGSiY9BokRen5OTHqKKzfJs1xsBg1mDN5KOKj9fDRQSTE6KBWyRsvWo0SJddlISXOgLLCodiyp0bEfpsSZ0B8lB79U8yIs+jg9NCitcblsvjoIPolGlHf7MC1YzIABryRE64eLlw/XKlwxHBtL/ZvpIdtY4cbBp28orlBp5b2B0g/C3aNLRJ/Ufh5GfWUbO5JfJQOj98zFqkxOiAA0P6AuBowgoHp8cp7QuKidDjdYIu4Ny+FaHBGgvFnnQBNcOFAPDcXAb3hj7kQ4N4wdxyol/BalAk8BtxnXK7CObllQ673oakWJEdpkRJvktwchEbW1r21orEU5GdG5IURzomwDeGYne4A/rp8L6rrO/HYsl04WtMZ8bfRRjanKNxDU1qYJeD2iMCzETJIT9R3yradlmhGfbMDZ1pdyEjoOj5Sf2+sP4yBqVG4cmQa1mypwssfHcSLq1i+jYL8TCzfyIZWUuIMmHblQKzZUoW/rzqAVz46KOGFmVc0HBYjhS17avDKR9+j3eZFRoKY54QLUXjpoKgqx0sHsHzjEdxzc47snLD5UmrMui4L5V9XY82WSpRvZ3lWUuPYhNC5NwzDsMxoXD+2P15afRAvrTmIp9/dgxP1nYCii2ckzqyRzMU9N+dgTcVxBEIhqBdXH8CTb+6G0+3n+WY4pMQZYNJrUP51NV5cfRDrt53A1AkDEB+t63rr1lOIM2mQlWpBnEmDaBO7Frjz587hj2/vxtlmJ9KTjCjfXo01X3ZpLG3dVysx/iX7ojecLBEQaX2ePGODjw6iTGavGgX8NXx/kcYQIknk8njC+wk/L5NeLfHAzisajlVfHINJ2/X7aJOWrxwTtRfWfqfTy/8t9ITEmTUYmhETcW/+VOiWE+s8riPBfxdItdRFwHlLB5yLGjZXLeVhy5mdHhrRRg1arB5RGWnEnJtzhOxcKqRVLvNvycWps52INunw8kcHJe1wFS/8nAjaMBspFORnIinWIKo+4vI/nrvvV92XiCuBuhYXjp5uRzDIkpbx5w2xrEAwEITV4YNRT+FvH+4HHQiitGCoiH+Em7uCKzKwZkuVpNribLsLh6rbRVIbrVYPHrvrCjy3Yp/ojTIlzoA5k7Px/1buR3y0DnfdlMPnnQh/M3fKMNQ02gEGyEgy4b1P/8N7MMoKszB6aCJfkSSsDCmaxIamwnHH1GFwewJIjjOgsc0lmpM2u3xV3GN3XwGznoLFoEZzpxd/eOPfkt8IVa/l5kKvU8HtCch6Dh6cOxrPhuZHS6nwwK2j8fwH+yS/YxOyHZgwPAkmrcD5rAAcHj9ONdrRIOBSEh7750Xj0WZjeZnOtjh5GYd5RcOx7qsq+cRYQfuifdhTpZDgODkdsE93nkKMWYubJw7EmRYXr9aenmRErFkLvZbqNQ+LzU3jmRX7JPkti2eOwOovjkvPSwk0tHlYsk2NCht3VOOmKwdJmLmrz9pxts2FlZ8fi8hCnRxnQIvVLUmiBgCogB9OWUU5Phel/FpQLXWpPTQ/N/kFDn1BhoFUS11inJd0wLmGtMISgrkHjcVAicIFXtqPUUNGizf9uRhR3SECDXpCtA6OCGGbzGQzygqHIi5KF2KP9SE9yYSn752A2ka7KCGUqz4CWE+EUElY9mYWBNLjDYgyULA6fZgwPIl/uxQaUJFKZh1un6gKiCuj5Uj5wl3sJoNG9uGt00qrNRraXNBQLI9Mq9WDmka77G9qGu28MVd01SBRaCYYBPYdb+aJ7e6bNRIaSsm3IzffnIGxtPRyQAHcML4/LEYNaluciEQ2eLi6DeXbWXI/tVqaI8RdC6EmVfhclBRkRczFqW9x4P7ZoxAIBtHY5kRNo3xIw+NlwyVOnx90gGEfrFE6OF00/vLPvSi7PiuisnZrpwd//ede3pAsyM9AMAh8sft0iItIRlcLiEgGuGZLZa8MoigjxVdSnTxjE5Vi+/xBZCSZ4aX9iIvS48PPj+JojZW/ljEmjbSyMQxWh0+WrC/eosVDc0dL90UASInRwahVwer0YeEtuVIDgAEGpZqRFKvHkPSxcLhpRJk1eHJAlxyCx+PHo6/t5F9iFhWPAMMwXfQTAWB4ZnT3oaALdd8RIlJhxE/RF8HPEsS4uQiQUxTuLZPnBVPDDt/s0PCfA7jweUGS/oAT9Z1Ys6VSwp5bVpiFdzcegd1JIylWzz8wOLHBcPVtYfURbyR2U+UlOx6GfdvlzleugogjiHN75T0NkcJ6ka63XiNPhtfY5uSZZbnPIvW1YHouPhaQsXFeJGHSqLAyhAsLhrMVV+xmq9be2fgDAODGCQN45ehI5fpcbspLaw7ijwvGyf7m1Fkb/P6ggJRPPBc7DtRjwS25sscmxRqxfNMRzJ0yDO9/diziOFqsbj5cxqlQ25005hcNR0ayCW5vAF6ffFk8V13UavXg3Y1HRNVFtY0ONkk33LBBZDJAOdJDIZu3XMWfcC1dOzoDb6xnvYIl12VJ1vorH3VVinW3H7kXqHCyvgnDkyLvi572TOgzk1aNAWkxaGmzS/h6ygqzYDZSMINC4djMHskZJf1cpHzEi94XwSUHybm5GGCAYZlReGrheDx8+xg8tXA8hmX2bkN1J4lwIfGT5AUpWeK/yrM2NFm9eH3dYdHbZVlhFh69Mx+beE+IuLpjYl4ajtd0yJ6/lw6yoYs5owAgchVaWJWaw+vn/xbx5kRIllQq2byh8LwILkdFtn+B5+qJeWPxzL0TMCwjCm4PjTmTh0ryK5JidUiJM+D/bh2FrPQoLLhFmmOTmWLG3CnZUIDB/FtyUVbIVrpU7K5B4dhMUT4VOz9+LCnJg91J49OdpzD96sH4n7LLcf/sUVApgUXFI/h5D6+y4cr1w89XWOVF+wOSvA2u+kikzeWiYdSr8dTC8Xhq4Tg8NHc0MpONkqq30sIsLN90BBPz0tDQ6oSWUmFQqkWSO1EW6oMbx6qQNpeXDuDN8h9wy6TB2Lq3FhYjJTmH8OqiVqsHFbtr8Oid+Xhq4Tg8uWAc1GoF2hw+OHx+nGxyoMHqgcMXiMixJJeEzCF8T5mNFKrPdGJJaR6vhxUXpcGS0jz8ZlYecgbFIiPZJNtHT/tRLs/kvlkjYTGGvQCdR9Vmm80n+6Jx7egM2Uqt3tw/LlY+Ynd9tdl9P0n1KsGlBfHcXAwIeF/O9Y3hvNWwe4le6UqdC8KkGpaU5PHtC98uf1OSJyIUEz0wFJFp/UcMjseVucmoa3byrMWSeZV5UxO+6Yfz5sj1k55kxrVjMqBUKvDUovFwumk+rDdm6Jhu+w9/WzUZNNi86zRKCoYg2qSDTquCw+WDwxXACx92Ed3dMXUY5k7JhkFLQa9VgVIrseyTw7z8wHP3/QqxlyXB6vQhf1gi/vbhfgkpm0nHShgIwwFKpQLtdi9GDooHFAJ1bply/U07T+Gh28fA4/OjttEhCqNoKRV8fgYjh8TisbuvwOHqNlG4DoBIm0s4N6mxBiAAmPRq2VCfUglk949lPSBhFUIqlRIvrj4Qcb146QB8dACtVg++2leHssJsLC3Ng9sbQKfTC41aKR8yNVH4/oTUI8Gtk7LCLAxJi4rozRL+L9yXwj0lx0Fz78wRUIfEVLnP5hcNB3AaVXWdkj663Y+hF6hH78znc8tWfn5MnFN3np6LSCSewvkP/66n+8cFv+90g0h97TvejPREM/Hg9DEQ4+Yi4HxCS+cT0joXdGtEhVOoWzSwOdi4tY9RQKOEJKbd0ulBU7ubZ1rVaeVDMvHROsRH60QPTuF5cZVf4dIHqTE60bxyZb91zXYkxugRZ9bw35uNFMomZCEx1gCvL4D5Rbl4s/wwVn1xDL+eNRKvfvQ9750JJyh7d+MRJETpMHfKZWjr9CAhRo9gkIHD7QelVuHA8UZWBNLpg8WkwaffnESUWQuLgeKTk7nkU6UCKC0cKtKQun/2KJ51FwhVM206iqKrBmHFZ8ewqDgX73/GkrndfuMwpMYbYXf7YTZQrM4PgLk3DBOtD96b1OxEtFnbpQcEdCXgKrpEDwHIln4fO92BrftqcevkbN4Q4rwsb35yGP87exTMegrl26tFD/GC/EwwkOce4mQWDDr5cuTszFi2FFlQZs0ZiTaPmBU5fL1oKRViLayY6ujsZDz7/l5J+0KjiX3gKyJ6JLiQ06qKStw/+3I8eNsYvFXeRYjHJetybYfvS6GeVniiuJdmyfqWll7O7xHO+7S09HI8u2KvKO9LuB+5nLTwnBGbk+bDQhyE95nzDXFHIvFUKhQIMsyPegkL1366dnQGlEp0qdJfqHucgiVqlBtjMIhzmoeLiUfvHveTtU37gz0mO/83SzQQ4+YioFvZgJ42U3ccG+eKbpLpIhpRJgpHa6yoa3IiyDA43WBHWqIRm745ySc98m9/6JJ/4JIz5xXlYu3WSqzfdkKielxamIXX1x4SKTpzSZoAcLiqGbOuy8JHX1byIpBZGTHon2IE/F1vYuFvxZyidZSRgtlIofjqwfD6AqI35HlFOYg26XC21c6LFQ7JiBYpHyuVCmT3j0Zjmxt/fHu3JOk4Jc6AWddlifIMFkzPhdXuRlVth8hQ4s6LDgQx/erBSI41oM3mxpkWaQKxlw5gYD8Le7M1UhiUOganGu34x8ddRlFZYRbSEkwYlG5GjEWLJaV50GvV6LB74PT68bycNwni6z+sfxSeXjwBHjqAjCQT6pu7qocWTM/FrkNn0Gr1QBNKYg73srTbvRiSaubXjXB+goy8kvqZNhdeWn0QGckmCZvy4hkjEB+jjbh8LQa1RPWa87Bw4TuViq1CZJjInoRwj9JvBF5F4W+FHon6Zgf8AQZzJmdDQ6lwtsUROVmXG69ejQfmjEJ9iwN1TfLXubbJxgt4cqFZhZI9B67yjfPYLSnJk1Q9Cj0vPXlBztdLwpF4Cj1ci2eMwKB+FthdNDKTzbx2G6fO3tO9SsgELqz0EqrSn5eBI9CVa7W6JazaQrX5n6MMw09ZLdUb/DdLNBDj5iLAqJN/YzDqermRepP41xN6cklHMKI67DSa2918WS33QCm+Zgj+HHqoC9/I5XStOJHFrXtq8MT8cfi+qkWk9LyqohKP3zMWJq0KFiOFAcnsA0OnVePFVQd4j0IwCN5jEGfS8G99keL9Ty0aj4L8TNictKgs2Gyk4HDReKtcLPL46kff46G5o0Xl+SxPDHszD086npiXhmXrxW/8b6w/jD/MG4eX14i5ZYTJp5wExuP3jIVWrZRdG8kxevZ6B4FgkOENG669VRWVmDN5KJxev8gT9OBtY0QeC+H1ET7U2fLybDS3u2UrxN5Yfxj3zx6F0412mCKQvkWZNKJ14/AG8Ke3d3cb5qsLKYmzYZfTfLUWGGD99io+qVeu6uhoTSdWfn4MJQVDkBhjgM3pRWKMAXOuz0Z8tB6bvqnG7MKhsERT8Abl+zfqKPxluXh+6psd3YactBTLayTUtSorzILFoIFJq+42iT0hWofnV+5H0aRBEb0GqwVeIi2lQrxFx1Y5KiAynpRKBX77yrcRPS89hbDPO8Qtp58VpZFISyyeMQIDU8ww6XpRicR0MYEL6QfO1askC5l73h0hjTTaH0R6khnvbjzCG49EhqFvgSQUXwR4fX4JqV5pYRa8tP+ijcHh8aOu2YGiSSztutlISRP3GCnBlcNDY1VFJTKSTfjt3NFYcEsukuKMMOrVvBJ00aRBcHj8cNN+zJ0yDD46gKJJgxAfrYPZSKHd5sWc67NRUjAUXp8fqyoqRYrSXjoAvz8gISbrDJW3rvmyEmu2sMc0tLnQZmOP49765EqLzUYKHl8A6UkmZCSbYBYkVsrRyHNEgnxCaCjxsqXTzZ+LJOlYwfZTcl0WSgqy+HntsHflJgjnKCsjCrdNyWZp7ycNgkrJQEspsXiGVMbBYqD4MURink2NN6KxzSW6plV18gnYDe1u1DU7+HmYmJeGuian7DxwCbqnGzpx7egM1DfbZdev3x8QrRs/HeDbCidv5B56W/bUID5ahztuHIZbJg2G2+uHUafG+u1VvGijXEIpF1JpaHPB6wvilY++x5vlR/Dn9/bgpTUH8ed3v8M1YzKhVLLuFqVSIUuO5/JK5TG27KmRJEdzpJeccb58k1j3a1VFZa94tThvidx8cEnanJeI8/DFWboqGYX7sT3EzxN+bbk12y15XS++7zHZWMGGvpxulsU6zsyGp8NDXa+tPQSvP4ja5l4mLTOIKFtyPoUTcmG45ZuOwusLYlVFJeqa7Lxhs3jGCCLD0MdAPDcXASaDBhW7a0Su/YrdNRg1ZPT5NdxbUjEFcLLBju376/hw0eLiEfhs56keXbEeXwAZySaejVYYZpl17RB8sr0aE/PS8MPJNgzNiMHGHdV8uGrO5KGg1EqRQrac+GVKnAE6rRpVDTZoKTWWrT+EhjZXxFLgOItOVIkzMisB2/fXo6HNBaBLduCpd76ThC9arZ5uK6OijRpZEsJ5N+fC7aVFEgB6rUpWSDAxRs/nWnDhMrORgl4jVuJOiTPgi38fxRU5KVhamgeVSomkGB1/PbgxyL31p8QZYHfRIo9aaWEWAPnch1NnbSj/urorh0MBMbW+YB64B20wCAzsZ0FclA5/W7m/x/Ur9Ay0Wj18ldbgtCjEW7RQqpSgVEpMmzhQEiZccEtXIq0oRBDK92rp9ODRu/LRZnVDoZDn2KlvtkOnUcKkVaOt041NYZwvm3aewv1lUukJu5PGoFQLnl48gZdviDJqkZ5kwol6G5weml9bwv5EeydCyFdYov2pQBFe6LnUUioMHxiL/GGJ8AeCONngQHyUTsLB0hvPi1GnxtLSy+H2+qHXqWHQqERjS08ySZLMudwsh4fGD9VtCDJsztXdN+Ug2qJFW6cHVo8fbR1uSUjMqFfDbKRQNHoQb8Rs3VuLM81OrPj8KB+e6im8dEEKJ8KugSOCwcSt7/QkM8u7pFBgYIqZJBP3MRCG4ouBn4JfQeYBXFKQJYqHC5Wx5dhLFxWPwOA0C4yUKmI3rU4fWjs8eHH1AcmNpzREpicXguJu2uEK0BwjL5c3weWtcOGd8CTKcONhUXEuRg6OlbjC5xcNx9oQw2xZ4VBZdlpuLJG+f/TOfKTHG2Bz0XwFlFyVC2coCT8XtvPQ7WPgcPlwVsCSWxKSMwj/rTChWEuxydI5mdGSMZQVDuXVxuUSkbn25kweCr1Wjbf/dUQyn9w14R74SoUi4jxpKCUqdtfgobmj0WL1oL7FIWGmDV+/Dq8f+463SIy90UMT2ERmJXC6yYnKWqtsv0tK83hVai7fKJxXZeH0XHQ6vVj1hXTep189GOMuS4RFT8HHKHhlcOFvIrJZZ0ZJKhoXzxgBt4dGZ1hYk2uLD5l0t7/Ru31q1KlxqsHW/Rz3cB+Rm/87pw6DyaAR5SlFOl9hhdgdU4eB9jO8aKbkBSE0B08vnoBDJ1ol11ypVMDp9vOhth7DS+d7j5Q5/r5ZI7Hy82Miw5RbJ1pKyZ/rT81183NlKO4Nfu4sxoSh+FJDwHPDx6rDacrPEeEu14l5aZKKDy5m7XDTmDtlGOqa7CiaNAhb97JyAK+vO4SnFo5njZsIb57xFg1aIoRFooxavPGJuM+3/3VERGwWDLOdG9pciDFr8Nx9v4LN7UeQYfDnkIeFa0OYg7Bp5yk8dvcVcLppft7kXOFclcnpRhsSY+TZaTOSTPjdXVcg2qRBcpxBlKty36yRSE8wAEFxArhcPs+qiko8dNsYdNjlwwSVtR3IGRgHvZCROIK3yB8IoKRgCLw0uxhWfn6MzbMQJH+2Wj34/N+n8cCto2F3+aDXqcFE8LpEm7XYGFLrTk82oS6sjJt7c91xoB633pAtqRCbVzQcDpcPX4SkOZRKBZ5fuZ99OxcYRemJRsnDoN3mlfWWDEi1wKRlvYqnznZG9BgJVaktJgpNHV4wDLC0LA/rt51AVV0nlq0/jLLCLFkiyLQEE5/AmhJvlE2QN+nVSE804vF7xsLj8yM+VNlmc8qHV55aNB7+ICO7XixGNieqpyokObbucO/Jzh+aRAZUeBsAIhcXgA0nNYVYs4XVV51OGu9tOiryrtQ1O5Acb+DD1ADrbVlVUYnbb8xGtFkHvVaNZ/65V7LuuX3JfcZ9Hv67koIhooTsVru3a6xyRQ3nWTghdw1e+eh7PHpnvijhn8sHAoABqZZLLs1A8NOBGDcXA+fBcxMJksqHCA9Ph4eG0+tHXZMDQYZ9MBVfPRjrtp1Aq9XDGltmTeS3pgB4ocLwN1e9ViXbJ3dT01JsmagQWkoFi0HDvz0XTZKvquHasDtpUGoldBqW7wUAWiPkHnh8bA6TTiuvtFzb5EBSrB7v/OsHvmopLdGINE74MsjOoyIkFsiNg2tHqDKtUivg9tIRk0QPV7fxooPc93KhJbc3IApVlRZmweGhJW76DrsXdc12ZCSZoaGUcHsDsn2b9Kwbv/zravx27mgolQpWBRvsA8zupDEg1YKBqTko316FK/P64aHbx8DnD4b0jFRo6/TgkdvHIMgwaGh3Y2lZHpraXdh56CzGDU9BtEnH5kIoFGKhRbMWdifNP/y4MXGhBXbNBiXzwv0uJY5VbJbjnZkn5H9RAEyQwYJbcqHXsYnNZh0lSmBVKhXiF4ooHXQaFQ6d7BAZKUtK8mDJiIpYSdRu8yI+SoekWB1+f89YHKtph9cXFHHI9FiFJCwICOWtCB/utc3OiAafJGzMtWVgS7vrWp2wOnyiSjqhpy7IMKLwKOc9SorVS0Kauw6fhV5L4aXV0n3Jrf2kWNbzxK0ll8cvv39DY+WoAWwOH86o3fB4/RGrvX5U4YSgGkr44saNg2GYiAaTSaeGzUXzYTkixdC3QIybi4ALJqEgQKQYtdQAoVBV1ympduLCPXEWXY/jS4jRYmFxLpYJHjYLpg+H2SBfBcZJBSyeMQKUuutBxoWVggwj6q+7NuYVDceLqw7w3CKLinPh8sjrU3XYPVizpRLx0TrZsvNPQ27o8KqlpxaO7+IKcdF4fd0h3jPAtR3+gGDf3kdISmPnFQ3H1j01yB2ciC17a3jPiByPzh1TcyQcN6srKvHUwvGi0nw53as7pw6TlFKXFmbhrfLDuGNqDnSUElanT3LddRoVGlvZRGKzkYKXDvJv6N2F/coKs3DzxIFY+XmXCCNfjp5qlpVa4I2H0AMl2qzF4apmTP3VAMlcLJ4xAskxWiAItHVKeWfeKv8BS0rzsOKzozDqKFHY7d6ZI5AyQC/yhAaDjOSFghPHlFvnkfbTqbM2nG1xQKtRYcOOkzwbdKvV0+OxknyRCKGX9CRTRINPNudE0A4nzRDJ86lUKFCQnynyPsp5eVdXVGJp6eWi8DM3nkih2bQEE69EHz7u/ilR+GznSdmcNKFn6bzug90IkgoroCJpTBEphr4NUi11EfBTSCiEVz7sOFAvqfhYUpIHl9cv6zZOijXwlRnd8fDY3DSq6+yIj9Li/tmX4zcleVhSmgefL4CVnx/DPTfniPq8d+YIRIVCGJ0OL1Z8dgxFV7HVPEVXsQrVrZ1dnpdIVTVRJgpLSvN4pWZuTK+vO4zEGL1sJUxGKEEQAHb/cBaP3JnP9yvkEQmnzOeqr7hrJZSIiDJQWFQ8QvKAYN3eh2DWa0Tnt+6rKhRNGsI/ADeFkkgL8jOQNzQBT8wfh/+7dRQevTMfVrt8uM/poUVu+v8pGyW5hu9tOoook7jvT3eeQkObC6cbOqHVqnkPhfC6Z6ZYEGXSwksHZENur687jGarV2LsrqqoxJkWl0jDalVFJarP2uDw+Nkqm2Yn+iUY8dx9vxLJTgi5lMquz8YHnx+HhlLiodvG4LdzR+N/54xCZrIZtU1sdU0kJlyvL4D5t+Tyhg33+T8+PgSrk0az3YtjZzrR4vDibKtDcg5vlf/Aj1/YbmOHG0qlQlJJxMlJrKqohM1JY2JeGl9Nxh3bavPC4fLhvlliOQk56YNw0smiqwahrtkOry+A7IxoyZpePGMElCqlpNpI9DISwWPLJc0OSrVgUD8Lv05KCrJEoqrCYxgwsvsyUmg2KlTEEH7unJE9baJUr22VYP64z37sfVDupYy7Ptz8RaqAupiyDwSXBsRzcxHwk0goyMWow2L5FgOFqrPy5GFqtRLZoQePIsJbo0KhEEkLCBMKSwqycLTGipZOjyjHwuH04c1/HQHAKkBzpdxCGPRdYSNhVU1GshmxFi2YYBCPrDrAHx8+dpuTls3tuHZMBsq3V2PO5KHQqJU4dbZTxJ7LnVc4ZX5ciNUWkBcgHJYZjaJJg2XnsdPlk5xfY7uDd43bnTRqGuw4UNmExBiDKHF68YwRSIkzSBIe+XURctNHMo5tLlo2STkYBBpanbLHVNVZ4Qx5viI9GNsjGBdBhpEYhmqVAtVnbbKJ7EKWYe58GIZBQ5sLb5YfEbXPeXK0lAq/v2esfNgq3ojWTqlBaDZSqAlTjl9UnMt7CITjVYa9zmkpFU6eseGFDw/ggTmj8Pg9Y3HoRKtEToI7d6FxrKVUOFHfiVUVxzEsMxoP3T4GlbUd8tIH6J50cvGMETDo1Lh/zijQdABnWpxYsVm+2ih8PcjN1YjB8ZiQkwSLkcKR01Z+nWgpFe6fI60Y01IqxESodstINsuuh/0h2YIYs0aW5LGts3u5Bq7fH3sfjLQvkuIMmH714G4roC6m7APBpQHx3FxIROCJ6JFf4hzakkWIRl6OpybWouX75aClVLCESp6FYRjh+H49ayReXyd98y/IzwQA3o3OGQFrtlSi/OtqdAro8bnfhPetVatEb6l2Jw0tpUS0SYNEixZ6LSUaS/jxeq2Kz+3g+G8olRKZyWYUTWJVvH3+IDZ9e0pyXgun52LHwXr+/0XFuYiL6rq5yl2r/JwU3gCUjEUjfj9IiTNgSFoM5t6QjbLCobhj6jBs3VeL6ZOGSAj/Xlt7CPfOzENZIftGnRJnwLyi4eh0+UTXnDO4wvu22j0Szxkn6KkLKZCHHzM4LRo6jRJ3TB0W8fpwytnhnytDa0z4WWq8STaRnX8DDlvHcaHKL86LMCQ9CmWFQ5EQbeC5elZ89h+JF3JRcS5MBjXONDvZiiMBt9CNEwZIxvD6usP8WhWOd0h6DN9/WeFQ3D3tMp5r5vmV+6FVK1G+vVrEwyQ8d2HIVCjimTs4Ec/8cy/P4dTQ5pJ4ArojnXxt7SG0dXpxsr4Tr3z0PVZVHOc9jZHaAeQ9n0tK8pAaohSwOWm+Uorra/nGIxJupdLCLJRvPyGad7uTRnK8AYkx8uuBky3QUmqUf13N70UuJJQUa4i8jsDulUfvzGc9xOco4hk+D8L2WzrcSE80sTlY53gsIfLrOyCl4BcKkWK4mVFsAqHTB71WDYebhsWg6b5aSgFUn7Wj+qwNQYaBUqFg3cup5nOPGSshSaIsLcxCxe4azCrIQpxFiz+8uVuULAsGyMqMxlPvfCcZ2m9m5aHF6kLOgFjWtSvgKlk8YwRWV3TlZDwwZxR8/qBE90ivp3DsdDtS443osHkQbdGhucOFgalRbMUSw55/S6cbyXFGWO1eWB0eVOyuRUlBFr7492nk56SIEiRnXpeFNwReEa5keOehs5iQm4rkeCO0GtYYsbt80FIq+ANBfPxlJW66chB/ndw+P1QqFVxeGlpKDavdi2iLFka9GifqOkV5Rwun54IBgzfW/wCzkcK0KwfCbNCIvDNzJg/F5l2ncduNl+HZ9/dK5vO3c0ejw+ZBIMggLkqPTocX67efgN1J475ZIxFj1gAKBU6fteH9z6RluQlROsy8biiq6liPwY6D9Sgcm4ldh89ifG6qKE+iNMTB8/Ka73nvlk6rlng8IuXcxEfrJDk3qfFG/OWf4vOKj9bhf8pGwe8P8BICwpypNVvYh39KnAEzrhkiyRv6dOcp/N+cy6FWqsTVhQyrl9Xa6REdc1/JSPy/D/ZL5vaBuaPxsmB93jl1GPxBBisE88jmgAVhtbPJznlZ8fB5AzhWa+X3XpSRgkajQqfDh8QYAyi1EkoFeDFTgPVScrIhQjwxb2wX43Voz9Y127GqQvpbLqTa23a4fKwbJwxAcpwRFgMFvZZd39EmLZQK4GybC3/9p3TdLSoejjiLgd0LGhVarW4kxOiRnRmF2kYXjtW08+vpzpsug9sbkNxDOA/NUwvHoVNQwcjfi/pH4cgpq6QMPT3RCJvLhw6HD6+s+b77+1d3kLkH9pod+SLm3JxPKfilBu0P8oUc3eFSaVB1N7fEuLlAsLm7eEk4hHO6CA2LcJe1ED3yhUToT0tF4JNQAk1WLy97wOWDaCkVfj9vLP741m5JO0vLLseLq6TcNg/dPgYGnRqJMVocPd2lOaVUKJCRbEJavBHtdq+4TNXFGnexZq2EY4RL8uQefvfNGomc/tGoPmOXcKvcN2skcgZEw+ag4fDQ0GrUaO5wQwGFLA/P9KsHw2KkEAgy+PTbUxKenwW3DMfmf5+Gw0VjzuRsfPrtSVybn4l1X1VJfju/aDiOnGxB/mWpgIIBGAU6nR786+uTuH5sJowhBlk5PhRWTdyPvyzfI/nu/tmjsHzTEdwwfgA2fFMNu5OWJD/vOFiP2dcPxdlWF4IMA51GhSFpUfDSQcRHaaFSKWF3+XHohPj6shILw1DTaAcY9kE1d8ownktm+tWDER+tRVqiGVa7V0xRwAmg2ryg6QAotRLrvqrCmMuSeUXzVqsbg/pF40/vdK0fjkBRTtJByLOz5svKiNw/Qr6a3uyzSLxFt9+YjbREMxiGQbRRA1+AweMCin9ujxZfM0SUfC40wNhrdDmf28P95p6bc7B+2wk+pFhSkCUbApXsRwXQZveJpAa433KhnV61owQaOzxobHOJjOlwNXMGDFZXVEnaKy3MgkqpEOks3TtzBDKTTHjoVel95Q/zx0GpAA5WSu8hnBgpt89FVUkKyH5u89Cy/Tzz6wmw9FaWBpHb/8mPPQf8N/Pc9BaXig+nu7klYakLBLkY7sS8NIlLmKP57y55zeaWTwK2ubvkGs4pSTkIdNg9srIHnQ4vfnfXFVhSkocHbxuNYZnRKC3MwvptVZgzOVvkup4zeSj+sfZ7eLx+tFl9eGk16z5fs4WtPHpx1UF46SAbFjNQcHj8ONPuRpPVA6OeklRJeWlxkqeXZrkpOl1+aDQqyRy88tH3sIXCXn4/AzDAis+O4nSjLWKOiM1JY8Vnx/hkUGF7b3zyA2Zck4W5U4bhTIsDZddnY91XVbK/XftVFa4enYHTjTYAwDsbf8Dar06gcGwmvHQQb//rSMRy3nabF8vWH5INIS3fdAQT89Kw8vNjvOyBUAIBCnYdvfrxITYEwrCs0UdOdSDGrIXV7kMgwCA5Rov0RDPKv67mHzqzrstCq9WNrXtrUf51NYqvGYJPtp/gx9UvwYjUeBMSLVpkpVpYPSPOmxgKcQ5KNoFhgHc2HEF+Tgq2fFeLpnYX6prsSEs0YcOOE6KwSEF+puS6VeyuwV035fCyE3pdKBwQIecnM9kcMWQrXPectIVapcCDt41GSpyBn9sFtwxHYowBR0+3w6hjS6flcokm5qXxhg3X/+vrDovW5OkGu0Tb6+1/HcHiGSP5PnccqMeDt43Ck/PH4f/mjMKTC8bhsbvGSM+DYQUow0Ofi2eMwI6D9XxVXcQQdijMd7LBAavDh4++lE/Y5f5WQMFfH44M8t6ZIzBsQCw27zotOvYfHx9Ci1U+T+ZQVQsMGpVkjQmr4cJD4sJ1FP55pH5aOs/xQR6p35/6WIKfPUhC8QWCXNKwnOYR98DqLnnN45XnjvB4u4ybSEnKarUKNo9fwtkQF6Fk02LU4tWPDvJvqZwadIfdC0qtYNk8NUokxRgRZIJYVDwCZqMGTe0sr8SB4024PCupi5fGzebPnGlzorHVJXqD70l9GQAykk04ddaG5g6X7G/rW12iUMOcyUOhUsonRIMBglwFSCQSvWBQ1F5pYRaCQbGREh+tQ+HYTBEZGPeG/OnOU7jrphz+9ylxBl7oE2AfelpKhevHZiLGrJVNvAxPVOX+1lIqZCab0dzhki1Fj4vSYvWWyq6k08woPHPfBNS3uFDf5MDKL47xSutmvQarvzweEqxk26YoFTrsXiRH6wAV0Gz1oj3ECZMQrYXNzvKx9Esw4pHbx8DppREXpZNwquw6fJY/r4RovezcCcOX99ycw2p1QT4Z1qBT9yhnIDcf99ycA4NOjaZ2F7RaFZ7/gC2z5xSmk+L057RHOUQyWv9zqg0lBUMRbdagw+ZBp4PGsvVdHC4Lp+eiX4IREB8KANColZh+9WDe62nUqvHI7WPQbvci1qzF6KEJsLn98Hj9iDJpcLbNBYtJi7omR8TS5/Cxe+kA3L4AbzDFWHSi0NKcyUPxzfdnRPtXmOwvvCbBIFDX4mTX2I8k2hNCG8oJC+9HS5H3bYILA7KSLhDkElGH9Y+VTVrjEhIjJa9x3BHhx8VbtN32V1aYhb+v2o+HXv0WR2s7RTfohCgtFk4XJ2myStgHUTg2E/HROtabsf4wrhmTgWtHZ2D5pqPYsqcGDAO8uPoAnv9gP557fx8OVbXinQ1HUL69GteP7Y8dB+vZhOLt1bA5fdh7vAU1DQ7JGzynviw3Hxy4xNsgl7wZ9tv6kKo01+bmXadhMWokb7ucKKEwabY37a2uqERqvFH020ilsAX5mWi1etDcwRqGB443ofiaIXxyZfn2asy4dggCQfa46jM22cRLYaIqNy6lgn3j5nhdwqUeWK/XEf4t/aU1B+Fw+9Fu8+Hl1QdFCamvrzsMf5BBbaNDtFaa2pyoa3LA5qZxsKodf3jj33j2/X34+4cHcLCyHQ/9YyeeeHs3Hnr1W9Q1O6FWqSQejNUVlbg8K4lP7m6xunucu7f/xY6bpS+QJra+vu4Q6lpcov659cyte7nS/Lf/dQQKhQK0n8HLq8Ue05fWHIQCUjHN/qlRPa7JSInXwSDw+rpDUCgUiI82SBLGl61ny+rDYXPReH7lfpHX8/mV+xEMMsiIN8KkU6Ou2Yk/vb0bT737HR57fRcq6zux91hzxNJnubFzf7daPWAACTXAys+PY+a1WaL12tbpxf/OvlxyTXYcrGfXipO+IN4Oo56SpXMwnktIioCgGxDj5kJBUJrNcXykJxgkBsg9N+fAqFfjgTmjpC7rkMvZ4fLhvhIxd4SkukrQ32N3XYHpVw/my7S5m7moYsVBIyVejyfmj0NZoZgXJZy7w+ML8G+0kR7s147OgNlIoa3Tg1snZ+O3c0dj1nVD0NjmQmK0XvZtd8ueGkloZlFxV/VSSpwBWi2bexBj1kgqZjhVaSG4kM2mnadQUjAES0svR2lhFhQACvIzkJURjXtnjsCOA/WSqpJFxbnQapQoKcjiPQleOoAWq0v0W6VSXv07KVbPJoEywLyiHEyfNEQS4njzkx/g9TFYVVGJLXtqZJWhdxysx5zJ2bwK9cLpuTAb1PjPyVbMnTIMapUS2ZkxyEg2ic49/C2900WjslaqCm42UtBq2HYfvG005kweyjM+b9lTg2arR/Rgvn5sJpo73BIF+Uiq1Fx5tZZSIT3JKFrzkTwjqfFGzJ0yDBpKgSWleZh7Q7ZoTR493c7PedGkQahrdsDhYT2X6YlGDOhnkW23sY3l2pENeVhZMc37Z1/O74G1WyulFXXF4oq6KCOFOZOHyhrPXjqA6nor7K5IDMcehKOnkHIk/pYoo7bH+ReqmQv/To2Xn5NTZztF/bz60fdIitXj4TvG8HNUsbsGhWMzsWVPzXlxcwkRY6SQGKvH9KsHo6QgC9OvHozEWD1iTMS4IbgwIGGpC4lw+vBglwFyps2FuiYH1nzZFUYQQUYI89E78/lkSFn3r4AHRShOCbA3KoeHNW64vrfsqUFBfoakUkP4kNRSLA1+RqIJ67dVRwznaCglbpwwgL/xiQU5c5GVHi3hcLE7aTjdtCg0kxirw4LpuahttCHWrIPT5YNOq4JOQ8Hq8KCsMAteOoCsjBjo9SoU5GcgyLAlsADQP8XM6+N8tus0YsxaFF6RifXbTmBiXhoqazuQnRmDe2eMgJcO4vF7xuJEfQeSY01YvukIH47j3PuUSomsjFi0WN34w7yxOF7bgfQEE7SUlGnV5vTxjMjTrhwYkdcjwAThpQPwWgM8OaCGUiK7fyw8Xhr3zx6FIBiYDNmIj9JhxeajACBRYp8vlCBAV9iqpCALOw7Us8zNGhXKCofyml4HjjdhQm4q/hpKZk6JM2DB9Fy4vX6Y9BrMunYwTHqK54SJj9bBqKewquIIzEYKBfmZuHXyMHTY3TDq5EMWlw2Iw9wbsjE0Mwb94vQAAzxz7wQ0drgRDMqrlOu0arzw4X6+j6RYAzrsbv57Sq2UhJ2S4wwwatV4fuV+WaV0du2aQFFK2e/iLDrYnTROnrWJKpI67F4UXTUISXEGtHS40T/VgkXFI2Bz+RBr0WH9V1XodPrwu7uuwH9OtUkUvfslmBFl1sj2GSvgUOJCbf4gw6vLA6x3S6kEjDoKDq8fjRG03PQ6+VDOyCEJSE8yo6md3WsF+RkY1j8WsRYtr5/kjCDXEQyr2PTSAVgdvhD3kwJQMJiYl8YnuJ+PSrcoVB4EhmVEIzHacMH09ggIhCDGzU+N0GbmHlIcwmnHw9/WGtpcePq9PV2/6cb9K5d/kxJnQIfdhz+905Unwqp4yz9suNDIouJclgafAZaU5KEuFEqSPETijawGzVWDZFlup189GDOuGcIrdbMx/mxs+KZaxB+SmWLm2wG8CAYZSfnylj21WL+tOqTqzRK9carFL4blyygArN92QlYBfc2W47jtxmHweIOysgelhVmIMmnxxJv/FhkxDe0u2QTvR+4YAy3F8pas/Px4xAeuStH1sOV4gbSUCiUFQ6BUKLCqgn1Yl2+vRtGkQahtdGBJaZ5ozXjpLnHQZ1fs5cf27sYjfF5NYqwejW0urN/Wdd5C9XAu/+Wvy7vkFuYVDcf7n/4HUycMYIkQR2fg7X8dkc1puXdGLubfMlykTl5amIXX1n6PSaPSYdap+YcTq6OkwDPv75WIXC4qHoHlm+T7KCvMQlK8AS3tbr6Shzv/19YewvSrWTJFjt8lfK28s/EHUCol7rk5RyTRsKQkD3EWjeyabrV6UP41u76yM6JRE0ZKOL9oOLy0H6+t/V7WkF++6QgeviMfC6fniiqXFk7PRWK0ls25kSk9njM5G5RageWbjsJLs7lBXIWT3DpqanNJZCuWlOTBqFXhibD7i5Ziq5i48nGlSikrkrruqyrRvYQLl5u0KqQnmiJKafSI3pRaB4E4k4ZNZA/9T0BwoUCMm4uA3rBhng9jppymz6LiEXwCLNfW6opKXlG5YncNJualQatRYmhmLNweGk/MH4dAMIA2uw9xZg2G9Y9CYqweSbF60c2+rDCrK+E3gmcnyDB4s/wH/H7eWHQ6vNCoWM4Se6jaiXsY8Uy6CiA+Wi95oAt1cjhvhJdmWYrDVZRZfZw82WqnNVuO49cz82Bz+XDZgFjsO2YSJVJu3VuL1AQTXlgpNnpWVVRi4fRc2XP00kFWYyfRhKKrBkGvVUkeqmWFWVi/vUryIJ4zeSgA8A9w7mFdsZsNXdH+oGyfDBj8edEE1Dfb8dHWKt5QfH3dYfxx4Tis/PyYaPynG7rCDnIhxrfKf8Cjd+bj9XWHUJCfyYcTi0ZLjdZ/rD2MB24dJZsUnZ5kglKpQG1LSITQoAYYBndPy4ECCvzP7Mvh9viREKOHAgwmXp6GzGSz5HqvqqjEPdMuQ/8Ui0ixmgu3ClXmGYbBktI8KBRAU5uLD8sCgNPDeggH9rMgOUbPPpRDntT0RKNE5XtpWR4SovXw+AISQsA3y3/AQ7ePEclycOdPqZVoaHOh1epGXlYsnlwwDu02D2Itui7DBvKhppWfH+ONNeH5y6melxZm4YvdNbj7phxJQm9tszwbtfDeYdKqkJZgEiUxx5g1EqoKoQEjZEBPjjNBo2TOS6X7fPX0CAjOBcS4uQjojfxCryQaunHzctUXapUCqfGmiGq9bl8Ah6uaUXZ9Nv7xsZR/Z/rVg+H2+pGZbEKHnRUwNBspXkE7yqTByTM2JMToRTkIcp4gLx1Ac7uLD00dON6Ex+8ZC4fLh4ZWlh8nxsyWp2o1Snh9Adkxc5VDwhurMKdHSEBo1FMw6sXj4TwWHB+LHHlcWWEWVCFDTURoCPDnKgmtaFRIiNbB6vDxfC0pcQbcP3sUmtqdGJwWjcq6Dlw+NElUUZQzMBYqJRAIMF0PSgC7Dp/FxLw0DE6zIBiUrwCrabDjpa8PYl7RcMSYtaIqmZYOaViMS8zuzhB1emhMmzgQFoMGSlUoeTbCb8MlH7jyYkqtxLFaK5xuGsomIMqsw+ovjmPaxIGwOWn+garRKPHBZ8fQEPJCyPVBUWpRZZqQ96d/sgVD0qNkCQqF8+T2BFD+dbWs5zMYZBBv0eKphePh9NA8/9Jjr++KqFLv8wehpcSyHFpKhcfuviJ0HkFU1dowJN0CnVoJq8MHm1PF71GHhxZda85gC4bRjHF7dOveWt44i7Po4KX9GDVktKjsGoCogqzbewcDDEo1s+tVWOkERK5+EoTZExJM58RjQuQNCC41SELxRUBv5Bd6/E3IzStXQcJVX2zZUwOlkiW0O9Vg49vioKXYKhyhYQN0eT0m5qXh7X8dgdsbQCCo4N9gW60erKo4jpfXfI8jJzvw7sb/4J0NR9hkWJlEXS7ZUkup0NTu5isxJuSmwqJXIzXkKl+zpQovf3QQ67edgFKh5LVtZMccapMDV8HC6fRwFR9Pv7cHUSYdzz8CSD0WE/PSeMOGO/9VFZUIMGw4T9he+fZqdNg8sgmlpxtsqG9xiZKIG9pcWL7pCCxGLf749m4s33QU5durMT43FVv31aJ8ezWcLj9MWpYaX9jP+NxU7DhYD5VShWXrpZIYwiTWt8p/wC2TBovnSSmt6tlxoF5CtR8+v7WNDnh9AWg1Krz/6VHcN2tkxAohq93DV7lwhH3rt53A0+/twdshTbFgEHjt40O4fmwmvL4A1m87gTVbKrF+2wm0tLtx/VhWFkETyo0J76PD7pGszYL8TJQWZuH9z/6DGddkSbxKQsFELklbkoQv2EOPvfFvPLZsF5xuP4JBqUp9+JjMegoLbhkumseywizUN9mxZU8tXlx1AGdbnTgjV+WlAlptXtG1vnHCAKTEGXgpAmFfXIVT+dfVSI7RI86kQWqMIWJ4utfyLnK8Lj8R1wuRNyC41CAMxRcC3SXOcRDyiFhYHhEJ/0U3jJndMRJbHT488fZulFzH3tQn5qVBQymRlmTG8o1dSbMcNXm73Ysn3totOQ2OQr6kIAtKBSJSxHPJmJyHY3CaBXodhU6HD/XNbOIyx5C68/BZjBuegmiTDnodqzfjDzD4vQxD6+P3XIG2To+IR2XB9OFIitHD7qbx9w+7wm5czo3b65dlp33o9jF45p9sbgmXayB3DkLMvSEbA1KjUFVn5d+ot+6t5c/F7WUrU4b1j8XJs50IBNjfrNh8jG8jPlqHu27KQV2THRpKCQUUcPsCUCoU0GtVMIXIDC1GLQLBIJraXdj0bVdy6iN35MPq8OLvqw7w85sUZ0BTm4tnheXwwK2jUNvkgFKhQEqcHjsOnkHBFf1xuqETQYY1bDiP26ET7dBqlDDqKFHYTOgVefC20VCrlPAHgvAHGHi8NP6xtiscec/NOfhqby1uncLqUlntXj7nSTj3j96Vj5fXHMRdN+VIcs20lApLSvPw3Ip9uOPGYQAgSdQGgOWfHhVdm9/MysOHFcfQavVIrieH384dBZNegw67F6nxRsSHJaiG76H4aB0K8jORmWzGqQYbn3xt1IvnaFFxLr7492lcm5+Btk4vuzYYlqWXY5Hm8qiWlubh2RX7ROf75IJx+MMb/5bMw4O3jUFzuxPvhXJuuPPnGIbPSQ7gJ2bbPed75kWUN/i5479ZfqG36K1Mw4WAUOqhu7klYanzRW82sRL4/kS7VMNnUKw4iS682kpwE+jOzcu9Jel1KknC4z035yAuSoc4s5a/4YlCFSEIk4pZ8jv53wjfNLm3yyWleXh62S48cOsoBIMMCvIzkDMwDv/89D+4cmQ/EcV7WWEWEmMNsudSXW9FjFknygtQKxX4YPMxTL1yoCjXYd22E4gxa1F8zRD58InDh6KrBiE92QRVBNXz8P8VCqDd5uGNJeHDPxBk+HAER7X/0O1jQIfCFVw468YJA0QVTmWFWbyBtKh4BFRKBq9+LA6HFV89GOu2nUCr1QOXl0ZjyBjlQiAlBfIyBUqFEmu2sA/V+2aNQO7gRD55mEsYDQaD6LB3VdPFR+uwpDSPl2Tgcmbio3Wwu2gJyVtZYRbcvgDAACYDhSm/Gog/vf0dnw8jN/dHT7Vj6oQB8NHyYUaPl/1MGH4RqrsX5GeIjtFSKlgdHoFCt/T6pcQZ4HT78dLqyFpF4ezGwmRmoQwDH4ZNMiEpRg+/P4CWTg/UKpWkKhGAmDTPK18OLh/qCiBnQKwoLKRUKvgKp3MyULq5d1wShOXs/JTyBn0Z/y3yCxcTzy2d1KvfkbDUeSJS4pxQWqHN5pNVLW6z+bpUk1udaHP4cLbdJVXIVQL6UBmuEJybl3NLJ8UYZYnNLAYNr/1S2+KEUqmQuLE5Vz4X+thxoB5Ly/JECspLy/KQlmgUHbdgei4+2X4CKXEGmA0aKJVAzoA46DQqjM5OllS8cJTwcucSH23Aqx8fEpGb/WPtYZRdnw21WikhwBudnYyaBrtsWzqtGlv31YJSq9BidYn4dXYcqMeCMELDeUXDoVQoJDw1qysqMe3KgRiSHs3Pg17DPlgbWh34ZFsV7p89CmWFWbjrppyInEDsNT+Es61uyfc2J82HVFjDRini+JEb78LpufD4aF7SwOb0SZKJ3yr/AUmxBsRGdSlxXzsmA80dLonydUF+pizJm9sb4BXfo4xafPj5Mcy/JQcjB8cjZ4A8SWUwyHpjDDpK9vtOJ3uzVioUEnV3u5NG/5QoybXJSDLjwdtG446pw3C4qhn3zRLzQN0xtSuRO9I+FIZK5EKV3LXnw7CrD4JSKqDXsuXqTe0u2fMRkuZx5yb8PpLKerxFB5NWLQoLmbTqviMHQOQNCC4hiOfmPNGbxLm2CG9uNpcPzR1uCZ26SFhTAXxf3Y41WyolFRThlQ2nBGy7wn6cHlrUT0qcAb+eNRKP3zMWXp8fei0Fq92DSaPS+RDFb0pGwu70ibwYcyZnI8qoxv2zR8HpoaHTqGBzeuFw0Zh1XZYoCfTXM0cgPckkO56zrQ7MKxrOvyVzPCeR3vQ7nT58e7Aev545glU8Nutg0KphMlCob7ZLSnAXTM9FMOjHtCsH8dVPXKLvmRY7+iWYseW707zHIMpIoV+iER02NZaW5WH9thOoqutEfLQOU8b3R2yUDkdPd/AemLlTsjG/KAcJ0QZcMyaD95ZESpDVhCjlvXRANoE0IUYPj5fG43dfAToQRIfNC4tRw5aKKxVIjDHA7aHxh3nj0OnwsrlMHU42NLK3FpRKiTmTsyNce5b4bvv+Oj48Of+W4fjNrBF4+aMuL01SBG8al8x978wRgCKI0oIh8AeAP769G2YjJSkv5jxdXjoAtVqJe2eOEIUZ75g6DEmxBjxw62joKBXmTskWqXTPKxqOtV9V8lxA6UlmvCcIrZYVZuGmiQMxLDOa9wooFAqcqLf2uA+FVYWShOkICdRWpw8ZCUakJZrw3qYjkj3IhZG0lAq3TclGjKUruZfbownRWiwqzpV4bgmvCwHBTwdi3JwnelOpEEnXyain8Oz7+ySegqKrBvFlk3SA4W+KXBmqUgnkZSUg0aIVVTZwb4iSfnQU/hLiNuErh97+TnSD3nn4LC7PSsK1YzKgVCgQZdbh5TXipGOudDUYdPM5Br+7+wosLbtclFPgpQP48IvjvNxD+Hj8AQbRJlYywWSg8Fb5kZBxMFT292dbnLh+3AC0Wl3456ddD8L5RcOx9qsq0IEgpl89GEmxBrTb3Pj4y0rcNuUykSejoc2FFz7cz5c9N7S5sPdYC4akR2Hy2P548s3dogdslPEsLhsYL6nI+XTnKaz4jJ2HTkenKFwUKdyXlmhmwz5OWjaBtKXDjZR4A+qa7KL8izunDoPPH5SEuYSqz1MnDIDbG8CZFqds3zUNdpR/XS3SIHrzkx/w27mjUVqYhWiTFo1tLnTY3bLH5wyIhVKhwMZvTuK2KZdBQ1F4bR2rwO61BrBp5ymWXTaG5dgRktvFmTQYkGjEM/dOQKvdi9pGNmeDW/OcsVNSMAS0P4j0JDO0aiVqGx2oqutEyXVZ+JtMaf70qwejX5yxKwyjAAxaeYLB8IohLlTi8PhRvr3nUGW0UQMwQFKsHnYnLSoF12lUSEs0oSA/AyMGJ8CkU8GkV0tDMQFg5KBYPLVwPCGsIyC4SCBhqfNEbyoV4uSkBGaOQKdDns5eKKwp9PpwORirKiphtXslbt5IY/EzDE+lP2V8f9nQiVAfaMueGtB0gD9GKE2g1SiRmWJGWWEWHr0zH7kD4+GRKTufmJeGZesPS6qM5t8yHBYjhfoWB7x0kDdsAFaeQU4jasueGtD+AN7Z8B/RuN8MKYpzYYTX1x2CUqHAxLw0BILyPDFWhxcLi0egrJCVUZhxTZakcuqt8h8wbaKU52V1RSV+MysPZddnISPZBA2lFPWxdW9tROXvgvxMzJk8FBYjJXt+//j4EDqdtKi/TictG9YTqj4nxhoABSJKO3DVVeESGyfqrRiQasHKL45hzZeV+HTnacnxi2eMwOvr2DDh5VlJqGuyQadRidYFN/caSilSi148YwQcbho2tx8WA4WBSSYM6hfFE9Zx41i+6Si8viBWVVTC42WlP/g57IZDSSQDEEFpu7uKITCM6Hx3HKjHvKLhEY/n9jAXRivfXg0AWPbJYayqqISP9sOkZUkMZUMxIcI6ifo6By483eKUhqUJCAjOGcRzw6GniqfQ9w43Da1GDaeHRrRJC2UorMFxZsgmzgW73tza7WxYYdn6Q5h4eVq3ib3RRg1ovTxbaZyQ1p1DWBKfUU+hxerGE290Me4uKh4R0aACgCHpUZhxTRYqazsAsDf9GycM4KUJTHqNyJNw/+xRSIiW8RhplKADbAa9UFmcAQOVQoHMVAs67WLjrtXqwaadp7C0NA+nBQmvdieNICP/oBM+BMxGKiQdUBmRLVhLqfEXQfjsN6V5su063LTs50dPt0NDsSXTd9yUI+qj1eqB20vLktz1T7WgodWBr/bV8d9nZ8bgwy+OCRJluxZNfLSu21AR/zdYCQq7k8auw2fx6J35sLl8qGt0RFSL5vJigkGGJ1VstXrwacgLkxCtB4MgkuMMmDtlGKLNGtD+IGxOGpV1Vj48J6y0ijJp8dDtY9Dp8CLGooOfDuDQyXbsOFDPh1iZCOraXOirxerGqorjfAhRyKbNVY4plUBGsgWxZq2onV6TzoX2sYcOQqVUoKRgCLx0kA3x7amJvI9De/jJBeNwsLJFIsFwXiXOpLKIgOCC42dl3GzYsAGvvfYa/H4/7rjjDtx6660Xp+Oebi6h71dsPiqpRgov3cxIMMrfkEJvbpRKwZejRqKQr9hd0/XWqMC5xesZwGKgcKbVhWM1VgmLL5cUGf7QVyoUiI/W4fqx/UUVN9x4CvIz0T/FIpEteOHD/SgNY1RNiTOgf0oUCvIzsXzTUZ5m/8XVB0Tzxj3UhGOxO2kolUqUb6/mf3vPzTloapcPuwjnuiA/k08qlZvbe27OwfJN4qRTZYRKqmiTfLgxGAQfOly+8Yjk2iTHmUShFO64M812rNlSJTIIlFcPxuVZSaiq6+SvAcAaNsVXD4YhgpaTMIG1qZ0NBf165gi0dXrw9Ht7WCkHmeoqzmjmrmnOwFjcNiUb74dyXuxOGhpKiaOnWnDZwAT84Y1/w2ykMHXCANm8mtWhEFFijB7/+Ph7EeXApm9PorbRwSqbbz6Kh+aOjjinHI/Rpp2nAHSFEMsKszBncjY27zol2XeyD/+eSOdk9rmwmm1JSR7izAI5gPB9HAQSLVqkJ5p/vCyBDAibLwHBhcfPhuemqakJs2fPxrp166DRaFBWVoa//e1vGDx4cM8H4/x4brrjkLHoKf77oqvkHxpCnouebki1LU488XYXxwz3RjooLQqxZi28tB8mHSW+WSrZiqvexuv58U4aJOFziY/WYe4Nw0Qlv/OKhiPGpIGfgUh+QHh+/VPM8NJBvLj6gKS/kgL2AcG9WQ/rH4vX1x3C7MnZ+NvK/Si5Tr6UuaRgCBQKhcQACQSDcHsDfD6IUa/Chm/YXAdh8unC6bmwu3zY8M1J2J007isZif/3wX7J3HI8MXqdCss3iflThqRHYcr4AaKE5HlFw0GpgHa7TzbnptXq4bly5hflICXehMraDgSDwOETzSi4IhNvCPSX7p52WcgTFOT5bgJBBp/uPIVrx2SgfHs15kxmWX6XbzoayqtSYPv+OslDfX5RDlITTfDRITFOrx///OyoaG7Cy5w5g6PT4YHTHcCOgyz/zfptJ3D9WFZyITXeBINODdofhEFP4cmQvlaka8et+YfvyOe5lMK/37qvFtOuHIj0RDPoQBBJMXpY7V7UNNkRH62H18eO1aCn8Nfle0QcPgDw+N1XINqkhc8f5PW+hH3I7jXBXokyaWEyqGEMGXaR9vnj94yFVq2E1+eHyaCR56kSQsApE2vWwusPoq3TE6JcYA0jfr9ynwWlx3Ne4DabB3/9515JN7+76wpEGdUIMiynUHdtiTzOkPcy93he3ZyryxeAQavuaqM33F4cQtfE5vLBqKNgc/kQY9ZKz6WP4nx4bkgpuBTPLZ3EP7//K3hudu7ciXHjxiE6OhoAMHnyZGzevBn33XffT953TxVP/PcRcgCEYYKe6MXDE5A5rpiuG7XMW+M5CswJz0fOMzIwxYxn7p2A+lYX6psdWL3lOM/DInd+SiVwusEOnVZebZljVOV4YB68bTT/Ft9d7oSXDvI8JxnJZtQ3d6mmlxZmIcaiBQNAr1XDHmLzvWvaZTDpKdQ3O7Hyi2OwO2neyGnpcMnO7fSrB/N8MeHjr210gPYH8Oid+SzBYpQOXl8AdIBBxe4qLCnNQ12TXRKG4LwgMWY9LEYNsvvHwulmQzyb/32aV5m2Obzw+AJYs6VKZJR9tov13uQMjAUYYMM3JwGAPS7WgKYOl0TLKCPJCIVCgeOnO0SelLtuugxun1903txxGckmqFUqlG+vwk0TB8HpsWNiXhqcHhoNbS64fQGeuJEj/fMIZTC6WfNaSoVAICgybLjv9ToViq8eDK8vgGfe3ysysjRqpSi0uag4FwlROpFxkxJngMvjx7Pv74soiSDZa0q2sjBc9DI+SoeMRGPEfd7p8IrG02NIiPMQGSlJf0vL8uCjg6KXBxGnlYwXOFIItb3TDZtThTfWH+62LaEX6YE5o+DzB2W9zOcc6ork0c6MwtGaXobRlF3VnnKioxKuLwKCC4SfTUJxc3MzEhIS+P8TExPR1NR0UfruiSpc+L3c74Rhgp5i772mSj8PcOPlQjPhfZl0rE378pqDWFVxnBcljMTjMSQtGjsO1kMBhSThN1wWQUupkBDFajF9vLWS54+JNG+cAVLf7BCNZXVFJfRaNT7+shI07WcNGCeN9k4vXl7zvei3y9YfhtPtl02KFSbxbt1bKxn/nMnZ0GlUOHq6A6+vO4wfqtvx/Af78c6GH1A4NhMrPjsKDaUSJctynEBlhVkIIojaRhuOne5AXZMD5V9Xo6quE2u+rERTuwteOihJCl62/jBGZydjftFwBAIM3zZnILZY3fyccZ+t2VKJGLMedU1OiUL5uxv/g8QYg2iOuXlVq1RYvukI8nNS8O7GIzxnjdsTEK3drXtrea6YcBkMuWunVChQGhJQlfs+McYAm5OWjPW1tdLE6dfXHcatUy4TXZdFxSN4QcdIYwjfa3J8Um+W/wCXl9XEirTP6wQUClxISMiPEwly/dU2OiR8QTynFbpCUEJxV7l9yinUc4ZNd20Jv68+a5O0f67nxSFSuKzN5uuR2yt8juTGIzwXAoILjZ+N5yYYDEIhKJNlGEb0f0+IizOdU39Cd1ZckMH9s0eJck3unz0KA9JioFQq+O+X98BzITym27HGmjAoLRrtdjdizXqkxBt7POZcIDwfLkk0I9mM/ikW9Etg1ZsbT7RI3mK37KnBomK2QkYYJvpi92kUXzMEbZ1ubNnTxSirpZTQalQipe/7Z4/C4PQYvv8vdp9G6XVDJbkpwnlbPGMEVmwWh4u8dACBAIM7puagfHsVZk/OxiN3joHdJZ/oC4XYY5GZYkZdkwMbvjkJSqXEg3NHo67FDiYIlBQMQVwUW7qsUSuw8dtTuHJkPywqHoGmdlZh2WtlS++vHZ0BhQJ49M58nGroRGK0EWqVAguLR0BDKREMBPHqZ4cwbeJAqJQKEe/LjgP1mHODPP9MepIJn2w/gbk3ZEu4YFLi9HweitBDY3V4RYKhwvYa25ySa/frWSMRZVJjwfRcvLH+sMg4q9hdg3lFw7HuqyoArEePAdt2g4CHiDMIV4WFDmPNWrz+yWFQKiUWFudiWdi15YRR5cYqx/Vjd/nw4v9eze+Jts4uskO5/Cm5vVbVeFa2P7fPDxcdQM6AeMk+v3fmCLz/mXTtuegABmXEojvI9RfpnDscXmQPiOvadwKPmHDdciHUT3eewuxC+bUjaUuu/wget96cFwe59r10AO0Rqjzl2ubnKMJ4uHMhkMejd4+71EP42YH2B3sV6vvZGDfJycnYu7cr7tzS0oLExMReH3++2lJZ/cwSfoq2Nofo+4fmjobDQ4sqKsLp0oXHTfwoAAAADsNJREFUdAeNAki26AAwvT7mXCB3PmDA9yXHC2J30hiU2nWcUUfBS/sx4Nos+IMBZCSZsH5bNR9+AtjwQXiFSUeHU9L/wHQTnpg/Dm2dbkSZtGhsc/KcOmajhjeQOGgpFYxaFVJidFh4Sy4cHhpGnQYaqvskW85j8eSCcahrcmD29dlo6XDjtfWH+LAHl2fRP4k19BJiDThR14lvvj+JGdcM4dvnvCZaSoXfzxuLQIABAwZnWp1we9jclftK8jBpVDrWbTsBAJg6YQCWluaBAdDY5gQdeoiGj7emwY7aRgeiDBqkxxvx1MLx2He8GcEg8H5Iq2ralQPx0G1j0GH3wurwIMasRV2TQ7a9EYPiEWfRyNPdK4CH5o5Gm90Lry+Ipg4nJual4YvdpzFpVDoGp0Uh3qIFQt6iZqsHh6uasaQ0DwoF0NLhFlUVrd92Av87exTumzmS7cdISSQE7G4/Dle3RUwgDr/WMWYtNAqG3xNGQTK1sJKLG6vcXouNwDml16hhoFRoa3NI1qVSpZRdewZK1aOWklx/kRLUY0xatLTY+X3HfR4phAoAep2qV23J9R/e/rmcF4dI3EGxERLD5dqODfN6RzqXvozzybk5l+faLxHdze3PJiw1YcIE7Nq1C+3t7XC73fjiiy9w1VVXXbwB9EQVHvo+NcaAOJPm50+X3sP5RAqPmXRddPCcGnGcSYMkix4JFq3kmDum5iDOrJH2E94/DSRFaaGAAk+98x1e/fgQyrdXQ0spoQIih+oE8x5joJAcJR3Dwum52HGwnv9/UXEuEqO1SE804eMvK6GhlCLv0pKSPKTG6PjrNzDJhPREE2obHXjrXz9IQldLSvJAh8QvX1p9kFX6/roac28YhpQYHdITTbA76RDnC+tdWPHZUbz/2TF89GUV5ofxp0hUq0M8Lf1TovgQld1Jw2LU4M3yw3j5o4NYs6UKCjBISzRKx1eaxydnyl7z0BwOSDKBYRis+oKVOqhtdCA90YSByaZQlRG7JnYcqEd+TgpeWn0Q72w4gmCQwZotVXxIa+4Nw8TXPKxfk1aNlBgdBqVaJGNdPGMEosK4frjr1d36tDtp0Vjl9pocn9T8ouEwaFVdYV8mfKyqHx0mlusvI9kkUWHnqxsF57XjQL1syHhQqoX/bMPX1RLZDbm2hN8PSrV02/65hL8j3SPiLL3kFBLMkdx4hOdCQHCh8bOplgLYUvBly5aBpmnMnDkT8+fP7/Wxl1QV/L8VP0ZJOOyYAWkx5+Z5Ch3favNCF6q+4HKAej2W8HGbKLR1ylSTcVUpHhpaSh2ZhyiszVizFsEgIx4LAIfHD5vbD4/Xz3sPRJUj3O+NFGzOrv9VaiWbZ+Lzw2LQQLYiDmxo9VR9R8R2LAYKUAGtnT64vX54fQEkxuhg0Z2DYd3TNZeZM7NBw4YdPGHn3Yu+JHNmpODw+NHppOHy+BFj1iI+SgME5I8/5/UpqJayGLUwG9QwalTdH3c+itpylYzoobqxu3WJrrEYdRQYRRDBYKhaqpu2wtdqr9d9T+CqpegADJQq8prvrm1BtZRBR8Hu8iHGpP3FsDQTz81Ph+7m9mdl3JwPiHFzaUDm8sKBzOWFA5nLCwsynz8exLj56fBfEZYiICAgICAgILgQIMYNAQEBAQEBQZ8CMW4ICAgICAgI+hSIcUNAQEBAQEDQp0CMGwICAgICAoI+BWLcEBAQEBAQEPQpEOOGgICAgICAoE+BGDcEBAQEBAQEfQrEuCEgICAgICDoUyDGDQEBAQEBAUGfAjFuCAgICAgICPoUiHFDQEBAQEBA0KdAjBsCAgICAgKCPgX1pR7AhYJSqfhJf08QGWQuLxzIXF44kLm8sCDzSfDfBAXDMERPnYCAgICAgKDPgISlCAgICAgICPoUiHFDQEBAQEBA0KdAjBsCAgICAgKCPgVi3BAQEBAQEBD0KRDjhoCAgICAgKBPgRg3BAQEBAQEBH0KxLghICAgICAg6FMgxg0BAQEBAQFBnwIxbggICAgICAj6FH5xxs2GDRtw44034vrrr8cHH3xwqYfzs8Err7yCqVOnYurUqXj22WcBADt37sS0adNw/fXX44UXXuB/e/ToURQXF2Py5Mn43e9+B7/fDwA4e/Ysbr31Vtxwww1YvHgxnE4nAMBms2HBggWYMmUKbr31VrS0tFz8E7wEeOaZZ/Dwww8DIHN5Pti6dSuKi4sxZcoUPPXUUwDIfP5YlJeX8/v8mWeeAUDmkqCPgvkFobGxkbnmmmuYjo4Oxul0MtOmTWOqqqou9bAuOb799lumtLSU8Xq9jM/nY26//XZmw4YNzKRJk5ja2lqGpmnm7rvvZrZt28YwDMNMnTqVOXDgAMMwDPPII48wH3zwAcMwDLNgwQJm48aNDMMwzCuvvMI8++yzDMMwzJNPPsksW7aMYRiGWb9+PbN06dKLe4KXADt37mTGjh3LPPTQQ4zb7SZz+SNRW1vLXHnllUxDQwPj8/mY2bNnM9u2bSPz+SPgcrmY/Px8pq2tjaFpmpk5cybz5Zdfkrkk6JP4RXludu7ciXHjxiE6OhoGgwGTJ0/G5s2bL/WwLjkSEhLw8MMPQ6PRgKIoDBo0CKdPn0ZmZibS09OhVqsxbdo0bN68GWfOnIHH40FeXh4AoLi4GJs3bwZN09izZw8mT54s+hwAtm3bhmnTpgEAbrrpJnz99degafqSnOvFgNVqxQsvvIBFixYBAA4dOkTm8keioqICN954I5KTk0FRFF544QXo9Xoynz8CgUAAwWAQbrcbfr8ffr8fJpOJzCVBn8Qvyrhpbm5GQkIC/39iYiKampou4Yh+HhgyZAh/Ezt9+jQ+++wzKBQK2bkKn8OEhAQ0NTWho6MDJpMJarVa9Dkgnne1Wg2TyYT29vaLdHYXH7///e9x//33w2KxAIi87shc9oyamhoEAgEsWrQIRUVFWLlyJZnPHwmTyYSlS5diypQpmDRpEvr160fmkqDP4hdl3ASDQSgUCv5/hmFE///SUVVVhbvvvhsPPvgg0tPTZecq0hzKzWWkuWUYBkpl31x6H330EVJSUjB+/Hj+s0hzRuayZwQCAezatQtPP/00Vq9ejUOHDqGuro7M54/AsWPHsHbtWnz11VfYsWMHlEolTp8+TeaSoE9CfakHcDGRnJyMvXv38v+3tLQgMTHxEo7o54N9+/ZhyZIlePTRRzF16lR89913ooRAbq6Sk5NFn7e2tiIxMRGxsbGw2+0IBAJQqVSiuU1MTERrayuSk5Ph9/vhdDoRHR19sU/xouDTTz9FS0sLioqK0NnZCZfLhTNnzkClUvG/IXPZe8THx2P8+PGIjY0FABQUFGDz5s1kPn8EvvnmG4wfPx5xcXEA2JDS22+/TeaSoE/iF2VWT5gwAbt27UJ7ezvcbje++OILXHXVVZd6WJccDQ0N+PWvf43nn38eU6dOBQCMHDkSp06d4sMCGzduxFVXXYV+/fpBq9Vi3759ANjqi6uuugoURWHMmDH49NNPAQCffPIJP7eTJk3CJ598AoB9+I8ZMwYURV38E70IePfdd7Fx40aUl5djyZIluPbaa/HWW2+RufyRuOaaa/DNN9/AZrMhEAhgx44duOGGG8h8/ghkZ2dj586dcLlcYBgGW7duJfucoM9CwTAMc6kHcTGxYcMGLFu2DDRNY+bMmZg/f/6lHtIlx1NPPYW1a9ciIyOD/6ysrAz9+/fHX/7yF3i9XkyaNAmPPPIIFAoFjh07hsceewwOhwM5OTn4y1/+Ao1GgzNnzuDhhx9GW1sbUlJS8Le//Q1RUVGwWq14+OGHUVdXB7PZjOeffx5paWmX8IwvDtatW4fvvvsOf/3rX7Fr1y4ylz8SH3/8Md577z3QNI1f/epXeOyxx7B7924ynz8Cb7zxBtatWweKopCbm4s//OEP2L9/P5lLgj6HX5xxQ0BAQEBAQNC38YsKSxEQEBAQEBD0fRDjhoCAgICAgKBPgRg3BAQEBAQEBH0KxLghICAgICAg6FMgxg0BAQEBAQFBnwIxbggI+gCuvfZaHD58+FIPg4CAgOBnAWLcEBAQEBAQEPQp/KLkFwgI+jpyc3OxYMECfPvtt2hubsa8efMwZ84cAMCyZcuwfv16qNVqZGZm4q9//SvMZjNeffVVbNq0CSqVCgMGDMDjjz+OhIQE3HbbbcjJycHBgwfR3t6OkpIStLa24rvvvoPb7cbf//53DB06FHa7HX/+859RWVkJmqYxfvx4PPjgg7y4IgEBAcHFBvHcEBD0Ifh8PsTExGDVqlV46aWXeObZL7/8EuvWrcPq1auxceNGpKWlYcWKFVi7di127NiBjz/+GBs2bMCQIUPw8MMP8+2dOXMGq1atwnPPPYfnnnsOV1xxBdatW4eJEydixYoVAICnn34aOTk5WLduHT755BN0dHTg3XffvVRTQEBAQEA8NwQEfQ3XXXcdACAnJwc+nw8ulwu7du3CDTfcgKioKADAI488AgBYunQpiouLYTAYAAC33347Xn/9dfh8PgBAYWEhACA9PR0AMHHiRABARkYGvvvuOwDAtm3bcPjwYXz88ccAAI/HczFOk4CAgCAiiHFDQNDHoNVqAQAKhQIAwDAMVCoV/z8A2Gw22Gw2BINB0efBYBB+v5//X6PRiNqWE0IMBoN48cUXMWjQIL5tYZsEBAQEFxskLEVA8AvAhAkTUFFRAYfDAQB4+eWX8d5772HixIlYu3YtXC4XAOD9999Hfn6+xKjpDldeeSXee+89MAwDn8+HxYsX8yErAgICgksB4rkhIPgFYNKkSThx4gRmz54NABg8eDD+9Kc/wWAwoKGhAbNmzUIwGERmZiaef/75c2r7d7/7Hf785z9j2rRpoGkaEyZMwLx5836K0yAgICDoFYgqOAEBAQEBAUGfAglLERAQEBAQEPQpEOOGgICAgICAoE+BGDcEBAQEBAQEfQrEuCEgICAgICDoUyDGDQEBAQEBAUGfAjFuCAgICAgICPoUiHFDQEBAQEBA0KdAjBsCAgICAgKCPoX/D/hk2LQy2ATnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(2, 6))\n",
    "sb.jointplot(data = customerData_Clean, x = \"Income\", y = \"MntGoldProds\", height = 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a815e95",
   "metadata": {},
   "source": [
    "##  <a name=\"removeOutliersWeb\"></a>(9) Data Cleaning: Removing outliers in NumWebVisitsMonth Column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e519d36",
   "metadata": {},
   "source": [
    "As mentioned in the [exploratory](Data-exploratory.ipynb#NumWebVisitsMonth) notebook, there are some outliers in this variable.  \n",
    "As there are not a lot, we decide to just remove them to help with the training later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1dc13c43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NumWebVisitsMonth : 15\n",
      "7     386\n",
      "8     338\n",
      "6     334\n",
      "5     277\n",
      "4     215\n",
      "3     201\n",
      "2     195\n",
      "1     138\n",
      "9      82\n",
      "0       5\n",
      "20      3\n",
      "10      3\n",
      "14      2\n",
      "19      2\n",
      "13      1\n",
      "Name: NumWebVisitsMonth, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 345.6x460.8 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogramCountOld(customerData_Clean, \"NumWebVisitsMonth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b1688736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1950</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>5648.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1968</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>41728.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>1986</td>\n",
       "      <td>Master</td>\n",
       "      <td>Single</td>\n",
       "      <td>21888.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>88</td>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>58</td>\n",
       "      <td>12</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>1957</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>6835.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>107</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>1977</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>85072.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>494</td>\n",
       "      <td>92</td>\n",
       "      <td>391</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>677</td>\n",
       "      <td>18</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>1962</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>83715.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>318</td>\n",
       "      <td>8</td>\n",
       "      <td>407</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>565</td>\n",
       "      <td>24</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>4861.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>1991</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>8028.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>73</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>91</td>\n",
       "      <td>16</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>1971</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>1973</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>3502.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1736</th>\n",
       "      <td>1952</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>85431.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>376</td>\n",
       "      <td>53</td>\n",
       "      <td>462</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>683</td>\n",
       "      <td>17</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1808</th>\n",
       "      <td>1963</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>4023.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1960</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>1971</td>\n",
       "      <td>Master</td>\n",
       "      <td>Married</td>\n",
       "      <td>30538.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>284</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>60</td>\n",
       "      <td>17</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>1960</td>\n",
       "      <td>2n Cycle</td>\n",
       "      <td>Married</td>\n",
       "      <td>92556.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>493</td>\n",
       "      <td>183</td>\n",
       "      <td>352</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>719</td>\n",
       "      <td>21</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>1950</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>76842.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>605</td>\n",
       "      <td>10</td>\n",
       "      <td>345</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>439</td>\n",
       "      <td>17</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>1976</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>5305.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "9           1950         PhD        Married   5648.0        1         1   \n",
       "43          1968  Graduation        Married  41728.0        1         0   \n",
       "584         1986      Master         Single  21888.0        1         0   \n",
       "748         1957         PhD        Married   6835.0        0         1   \n",
       "879         1977  Graduation         Single  85072.0        0         0   \n",
       "948         1962  Graduation        Married  83715.0        0         0   \n",
       "954         1965  Graduation       Divorced   4861.0        0         0   \n",
       "1014        1991  Graduation         Single   8028.0        0         0   \n",
       "1216        1971  Graduation       Divorced   1730.0        0         0   \n",
       "1488        1973  Graduation         Single   3502.0        1         0   \n",
       "1736        1952         PhD        Married  85431.0        0         0   \n",
       "1808        1963         PhD        Married   4023.0        1         1   \n",
       "1830        1971      Master        Married  30538.0        1         0   \n",
       "1871        1960    2n Cycle        Married  92556.0        0         0   \n",
       "1916        1950         PhD       Divorced  76842.0        0         0   \n",
       "2158        1976  Graduation        Married   5305.0        0         1   \n",
       "\n",
       "      Recency  MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "9          68        28          0                6  ...                  1   \n",
       "43         92        13          6               15  ...                  1   \n",
       "584        15        88         10               46  ...                  4   \n",
       "748        76       107          2               12  ...                  0   \n",
       "879        94       494         92              391  ...                  1   \n",
       "948         2       318          8              407  ...                  1   \n",
       "954        20         2          1                1  ...                  0   \n",
       "1014       62        73         18               66  ...                 15   \n",
       "1216       65         1          1                3  ...                 15   \n",
       "1488       56         2          1                1  ...                  0   \n",
       "1736       54       376         53              462  ...                  1   \n",
       "1808       29         5          0                1  ...                 15   \n",
       "1830       27       284          0               52  ...                  4   \n",
       "1871       32       493        183              352  ...                  0   \n",
       "1916       37       605         10              345  ...                  1   \n",
       "2158       12        12          4                7  ...                  0   \n",
       "\n",
       "      NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "9                   1                    0                  0   \n",
       "43                  2                    0                  2   \n",
       "584                 5                    1                  2   \n",
       "748                 0                    0                  1   \n",
       "879                 3                    4                 10   \n",
       "948                 2                    8                 13   \n",
       "954                 0                    0                  0   \n",
       "1014                0                    1                  0   \n",
       "1216                0                    0                  0   \n",
       "1488                0                    0                  0   \n",
       "1736                2                    7                  7   \n",
       "1808                0                    0                  0   \n",
       "1830                9                    0                  4   \n",
       "1871                3                    5                 13   \n",
       "1916                1                    8                  7   \n",
       "2158                1                    0                  0   \n",
       "\n",
       "      NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "9                    20                   7              2       1950   \n",
       "43                   10                  24              5       1960   \n",
       "584                  10                  58             12       1980   \n",
       "748                  20                  16              1       1950   \n",
       "879                   0                 677             18       1970   \n",
       "948                   0                 565             24       1960   \n",
       "954                  14                   3              0       1960   \n",
       "1014                 19                  91             16       1990   \n",
       "1216                 20                   5             15       1970   \n",
       "1488                 14                   2              0       1970   \n",
       "1736                  0                 683             17       1950   \n",
       "1808                 19                   2             15       1960   \n",
       "1830                 10                  60             17       1970   \n",
       "1871                  0                 719             21       1960   \n",
       "1916                  0                 439             17       1950   \n",
       "2158                 13                  12              1       1970   \n",
       "\n",
       "      TotalChild  HaveChild  \n",
       "9              2          1  \n",
       "43             1          1  \n",
       "584            1          1  \n",
       "748            1          1  \n",
       "879            0          0  \n",
       "948            0          0  \n",
       "954            0          0  \n",
       "1014           0          0  \n",
       "1216           0          0  \n",
       "1488           1          1  \n",
       "1736           0          0  \n",
       "1808           2          1  \n",
       "1830           1          1  \n",
       "1871           0          0  \n",
       "1916           0          0  \n",
       "2158           1          1  \n",
       "\n",
       "[16 rows x 23 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_Web = customerData_Clean.loc[(customerData_Clean.NumWebVisitsMonth >= 10) | (customerData_Clean.NumWebVisitsMonth == 0)]\n",
    "outliers_Web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ba7d18a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9, 43, 584, 748, 879, 948, 954, 1014, 1216, 1488, 1736, 1808, 1830, 1871, 1916, 2158]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>88</td>\n",
       "      <td>546</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>806</td>\n",
       "      <td>25</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>49</td>\n",
       "      <td>127</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>287</td>\n",
       "      <td>21</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>118</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>207</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>43</td>\n",
       "      <td>182</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>267</td>\n",
       "      <td>18</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2162</th>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1940</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2163</th>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>48</td>\n",
       "      <td>217</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>297</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2164</th>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Married</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>30</td>\n",
       "      <td>214</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2165</th>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2166 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0           1957  Graduation         Single  58138.0        0         0   \n",
       "1           1954  Graduation         Single  46344.0        1         1   \n",
       "2           1965  Graduation        Married  71613.0        0         0   \n",
       "3           1984  Graduation        Married  26646.0        1         0   \n",
       "4           1981         PhD        Married  58293.0        1         0   \n",
       "...          ...         ...            ...      ...      ...       ...   \n",
       "2161        1967  Graduation        Married  61223.0        0         1   \n",
       "2162        1946         PhD        Married  64014.0        2         1   \n",
       "2163        1981  Graduation       Divorced  56981.0        0         0   \n",
       "2164        1956      Master        Married  69245.0        0         1   \n",
       "2165        1954         PhD        Married  52869.0        1         1   \n",
       "\n",
       "      Recency  MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "0          58       635         88              546  ...                  3   \n",
       "1          38        11          1                6  ...                  2   \n",
       "2          26       426         49              127  ...                  1   \n",
       "3          26        11          4               20  ...                  2   \n",
       "4          94       173         43              118  ...                  5   \n",
       "...       ...       ...        ...              ...  ...                ...   \n",
       "2161       46       709         43              182  ...                  2   \n",
       "2162       56       406          0               30  ...                  7   \n",
       "2163       91       908         48              217  ...                  1   \n",
       "2164        8       428         30              214  ...                  2   \n",
       "2165       40        84          3               61  ...                  3   \n",
       "\n",
       "      NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "0                   8                   10                  4   \n",
       "1                   1                    1                  2   \n",
       "2                   8                    2                 10   \n",
       "3                   2                    0                  4   \n",
       "4                   5                    3                  6   \n",
       "...               ...                  ...                ...   \n",
       "2161                9                    3                  4   \n",
       "2162                8                    2                  5   \n",
       "2163                2                    3                 13   \n",
       "2164                6                    5                 10   \n",
       "2165                3                    1                  4   \n",
       "\n",
       "      NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "0                     7                 806             25       1950   \n",
       "1                     5                   9              6       1950   \n",
       "2                     4                 287             21       1960   \n",
       "3                     6                  34              8       1980   \n",
       "4                     5                 207             19       1980   \n",
       "...                 ...                 ...            ...        ...   \n",
       "2161                  5                 267             18       1960   \n",
       "2162                  7                  30             22       1940   \n",
       "2163                  6                 297             19       1980   \n",
       "2164                  3                 324             23       1950   \n",
       "2165                  7                  66             11       1950   \n",
       "\n",
       "      TotalChild  HaveChild  \n",
       "0              0          0  \n",
       "1              2          1  \n",
       "2              0          0  \n",
       "3              1          1  \n",
       "4              1          1  \n",
       "...          ...        ...  \n",
       "2161           1          1  \n",
       "2162           3          1  \n",
       "2163           0          0  \n",
       "2164           1          1  \n",
       "2165           2          1  \n",
       "\n",
       "[2166 rows x 23 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(outliers_Web.index.tolist())\n",
    "customerData_Clean.drop(index = outliers_Web.index.tolist(),inplace=True)\n",
    "customerData_Clean.reset_index(drop=True, inplace=True)\n",
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0ef30887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NumWebVisitsMonth : 9\n",
      "7    386\n",
      "8    338\n",
      "6    334\n",
      "5    277\n",
      "4    215\n",
      "3    201\n",
      "2    195\n",
      "1    138\n",
      "9     82\n",
      "Name: NumWebVisitsMonth, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 345.6x460.8 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogramCountOld(customerData_Clean, \"NumWebVisitsMonth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc8068e0",
   "metadata": {},
   "source": [
    "## Cleaned Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9b1ca140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>YearRange</th>\n",
       "      <th>TotalChild</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1957</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>88</td>\n",
       "      <td>546</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>806</td>\n",
       "      <td>25</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>49</td>\n",
       "      <td>127</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>287</td>\n",
       "      <td>21</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>118</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>207</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>61223.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>709</td>\n",
       "      <td>43</td>\n",
       "      <td>182</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>267</td>\n",
       "      <td>18</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2162</th>\n",
       "      <td>1946</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>64014.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1940</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2163</th>\n",
       "      <td>1981</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>56981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>908</td>\n",
       "      <td>48</td>\n",
       "      <td>217</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>297</td>\n",
       "      <td>19</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2164</th>\n",
       "      <td>1956</td>\n",
       "      <td>Master</td>\n",
       "      <td>Married</td>\n",
       "      <td>69245.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>428</td>\n",
       "      <td>30</td>\n",
       "      <td>214</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>324</td>\n",
       "      <td>23</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2165</th>\n",
       "      <td>1954</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>52869.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2166 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0           1957  Graduation         Single  58138.0        0         0   \n",
       "1           1954  Graduation         Single  46344.0        1         1   \n",
       "2           1965  Graduation        Married  71613.0        0         0   \n",
       "3           1984  Graduation        Married  26646.0        1         0   \n",
       "4           1981         PhD        Married  58293.0        1         0   \n",
       "...          ...         ...            ...      ...      ...       ...   \n",
       "2161        1967  Graduation        Married  61223.0        0         1   \n",
       "2162        1946         PhD        Married  64014.0        2         1   \n",
       "2163        1981  Graduation       Divorced  56981.0        0         0   \n",
       "2164        1956      Master        Married  69245.0        0         1   \n",
       "2165        1954         PhD        Married  52869.0        1         1   \n",
       "\n",
       "      Recency  MntWines  MntFruits  MntMeatProducts  ...  NumDealsPurchases  \\\n",
       "0          58       635         88              546  ...                  3   \n",
       "1          38        11          1                6  ...                  2   \n",
       "2          26       426         49              127  ...                  1   \n",
       "3          26        11          4               20  ...                  2   \n",
       "4          94       173         43              118  ...                  5   \n",
       "...       ...       ...        ...              ...  ...                ...   \n",
       "2161       46       709         43              182  ...                  2   \n",
       "2162       56       406          0               30  ...                  7   \n",
       "2163       91       908         48              217  ...                  1   \n",
       "2164        8       428         30              214  ...                  2   \n",
       "2165       40        84          3               61  ...                  3   \n",
       "\n",
       "      NumWebPurchases  NumCatalogPurchases  NumStorePurchases  \\\n",
       "0                   8                   10                  4   \n",
       "1                   1                    1                  2   \n",
       "2                   8                    2                 10   \n",
       "3                   2                    0                  4   \n",
       "4                   5                    3                  6   \n",
       "...               ...                  ...                ...   \n",
       "2161                9                    3                  4   \n",
       "2162                8                    2                  5   \n",
       "2163                2                    3                 13   \n",
       "2164                6                    5                 10   \n",
       "2165                3                    1                  4   \n",
       "\n",
       "      NumWebVisitsMonth  MntGroceryProducts  TotalPurchase  YearRange  \\\n",
       "0                     7                 806             25       1950   \n",
       "1                     5                   9              6       1950   \n",
       "2                     4                 287             21       1960   \n",
       "3                     6                  34              8       1980   \n",
       "4                     5                 207             19       1980   \n",
       "...                 ...                 ...            ...        ...   \n",
       "2161                  5                 267             18       1960   \n",
       "2162                  7                  30             22       1940   \n",
       "2163                  6                 297             19       1980   \n",
       "2164                  3                 324             23       1950   \n",
       "2165                  7                  66             11       1950   \n",
       "\n",
       "      TotalChild  HaveChild  \n",
       "0              0          0  \n",
       "1              2          1  \n",
       "2              0          0  \n",
       "3              1          1  \n",
       "4              1          1  \n",
       "...          ...        ...  \n",
       "2161           1          1  \n",
       "2162           3          1  \n",
       "2163           0          0  \n",
       "2164           1          1  \n",
       "2165           2          1  \n",
       "\n",
       "[2166 rows x 23 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerData_Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1e512fc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2166 entries, 0 to 2165\n",
      "Data columns (total 23 columns):\n",
      " #   Column               Non-Null Count  Dtype   \n",
      "---  ------               --------------  -----   \n",
      " 0   Year_Birth           2166 non-null   int64   \n",
      " 1   Education            2166 non-null   object  \n",
      " 2   Marital_Status       2166 non-null   object  \n",
      " 3   Income               2166 non-null   float64 \n",
      " 4   Kidhome              2166 non-null   int64   \n",
      " 5   Teenhome             2166 non-null   int64   \n",
      " 6   Recency              2166 non-null   int64   \n",
      " 7   MntWines             2166 non-null   int64   \n",
      " 8   MntFruits            2166 non-null   int64   \n",
      " 9   MntMeatProducts      2166 non-null   int64   \n",
      " 10  MntFishProducts      2166 non-null   int64   \n",
      " 11  MntSweetProducts     2166 non-null   int64   \n",
      " 12  MntGoldProds         2166 non-null   int64   \n",
      " 13  NumDealsPurchases    2166 non-null   int64   \n",
      " 14  NumWebPurchases      2166 non-null   int64   \n",
      " 15  NumCatalogPurchases  2166 non-null   int64   \n",
      " 16  NumStorePurchases    2166 non-null   int64   \n",
      " 17  NumWebVisitsMonth    2166 non-null   int64   \n",
      " 18  MntGroceryProducts   2166 non-null   int64   \n",
      " 19  TotalPurchase        2166 non-null   int64   \n",
      " 20  YearRange            2166 non-null   category\n",
      " 21  TotalChild           2166 non-null   int64   \n",
      " 22  HaveChild            2166 non-null   int64   \n",
      "dtypes: category(1), float64(1), int64(19), object(2)\n",
      "memory usage: 374.9+ KB\n"
     ]
    }
   ],
   "source": [
    "customerData_Clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "faacdd4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Income</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>MntFishProducts</th>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <th>MntGoldProds</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>MntGroceryProducts</th>\n",
       "      <th>TotalPurchase</th>\n",
       "      <th>TotalChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
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       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1968.880425</td>\n",
       "      <td>51485.832410</td>\n",
       "      <td>49.157433</td>\n",
       "      <td>304.746076</td>\n",
       "      <td>26.234995</td>\n",
       "      <td>162.832410</td>\n",
       "      <td>37.174515</td>\n",
       "      <td>26.777008</td>\n",
       "      <td>43.479686</td>\n",
       "      <td>2.316251</td>\n",
       "      <td>4.077101</td>\n",
       "      <td>2.613112</td>\n",
       "      <td>5.831948</td>\n",
       "      <td>5.320406</td>\n",
       "      <td>226.241921</td>\n",
       "      <td>14.838412</td>\n",
       "      <td>0.957064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.686277</td>\n",
       "      <td>20127.393997</td>\n",
       "      <td>28.890662</td>\n",
       "      <td>336.685171</td>\n",
       "      <td>39.551197</td>\n",
       "      <td>213.314447</td>\n",
       "      <td>54.123262</td>\n",
       "      <td>40.469013</td>\n",
       "      <td>50.512398</td>\n",
       "      <td>1.806630</td>\n",
       "      <td>2.623057</td>\n",
       "      <td>2.725789</td>\n",
       "      <td>3.213197</td>\n",
       "      <td>2.253213</td>\n",
       "      <td>276.225962</td>\n",
       "      <td>7.570376</td>\n",
       "      <td>0.747901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1940.000000</td>\n",
       "      <td>7500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1959.000000</td>\n",
       "      <td>35352.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>51222.500000</td>\n",
       "      <td>49.500000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1977.000000</td>\n",
       "      <td>67866.250000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>505.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>227.750000</td>\n",
       "      <td>49.750000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1996.000000</td>\n",
       "      <td>94384.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1306.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Year_Birth        Income      Recency     MntWines    MntFruits  \\\n",
       "count  2166.000000   2166.000000  2166.000000  2166.000000  2166.000000   \n",
       "mean   1968.880425  51485.832410    49.157433   304.746076    26.234995   \n",
       "std      11.686277  20127.393997    28.890662   336.685171    39.551197   \n",
       "min    1940.000000   7500.000000     0.000000     0.000000     0.000000   \n",
       "25%    1959.000000  35352.000000    24.000000    24.000000     2.000000   \n",
       "50%    1970.000000  51222.500000    49.500000   176.000000     8.000000   \n",
       "75%    1977.000000  67866.250000    74.000000   505.000000    33.000000   \n",
       "max    1996.000000  94384.000000    99.000000  1493.000000   199.000000   \n",
       "\n",
       "       MntMeatProducts  MntFishProducts  MntSweetProducts  MntGoldProds  \\\n",
       "count      2166.000000      2166.000000       2166.000000   2166.000000   \n",
       "mean        162.832410        37.174515         26.777008     43.479686   \n",
       "std         213.314447        54.123262         40.469013     50.512398   \n",
       "min           0.000000         0.000000          0.000000      0.000000   \n",
       "25%          16.000000         3.000000          1.000000      9.000000   \n",
       "50%          68.000000        12.000000          8.000000     25.000000   \n",
       "75%         227.750000        49.750000         33.000000     56.000000   \n",
       "max         984.000000       259.000000        198.000000    249.000000   \n",
       "\n",
       "       NumDealsPurchases  NumWebPurchases  NumCatalogPurchases  \\\n",
       "count        2166.000000      2166.000000          2166.000000   \n",
       "mean            2.316251         4.077101             2.613112   \n",
       "std             1.806630         2.623057             2.725789   \n",
       "min             0.000000         0.000000             0.000000   \n",
       "25%             1.000000         2.000000             0.000000   \n",
       "50%             2.000000         4.000000             2.000000   \n",
       "75%             3.000000         6.000000             4.000000   \n",
       "max            15.000000        11.000000            11.000000   \n",
       "\n",
       "       NumStorePurchases  NumWebVisitsMonth  MntGroceryProducts  \\\n",
       "count        2166.000000        2166.000000         2166.000000   \n",
       "mean            5.831948           5.320406          226.241921   \n",
       "std             3.213197           2.253213          276.225962   \n",
       "min             2.000000           1.000000            1.000000   \n",
       "25%             3.000000           4.000000           25.000000   \n",
       "50%             5.000000           6.000000           90.000000   \n",
       "75%             8.000000           7.000000          351.000000   \n",
       "max            13.000000           9.000000         1306.000000   \n",
       "\n",
       "       TotalPurchase   TotalChild  \n",
       "count    2166.000000  2166.000000  \n",
       "mean       14.838412     0.957064  \n",
       "std         7.570376     0.747901  \n",
       "min         4.000000     0.000000  \n",
       "25%         8.000000     0.000000  \n",
       "50%        15.000000     1.000000  \n",
       "75%        21.000000     1.000000  \n",
       "max        39.000000     3.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerData_Clean[colOfNum].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f10ed640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>HaveChild</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "      <td>2166.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.446907</td>\n",
       "      <td>0.510157</td>\n",
       "      <td>0.721145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.538321</td>\n",
       "      <td>0.543395</td>\n",
       "      <td>0.448540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Kidhome     Teenhome    HaveChild\n",
       "count  2166.000000  2166.000000  2166.000000\n",
       "mean      0.446907     0.510157     0.721145\n",
       "std       0.538321     0.543395     0.448540\n",
       "min       0.000000     0.000000     0.000000\n",
       "25%       0.000000     0.000000     0.000000\n",
       "50%       0.000000     0.000000     1.000000\n",
       "75%       1.000000     1.000000     1.000000\n",
       "max       2.000000     2.000000     1.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customerData_Clean[colOfCat].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7e5532a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## customerData_Clean.to_csv('dataset/customerData_Clean.csv')  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fec5ce6d",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54084813",
   "metadata": {},
   "source": [
    "With that, we have came to the end of this notebook.  \n",
    "Move on to Data-visualization notebook to see get more insights from our dataset, before moving to machine learning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85fd3bc1",
   "metadata": {},
   "source": [
    "<div style=\"font-size: 20px;\">\n",
    "<span style = \"float: left;\"><a href=\"Data-exploratory.ipynb\">&lt;- Data-exploratory</a></span>\n",
    "<span style = \"float: right;\"><a href=\"Data-visualization.ipynb\">Data-visualization -></a></span>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a7dea9",
   "metadata": {},
   "source": [
    "# References\n",
    "* [Source of Dataset (Kaggle)](https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis)\n",
    "* [M2 BasicStatistics](https://ntulearn.ntu.edu.sg/webapps/blackboard/content/listContent.jsp?course_id=_2606895_1&content_id=_2762960_1)"
   ]
  }
 ],
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