03-classification/homework_03_till_meineke.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework 03 Till Meineke\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import (\n",
    "    mutual_info_score,\n",
    "    accuracy_score,\n",
    "    root_mean_squared_error,\n",
    ")  # mean_squared_error\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this homework, we will use the Bank Marketing dataset. Download it from [here](https://archive.ics.uci.edu/static/public/222/bank+marketing.zip).\n",
    "\n",
    "Or you can do it with `wget`:\n",
    "\n",
    "```bash\n",
    "wget https://archive.ics.uci.edu/static/public/222/bank+marketing.zip\n",
    "```\n",
    "\n",
    "We need to take `bank/bank-full.csv` file from the downloaded zip-file.\n",
    "In this dataset our desired target for classification task will be `y` variable - has the client subscribed a term deposit or not.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Variable Information\n",
    "\n",
    "**Input variables - bank client data:**\n",
    "\n",
    "1. age (numeric)\n",
    "2. job : type of job (categorical: \"admin.\",\"unknown\",\"unemployed\",\"management\",\"housemaid\",\"entrepreneur\",\"student\",\"blue-collar\",\"self-employed\",\"retired\",\"technician\",\"services\")\n",
    "3. marital : marital status (categorical: \"married\",\"divorced\",\"single\"; note: \"divorced\" means divorced or widowed)\n",
    "4. education (categorical: \"unknown\",\"secondary\",\"primary\",\"tertiary\")\n",
    "5. default: has credit in default? (binary: \"yes\",\"no\")\n",
    "6. balance: average yearly balance, in euros (numeric)\n",
    "7. housing: has housing loan? (binary: \"yes\",\"no\")\n",
    "8. loan: has personal loan? (binary: \"yes\",\"no\")\n",
    "\n",
    "**related with the last contact of the current campaign:**\n",
    "\n",
    "9. contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\")\n",
    "10. day: last contact day of the month (numeric)\n",
    "11. month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n",
    "12. duration: last contact duration, in seconds (numeric)\n",
    "\n",
    "**other attributes:**\n",
    "\n",
    "13. campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "14. pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)\n",
    "15. previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "16. poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\")\n",
    "\n",
    "**Output variable (desired target):**\n",
    "\n",
    "17. y - has the client subscribed a term deposit? (binary: \"yes\",\"no\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# URL = \"https://archive.ics.uci.edu/static/public/222/bank+marketing.zip\"\n",
    "\n",
    "# !wget $URL -O ./data/bank+marketing.zip\n",
    "# !unzip ./data/bank+marketing.zip -d ./data\n",
    "# !unzip ./data/bank.zip -d ./data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45211, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./data/bank-full.csv\", sep=\";\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   age        45211 non-null  int64 \n",
      " 1   job        45211 non-null  object\n",
      " 2   marital    45211 non-null  object\n",
      " 3   education  45211 non-null  object\n",
      " 4   default    45211 non-null  object\n",
      " 5   balance    45211 non-null  int64 \n",
      " 6   housing    45211 non-null  object\n",
      " 7   loan       45211 non-null  object\n",
      " 8   contact    45211 non-null  object\n",
      " 9   day        45211 non-null  int64 \n",
      " 10  month      45211 non-null  object\n",
      " 11  duration   45211 non-null  int64 \n",
      " 12  campaign   45211 non-null  int64 \n",
      " 13  pdays      45211 non-null  int64 \n",
      " 14  previous   45211 non-null  int64 \n",
      " 15  poutcome   45211 non-null  object\n",
      " 16  y          45211 non-null  object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the rest of the homework, you'll need to use only these columns:\n",
    "\n",
    "`age`,\n",
    "`job`,\n",
    "`marital`,\n",
    "`education`,\n",
    "`balance`,\n",
    "`housing`,\n",
    "`contact`,\n",
    "`day`,\n",
    "`month`,\n",
    "`duration`,\n",
    "`campaign`,\n",
    "`pdays`,\n",
    "`previous`,\n",
    "`poutcome`,\n",
    "`y`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Select only the features from above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
       "      <td>58</td>\n",
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       "      <td>33</td>\n",
       "      <td>47</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>42</td>\n",
       "      <td>58</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>management</td>\n",
       "      <td>technician</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>unknown</td>\n",
       "      <td>management</td>\n",
       "      <td>management</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>retired</td>\n",
       "      <td>technician</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>married</td>\n",
       "      <td>single</td>\n",
       "      <td>married</td>\n",
       "      <td>married</td>\n",
       "      <td>single</td>\n",
       "      <td>married</td>\n",
       "      <td>single</td>\n",
       "      <td>divorced</td>\n",
       "      <td>married</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>tertiary</td>\n",
       "      <td>secondary</td>\n",
       "      <td>secondary</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>primary</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>2143</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>231</td>\n",
       "      <td>447</td>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>261</td>\n",
       "      <td>151</td>\n",
       "      <td>76</td>\n",
       "      <td>92</td>\n",
       "      <td>198</td>\n",
       "      <td>139</td>\n",
       "      <td>217</td>\n",
       "      <td>380</td>\n",
       "      <td>50</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome</th>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0           1             2            3        4  \\\n",
       "age                58          44            33           47       33   \n",
       "job        management  technician  entrepreneur  blue-collar  unknown   \n",
       "marital       married      single       married      married   single   \n",
       "education    tertiary   secondary     secondary      unknown  unknown   \n",
       "balance          2143          29             2         1506        1   \n",
       "housing           yes         yes           yes          yes       no   \n",
       "contact       unknown     unknown       unknown      unknown  unknown   \n",
       "day                 5           5             5            5        5   \n",
       "month             may         may           may          may      may   \n",
       "duration          261         151            76           92      198   \n",
       "campaign            1           1             1            1        1   \n",
       "pdays              -1          -1            -1           -1       -1   \n",
       "previous            0           0             0            0        0   \n",
       "poutcome      unknown     unknown       unknown      unknown  unknown   \n",
       "y                  no          no            no           no       no   \n",
       "\n",
       "                    5           6             7        8           9  \n",
       "age                35          28            42       58          43  \n",
       "job        management  management  entrepreneur  retired  technician  \n",
       "marital       married      single      divorced  married      single  \n",
       "education    tertiary    tertiary      tertiary  primary   secondary  \n",
       "balance           231         447             2      121         593  \n",
       "housing           yes         yes           yes      yes         yes  \n",
       "contact       unknown     unknown       unknown  unknown     unknown  \n",
       "day                 5           5             5        5           5  \n",
       "month             may         may           may      may         may  \n",
       "duration          139         217           380       50          55  \n",
       "campaign            1           1             1        1           1  \n",
       "pdays              -1          -1            -1       -1          -1  \n",
       "previous            0           0             0        0           0  \n",
       "poutcome      unknown     unknown       unknown  unknown     unknown  \n",
       "y                  no          no            no       no          no  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [\n",
    "    \"age\",\n",
    "    \"job\",\n",
    "    \"marital\",\n",
    "    \"education\",\n",
    "    \"balance\",\n",
    "    \"housing\",\n",
    "    \"contact\",\n",
    "    \"day\",\n",
    "    \"month\",\n",
    "    \"duration\",\n",
    "    \"campaign\",\n",
    "    \"pdays\",\n",
    "    \"previous\",\n",
    "    \"poutcome\",\n",
    "    \"y\",\n",
    "]\n",
    "\n",
    "data = df[columns]\n",
    "data.head(10).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            77\n",
       "job            12\n",
       "marital         3\n",
       "education       4\n",
       "balance      7168\n",
       "housing         2\n",
       "contact         3\n",
       "day            31\n",
       "month          12\n",
       "duration     1573\n",
       "campaign       48\n",
       "pdays         559\n",
       "previous       41\n",
       "poutcome        4\n",
       "y               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Check if the missing values are presented in the features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "balance      0\n",
       "housing      0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "y            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the most frequent observation (mode) for the column `education`?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211</td>\n",
       "      <td>45211</td>\n",
       "      <td>45211</td>\n",
       "      <td>45211</td>\n",
       "      <td>45211</td>\n",
       "      <td>45211</td>\n",
       "      <td>45211</td>\n",
       "      <td>45211</td>\n",
       "      <td>45211</td>\n",
       "      <td>45211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>9732</td>\n",
       "      <td>27214</td>\n",
       "      <td>23202</td>\n",
       "      <td>44396</td>\n",
       "      <td>25130</td>\n",
       "      <td>37967</td>\n",
       "      <td>29285</td>\n",
       "      <td>13766</td>\n",
       "      <td>36959</td>\n",
       "      <td>39922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                job  marital  education default housing   loan   contact  \\\n",
       "count         45211    45211      45211   45211   45211  45211     45211   \n",
       "unique           12        3          4       2       2      2         3   \n",
       "top     blue-collar  married  secondary      no     yes     no  cellular   \n",
       "freq           9732    27214      23202   44396   25130  37967     29285   \n",
       "\n",
       "        month poutcome      y  \n",
       "count   45211    45211  45211  \n",
       "unique     12        4      2  \n",
       "top       may  unknown     no  \n",
       "freq    13766    36959  39922  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"O\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "secondary    23202\n",
       "tertiary     13301\n",
       "primary       6851\n",
       "unknown       1857\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.education.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most frequent observation (mode) for the `education` column is: secondary\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"The most frequent observation (mode) for the `education` column is: \"\n",
    "    + str(df[\"education\"].mode()[0])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the [correlation matrix](https://www.google.com/search?q=correlation+matrix) for the numerical features of your dataset.\n",
    "In a correlation matrix, you compute the correlation coefficient between every pair of features in the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_features = [\n",
    "    \"age\",\n",
    "    \"balance\",\n",
    "    \"day\",\n",
    "    \"duration\",\n",
    "    \"campaign\",\n",
    "    \"pdays\",\n",
    "    \"previous\",\n",
    "]\n",
    "\n",
    "corr = df[num_features].corr()\n",
    "# plot as a heatmap\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.heatmap(corr, annot=True, cmap=\"coolwarm\", fmt=\".3f\")\n",
    "plt.xticks(rotation=20)\n",
    "plt.tight_layout()\n",
    "plt.title(\"Heatmap showing correlation between numerical features\", fontsize=20)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6umLfvn1K2588eSLdYdPR0VG6CNqyZYuUh2vXrils27BhgwCye0CdnZ0FALF161aFMLGxsVJ8VTdM8vpB0FYdyC9W9fX1xcmTJ5W2P378WMq/fH8VRkxMjNTYrVq1qrh//77asLm3JSQkSD/W5cqVE/fu3VOKc/78eenCunz58koXzrl7BHv27Kmyh6igYe/duyflrVevXmp7OsePHy99j27cuKG0Pa99XdznLE2N0Dc9boT4rxEKZI8CUJXnsLAwKcy8efPyzHN+yvIm57OhQ4cKILvnUVWjTwgh7t69K+zt7aWLodw0NU7koqKipHB79uxR2Hb8+HEBZDfM5ReCuXsJnzx5Ih1ruS/asrKypAvDWrVqSY2H3Pbs2SOlkfOGi1zbtm0FAOHg4CBu376tMo2cx+TEiROVtsvzLz+vPHjwQCnM8ePHpZ7CYcOGqfwcTXJ+9+UNh9yysrKEv7+/ALIbvKpuemi7EQpk3yTI3Yv5+vVrqYFpamoqDA0NxYgRI5TSefHihXQceXh4qPwsbe+ncuXKib/++ktt2TU18oKDg6Xtn376qXj16pXKdDIzM8XDhw8V1mVlZYmbN2+q/WwhhJg8ebIAsm9GqupdfJNGaEHT0Ebd37lzR+VNILn79+9LIxpUnXOEEAUaxZTX71BO+W2EAhCTJk1Sm050dLR0nlFVfiGyvws9evSQjoecN0z/+ecfaWSVpkamqmtJKrh3shFamEXVhZJ8KNWnn36a52dfvXpVSufAgQMFzntSUpLQ1dUVAMS2bdvyLFtBGqHjxo1TGy7nRdny5ctVhnn58qXUOzBy5MgClUlOU9lyXrR5eHioHU53//596eBv3bp1gfMhvxuqaV+qkjOPn332mdpwy5cvl8JdvHhRYVvOC72vvvpKbRonTpyQwg0ePFhhW3x8vLQt953ofv36CSB7SGKfPn2k/+ck/0HW0dFROLHK5fWDoI06OH36tLQtr+/Trl27FC4WC6Nr167SxcH58+cLFFfeMwRAhIeHqw337bffSuHkQ9vlcl44WFhY5DnssiBhR48eLV2c/fPPP2rDZWRkSBcLEyZMUNqe3x9/dbR5zsqrEaqN40YIxfNd7u+lXFZWlihXrlyhzxO5y1LY89nff/8tnXcXLFiQ5+ctXbpUANk9xC9evFDYlt9G6OvXr4WpqanK3wz5d7xNmzbSyII2bdoohNm5c6f0OZcuXVLYlnMEirp6l+vcubMAIBo2bKiw/vLly1Iau3btyjONsWPHSsdHbjkbN6puwsnJG2Senp55fpY6Ob/7devWVXtDae/evXmWS9uN0NKlS6u9MM75vXVwcFDbEJE3ugAo3cgpiv20fv36PNPJq4GWmZkpnQPLly+f583nwnr9+rWwsbERAMScOXMKlL/8yk8a2qr7/JBfR5iZman8bpdkI7Rq1ap5DhWX92LmdVwKkT2axMDAQACKHVAPHz7Mdz2TdnywExM9fPgQMTExAIDOnTvnGbZGjRqwsbEBAJw+fTrPsBkZGXjw4AGuXbuGK1eu4MqVK3j06BGsra0BABcvXtRC7rN17dpV7TYPDw8A2Q+GqyufkZERqlSpAiB7ohJN3rRsvXr1go6O6q9chQoV4O/vDyD7QfSCTlJkb28PADh27Bhu375doLg5ffnll2q31a1bV/p/7vo6ePCg9P++ffuqTaNhw4aoUaOGUhwAsLW1lV5MLZ/wQk7+t5+fn/TwvrowtWrVgoWFhdo8aFLYOjh06JD0/169eqlNo02bNtJ3pjCysrKkiXOaNGkCT0/PAsWX17uFhQU6deqkNly/fv2U4qjSrl07mJqa5uuzNYXdtWuXFM7Q0FBtuFKlSsHX1xeA5nOSJsV5zspNG8dNTu7u7tK5LzeZTCZ9V/JzvtOksOezffv2SZOHaPrtady4MYDsfST/vSooXV1dNGzYUMpLTqrOKydOnFDIrzyMtbU13NzcFOLLv6/VqlVTW+9y8rKcPXtWIX15GqVLl0abNm3ylcajR49w//59lWEsLCzyTEd+DtPGd6B79+5qJwzK61xZFJo3bw4rKyuV23Lum88++0ztLKy1atWS/n/nzh2FbdreT/r6+vjiiy/yTCcvFy5cwMOHDwEA/fv3h4mJSaHTArJ/Vx49eoQbN25I58Fr166hQoUKAIr2PKiJtuteLjU1FXfu3MHvv/8ulbl06dIK294mXbp0ga6ursptGRkZ0qRjn3/+udrjEsg+R7i7uwNQ/P20traGvr4+AGDDhg14/fq1trJOaryTjdApU6ZAZPfi5rlMmTJFbRrnzp2T/t+tWzeVs3zlXBITEwEA8fHxSmllZGRgyZIl8PHxgYmJCRwcHFCzZk24u7tLS0JCAgBI6WhDXu+UkjdCbGxsYGlpqTHcs2fPVG7XZtnq16+f53YvLy8A2bMsFvRHu2fPngCApKQkuLm5oWvXrli3bh1u3bpVoHTkjUBVcv7A566vK1euAMj+YdXUKPL29gaQPStg7pkImzRpAgDSy6uB7B+TW7duQSaToUmTJtLMpFevXsXff/8NABBC4Pjx4wCgcbZFTd60DgwMDJQuVnPS1dVF7dq1C52/O3fuSLOGyn9wC0KeT09PzzynxLe1tZVm75XHUUXTxXd+wz59+lT6vq5YsULjOWnbtm0AVJ+TNCmpc1Zu2jpu5PL67gL/fX/Vne8KorDns5y/Pfb29nnu45zHUWH2s5z8vBITE4Pnz58DyP4OyGdR9/Pzg7e3N4yMjPD06VOFWdPljdDGjRsrXdjJy3Ljxg2N39ehQ4cCANLT0xVmjZWn8fLlS5QqVSrPNNq2bauxPqpUqaL25gCg3e9AYc+VRSE/1wMFCZc7z0Wxn/K60aZJzu9oYX4HgOzfzbCwMDRt2hQmJiYoX748qlevrnAevHDhAoCiPQ9qos26/+uvv/D111/D0dER5ubmcHZ2hpubm1TeAQMGSGFLssyq5PX7efXqVWnm/qCgII3nI3md5qwjAwMDdOnSBQCwbds2uLi4YOzYsdi9ezeePn1ahCX7cL2TjVBtkF9gFVTu11M8efIEvr6+GDp0KKKiojROb/7PP/8U6nNVkd+xUkX+I5xXmJzhVPU8artsZcuWzXO7ra2twmcXxMcff4zFixfDyMgIr169wpYtW9CnTx9UqVIFFSpUwMCBA/N1JzM/dQoo15c8v1ZWVihVqlSen2FnZwcg+wcwOTlZYZu8ARkfH4/r168DgPQ6jpo1a6JMmTKoUKECnJ2dIYSQGquXLl1CUlISgP8uOAursHUgL4uVlZXau5VyZcqUKXT+cv4wynvAC0K+r3J+39SR76u8vo953eQpSFhtnZM0Kclzlqq8AG9+3Mi9yfmuoAp7Piuu/ZyT/Lzy+vVr6XU30dHRePnyJczMzODp6Ql9fX2pd13e8ExJScGlS5cAqD6vaKMs2q6P/H4HtPHqjcKeK4tCfvNS2Dxrez8V5Lypypv+Drx69Qpt2rRBQEAAIiMjNZ7nivI8qIm26n7Pnj2oWbMmFi9ejL/++ktj/JIssyrF8fu5ePFi6VVVf/31F2bPni2N3vLy8sKcOXNUvu6HCifvX/33WM4T7MaNG/Pdm5H7IBg+fLg0TKpjx47o06cPPDw8ULZsWRgaGkp3jitWrIj79+9DCKGlEhQ9bZctr+ERAN64boYMGYIvvvgCmzZtwoEDB3Dy5Ek8ffoUDx8+xIoVK7By5UqMHz8e33777Rt9Tl40lRHIu5w5L/QiIyNRvXp1qaGZs4fTz88Pf/75JyIjI/H5559LYWQyWaHvCr+L8lPfbxI3P99JTQ3u/IbNeU4aMWJEnsNTc5IPH8qvt/Gcpa19UZwKez6T72d9ff0CDbGVDwssjHr16sHExATPnz9HZGQkWrZsKZ0zGjVqJH0v/fz8cPjwYURGRmLMmDE4duyY1FhTNcJCXpaGDRti+fLl+c5PuXLllNJwcnLCzz//nO80cr5fkYqetvdTQc6bmhTmd2DmzJnS8M0mTZpgyJAhqFOnDuzs7GBkZCQ1yBs3bozjx4+X6PlHG3WflJSE7t274+XLlzAxMcGYMWPQokULVK5cGebm5tLvyOHDh/Hxxx8DePvOufn9/Zw9ezZatmyZrzSNjY0V/jYzM8PPP/+M6Oho/PTTTzhy5AguXryIzMxMnD17FmfPnsXs2bOxc+dO6aYdFd4H2wjN+Uxa7mFP+ZWamootW7YAyH42ZOPGjWrDqrtz/7YqirI9fvw4z6FAOe9kqXu2RZOyZctixIgRGDFiBLKysnDhwgVs374dS5YsQUpKCmbOnIn69eujQ4cOhUpfHXl+k5KS8Pr16zx7dR4/fgwg+3uX+6aGvb09qlatij/++AORkZEYOHCgwnNbcn5+fli7dq20Tf6vh4fHG99hLiz55z558gSZmZl5/mDIhxEXhvz5bCB7qHJBWVlZIS4uLl/DG+X7qrDfx4LIeU56+fJloc5Jmrxt5yxtHTclobDnM/l+Tk9Ph7W1daF6cQqqVKlSaNCgAfbv3690zsh9XgGA48ePIzMzUwpjaWkpPUOVk7W1NR4/foy///670N9XeX08fvwY1atX19gj/r7Ib4/sixcviiM7Gr1t+yn370C1atXyHVcIgdWrVwMAPvroIxw+fFjtEO634dpNG3W/detW6TGW7du3o3nz5irDabO8MpkMQohi+Y7n/P3MyMh4499PLy8v6ZGKZ8+eITIyEuvWrcOOHTuQkJCATp064fbt2zAyMnqjz/nQfbDDcXM+f7R///5CpXHz5k1kZGQAyHuSoBs3bkjP4ajyJr05RUVbZcvp7Nmz+dpeunRpODs75zOn6uno6KBOnTr49ttvFSbN+emnn9447dzkJ7z09HSFZ1VUiY6OBpD9TIyqXqycz4U+evQIN2/elJ4Hlcv5XGhCQgKOHTsG4M2fB30Trq6uAIC0tDRcvnxZbbjMzEzpOZvCcHJykhoh8nIXhHxfxcbGSt9xVRISEqQhS0XRIMytTJkyKF++PIDsyXeK4i7023bO0uZxU9wKez7Txm8PUPB9kPO50OTkZIXnQeXkz4WmpqYiNjZW4XlQVRfp8rL88ccf+Rrep4o8jZcvX+LkyZOFSuNdJJ+gTNNF/40bN4ojOxq9bfupTp060v8L+jvw5MkT6SZk586d1TZAnz9//lbUvzbq/vfffweQfUNMXQMUUHxmXZWCnHfy8x3PysrCzZs3852mOq6urtLvwpucV1UxNTVFu3btsH37dgwbNgwAEBcXJz3aQIX3wTZCXVxcULNmTQDA5s2bce/evQKnkXPmrLye19E0TCnnw/lpaWkFzkdR0FbZctqwYYPaC+uHDx9KJw4/Pz+tDtUBsn+w5A2XonjY/pNPPpH+v2bNGrXhTp8+jatXryrFySnnc6Hy+pU/DyqX87nQxYsXa+150DchH8IDAOvXr1cb7rfffpPyWxg6Ojpo3bo1gOyGuqbGS27yek9JSUFERITacGvWrJG+r+r2lba1b98eQPZsmvKJh7TpbTtnafO4KW6FPZ+1atVKmhBr/vz5hZ6BsaD7IOdzofPnz8eLFy+k50Hlcj4XunPnTuk5enXnFfn3FQB+/PHHApcBgMKolMKm8S6SD5X8448/1E5c9Pfff+c5G3Rxetv2U61ateDg4AAAWL16db5vhgP5Pw+uWbMmzxuVxUUbdS8vc1pamtqeyZcvX+b52w38d97JzzlH/h3Pq2GrrUl/SpcuLV2DREZGSjcttS3ndc7bNnHTu+iDbYQCwMSJEwFkP6D+2Wef5TlEMC0tDUuXLpWm1geyG7Lyu0LqDtxff/0VixYtyjMfOYdjvcnrRbRJW2XL6cKFC5g9e7bS+tevX6N///7SBCmDBg0qcH63bNmS50P0586dk+7GFcWzRF5eXtJsmatXr8aBAweUwjx9+hRfffUVgOyGlLpy5uyZWLhwodK63OHkYUr6eVBfX1/p2eolS5ZIPS05/f333xg5cuQbf9aYMWOgo6MDIQS6du2KBw8eqA2be1tgYKA0Ocfo0aNVTmN/8eJFfPfddwCA8uXLo2PHjm+c5/z45ptvYGBgAAAYOHCgxrvSu3fvliaOyY+37ZylzeOmuBX2fFa+fHkEBgYCyP6effXVV3k2RBMSEqShgzkVdB/Ur19f+t7Lzxk5nweVk59XFi9enOfzoADQqVMn6dU5y5Yty/NGApA9G/Ivv/yilC/562x2796d56z2AHD37l2Eh4fnGeZdIG/Yp6enqzzeMjIy0Ldv37dmcpi3bT/p6Ojgm2++AZB9ju/Zs6faSdbkr1+RK1OmjDQT8ObNm1XGO3v2rHSNWNK0Uffy1/G9ePFC5Q3OzMxM9OvXT+MjLvLzTkJCgsZZn+Xf8aioKJU9uHFxcVLPojZMmDBB+n3r2rVrnufFzMxMbNq0SeH64M8//1R4M4EqOXtZ+Vy6FhT9q0i1I+cLa/P7AvacL3lW90J1+Yt3AQgbGxsxYcIEsX//fhEbGytOnDghQkNDRb9+/YSVlZUAoPRC5DZt2kjxW7RoIbZv3y7OnTsndu/eLfr27St0dXVFlSpVRJkyZQQA0atXL5X5qFChggAgnJycxM6dO8W1a9fEzZs3xc2bNxVebp/fF5Tn94XCeb0kWBtly/mS7Hr16gkAolu3bmLPnj0iJiZGbN68WXh5eUlh2rVrl2d+1alUqZKwsLAQvXr1EmvWrBHHjx8X58+fFwcOHBBTpkyR9p+urq44d+6c2jzeuXNH7Wdoeql0bGys0NfXF/j35fKjRo0SR44cEWfPnhUrV64Uzs7OUvyxY8fmWR4XFxcpLACxdetWpTDr169XCOPu7p5nmnkdP9qqg+PHjwsdHR0BQBgZGYkJEyaI48ePi+joaLF06VLh4OAg9PT0RO3atQUA4ejomGee8zJjxgwpLxYWFmLChAni4MGDIjY2Vhw5ckTMnz9fNGrUSPj5+SnFXbJkiRS3bNmyYt68eeLMmTPi5MmTYtq0acLExEQAEDKZTPz2228FrofChhVCcV/o6+uLvn37ih07doiYmBgRFRUlIiIixLhx40TlypUFAPHLL78opZHXvi7uc5am75Y2jptKlSrlmVe5grxoXRVtnc+ePXsm3NzcpHA1a9YUwcHB4vjx49L3d/HixaJjx45CX19f1K1bVymNmzdvSvH9/f3F0aNHxR9//CHtg4yMDKU4n3zyicI5Y/bs2Uphjh07phDGwsJCZGZmqq2TS5cuSceL/DsVGhoqzpw5I2JiYsSePXvEd999Jxo0aCAAiNGjRyul8fDhQ2Fvby+l4e3tLVasWCFOnTolncfnzp0rmjdvLnR1dUWnTp2U0sjrtyyn/P6GqpPzWkTddYVcXsdhWlqa9L3V0dERI0eOFMePHxdnz54V69atE56enkImkwlvb+8885uf66L8noM0la0491N+8p2ZmSmaN28uhalataoIDg4WJ06cEOfPnxe7d+8WkydPFlWqVFGqnyFDhkjxvLy8RHh4uDh79qw4ePCgGDVqlDA0NBQ2NjaiatWqavNb0HN7Ycoo96Z1f//+fWFgYCD9NgcFBYlDhw6Js2fPipCQEFG3bl0BQDRs2DDP78CBAwek7d27dxenT59WOO/kdOXKFVGqVCkBQFhaWor58+eLs2fPipMnT4off/xR2NraCmtra1GlShW1dVyQ400IxePbxMREDB8+XPz222/i/Pnz4vTp0yI8PFwMGzZMlCtXTgAQly9fVvqsmjVrigkTJogdO3aI6OhoER0dLSIiIkTnzp2ltD09PUVWVpbG/FDePvhG6OvXr8XYsWOFrq6uwg+vqsXY2Fi8fPlSIf69e/dExYoV1capWLGi+P333zVeJC1dulRtGjlPTMXZCNVG2XJetJ0/f154enqqTa9hw4YKF68FIc9DXouhoaEIDQ3NM49v0gATQoh9+/YJMzOzPPMxZMiQPC/qhBCib9++UniZTCYSEhKUwty/f18h3a+//jrPNPM6frRZByEhIUJPT09l2UuVKiVWrVolAgICBABRvXr1PPOsyXfffSf9yKlb1F3szJw5U2owq1oMDAxUfl/yWw+FCSu3efNmjd8jIPvi9fDhw0rx89rXxX3Oys93602Pm5JohL7p+SwpKUm0bNlS4z4GIJo2baoyjZwXRbkXVXWd88YNAHH27FmlMGlpacLIyEgKk58bgxcvXpQuJDUt06ZNU5nG3bt3Rf369fOVRmBgoFL8d60RKkT2TTtjY2OVZdTV1RXz58/XmF9NnyGE9hqhQhTffspvvl+8eCE+//xzjXnJXT8pKSnSzVBVi5WVlTh69Gie+S3ORqgQb173a9euzfM3r0uXLuLgwYN5fgcyMzOFj4+P2jRymzdvntqwlpaWGuu4oI1QIYSYP3++1ODOa9HX11doOOf8rLyWGjVq5HmdRPn3QQ/HBbKnfP7hhx9w9epVjB49Gp6enrC0tISuri5MTU3h6uqKL7/8EqGhoYiLi1OaCcvBwQHnz5/HN998g6pVq8LAwADm5uaoVasWpkyZggsXLkjPnuZl0KBBiIiIgL+/P8qWLVviM88B2iubnKWlJU6dOoVZs2ahdu3aMDU1hYmJCerXr49Fixbh6NGj0oPsBXXs2DGsXr0aXbp0gbu7O8qUKYNSpUrBzMwMderUwTfffIOrV6+iZ8+ehUo/v/z9/XHr1i2MHz8etWvXhpmZGQwMDFCxYkV8+eWXOH78OBYvXpzny9QBxeFvuZ8HlatQoQIqV64s/V2Sz4Pm1KtXL5w7dw5ffvklypUrB319fZQvXx6dO3fGiRMn0K9fP+k9W+bm5m/0WUFBQbh69SpGjBgBNzc3mJmZwdDQEM7Ozvj4448RHByMzZs3q4w7fvx4xMbGon///qhcuTKMjIxgbGyMGjVqYPjw4bh+/XqRf1/U6dKlC+7evYvvv/8efn5+KFu2LPT09KRJbtq1a4d58+bh7t270iRV+fU2nrO0ddwUpzc9n1lZWWHPnj04dOgQAgMDUaVKFZiYmKBUqVKwsrJC/fr1MWTIEOzevVvlMGUACAsLw48//ggvLy+Ym5sX6Lxibm6u8DyonL6+Pho0aCD9nZ/zioeHB65evYrQ0FB07NgRDg4OMDQ0hL6+Puzt7eHn54eJEyciJiYGkydPVplGpUqVEBUVhR07dqBr165wcnJC6dKloaenhzJlyqBBgwYYPXo0jh49qnHY77vio48+QkxMDAICAlCuXDno6enB3t4enTp1wrFjxzBixIiSzqKSt20/lS5dGlu3bsXhw4cREBAAJycnGBkZwdTUFNWrV8dnn32GTZs2SUN35czNzXHy5EnMmDED7u7uMDQ0hImJCWrUqIExY8bg4sWLb93rzt607gMDA3H8+HF07NgRZcqUkb5vLVu2xJYtW7B582aN83Ho6Ohg//79mDhxImrVqgUTE5M8JysaOXIk9u7dixYtWsDS0hIGBgZwcnLCkCFDcOHChSKp4xEjRuD27duYNGkSfHx8YGNjg1KlSsHY2BhVq1ZFp06dsHz5cjx8+BAuLi5SvEaNGuH06dOYPn06mjVrBhcXF5iamkJPTw+2trbw9/fHihUrcOHCBTg6Omo93x8imRBv2YuA6L0SEhIiPf90584dHrgEIPvZxNu3b6NHjx7YsGFDSWeHiIiIiIrR23NbmYg+CGfPnpUmDPDx8Snh3BARERFRcWMjlIi06tatW2q3JSUloX///gAAAwMDdOnSpbiyRURERERviZJ/8JCI3ivNmzeHk5MTPv30U3h4eMDc3BzJyck4efIkli5diri4OADZr0iysbEp4dwSERERUXFjTygRaZUQAkeOHMGwYcPg5+cHT09PNGvWDJMmTZIaoIMHD8b48eNLOKdERERE745jx46hXbt2KFeuHGQyGXbu3KkxztGjR1G3bl1p4sbly5crhYmIiEDNmjVhYGCAmjVrYseOHUWQe0VshBKRVoWGhmL06NGoV68eypcvDwMDAxgbG6Ny5cro1asXTp48iSVLlrxVM50SERERve1evHiBWrVqYfHixfkKf+fOHbRu3RqNGjVCbGwsxo8fj2HDhiEiIkIKc/r0aXTp0gUBAQG4ePEiAgIC0LlzZ0RFRRVVMQBwdlwiIiIiIqJ3ikwmw44dO9CxY0e1YcaNG4eff/4Z165dk9YNHDgQFy9exOnTpwFkvxYuNTUVe/bskcK0bNkSlpaWCA8PL7L8syuCiIiIiIioBKSlpSE1NVVhSUtL00rap0+fhr+/v8K6Fi1a4Ny5c8jIyMgzzKlTp7SSB3U4MREREREREZEGJzo31HqaB2s2x7Rp0xTWTZkyBVOnTn3jtOPj42Fra6uwztbWFq9fv0ZiYiLs7e3VhomPj3/jz88LG6FERERERESa6Mi0nmRQUBBGjRqlsM7AwEBr6ctkinmWP4mZc72qMLnXaRsboURERERERCXAwMBAq43OnOzs7JR6NBMSElCqVClYW1vnGSZ376i28ZlQIiIiIiIiTWQy7S9FyNfXFwcOHFBYt3//ftSrVw96enp5hmnQoEGR5o09oURERERERBrIZCXbf/f8+XPcunVL+vvOnTu4cOECrKysULFiRQQFBeHhw4dYv349gOyZcBcvXoxRo0ahf//+OH36NNasWaMw6+3w4cPRuHFj/PDDD+jQoQN27dqFgwcP4sSJE0VaFvaEEhERERERveXOnTsHT09PeHp6AgBGjRoFT09PTJ48GQAQFxeHe/fuSeGdnJywe/duREZGonbt2pgxYwYWLlyITp06SWEaNGiAzZs3Y926dfDw8EBISAi2bNkCb2/vIi0L3xNKRERERESkwckv/bSeZsONkVpP813AnlAiIiIiIiIqNnwmlIiIiIiISIOSfib0fcJGKBERERERkSZF8J7QDxWb80RERERERFRs2BNKRERERESkCYfjag1rkoiIiIiIiIoNe0KJiIiIiIg0kMn4TKi2sBFKRERERESkCYfjag1rkoiIiIiIiIoNe0KJiIiIiIg04StatIaNUCIiIiIiIg34TKj2cDguERERERERFRv2hBIREREREWnCiYm0hjVJRERERERExYY9oURERERERBrwmVDtYSOUiIiIiIhIEw7H1RrWJBERERERERUb9oQSERERERFpwveEag17QomIiIiIiKjYvNeN0L179+Kjjz6ChYUFrK2t0bZtW9y+fVvafurUKdSuXRuGhoaoV68edu7cCZlMhgsXLkhhrl69itatW8PExAS2trYICAhAYmJiCZSGiIiIiIhKikymo/XlQ/Vel/zFixcYNWoUzp49i0OHDkFHRweffvopsrKy8OzZM7Rr1w7u7u44f/48ZsyYgXHjxinEj4uLQ5MmTVC7dm2cO3cOe/fuxePHj9G5c+cSKhEREREREZUImUz7ywfqvX4mtFOnTgp/r1mzBmXLlsXVq1dx4sQJyGQyrFq1CoaGhqhZsyYePnyI/v37S+GXLVuGOnXq4LvvvpPWrV27Fg4ODvjjjz9QtWpVhfTT0tKQlpamsM7AwAAGBgZFUDoiIiIiIqJ3z3vdE3r79m10794dzs7OMDMzg5OTEwDg3r17uHHjBjw8PGBoaCiF9/LyUogfExODI0eOwMTERFqqV68upZ3brFmzYG5urrDMmjWrCEtIRERERETFgj2hWvNe94S2a9cODg4OWLVqFcqVK4esrCy4ubkhPT0dQgilF84KIRT+zsrKQrt27fDDDz8opW1vb6+0LigoCKNGjVJYx15QIiIiIiKi/7y3jdCkpCRcu3YNK1asQKNGjQAAJ06ckLZXr14dGzduRFpamtRQPHfunEIaderUQUREBBwdHVGqlOaq4tBbIiIiIqL3k0znvR5EWqze25q0tLSEtbU1Vq5ciVu3buHw4cMKvZTdu3dHVlYWBgwYgGvXrmHfvn2YM2cOAEg9pEOGDMGTJ0/QrVs3REdH488//8T+/fvRp08fZGZmlki5iIiIiIioBHA4rta8t41QHR0dbN68GTExMXBzc8PIkSMxe/ZsabuZmRl++eUXXLhwAbVr18aECRMwefJkAJCeEy1XrhxOnjyJzMxMtGjRAm5ubhg+fDjMzc2hwzshREREREREBSYTuR+E/IBt3LgRgYGBePr0KYyMjEo6O0RERERE9JY4N6Kb1tOsFxyu9TTfBe/tM6H5sX79ejg7O6N8+fK4ePEixo0bh86dO7MBSkREREREij7g4bPa9kE3QuPj4zF58mTEx8fD3t4eX3zxBWbOnFnS2SIiIiIiInpvcTguERERERGRBudG9dB6mvXmhWk9zXcBZ9chIiIiIiKiYvNBD8clIiIiIiLKD5kOnwnVFjZCiYiIiIiINOHERFrD4bhERERERERUbNgTSkREREREpImM/XfawpokIiIiIiKiYsOeUCIiIiIiIg1kfCZUa9gIJSIiIiIi0kSHg0i1hTVJRERERERExYY9oURERERERBpwOK72sCeUiIiIiIhIE5mO9pdCWLp0KZycnGBoaIi6devi+PHjasP27t0bMplMaXF1dZXChISEqAzz6tWrQuUvP9gIJSIiIiIiegds2bIFI0aMwIQJExAbG4tGjRqhVatWuHfvnsrwCxYsQFxcnLTcv38fVlZW+OKLLxTCmZmZKYSLi4uDoaFhkZWDjVAiIiIiIiJNZDLtLwU0b9489O3bF/369UONGjUQHBwMBwcHLFu2TGV4c3Nz2NnZScu5c+eQnJyMwMDAXEWTKYSzs7MrVBXlFxuhREREREREJSAtLQ2pqakKS1pamsqw6enpiImJgb+/v8J6f39/nDp1Kl+ft2bNGnzyySeoVKmSwvrnz5+jUqVKqFChAtq2bYvY2NjCFSif2AglIiIiIiLSQCbT0foya9YsmJubKyyzZs1S+fmJiYnIzMyEra2twnpbW1vEx8drzH9cXBz27NmDfv36KayvXr06QkJC8PPPPyM8PByGhoZo2LAhbt68WfjK0oCz4xIREREREWmio/3ZcYOCgjBq1CiFdQYGBnnGyT1LrxAiXzP3hoSEwMLCAh07dlRY7+PjAx8fH+nvhg0bok6dOli0aBEWLlyoMd3CYCOUiIiIiIioBBgYGGhsdMrZ2NhAV1dXqdczISFBqXc0NyEE1q5di4CAAOjr6+cZVkdHB/Xr1y/SnlAOxyUiIiIiItKkhCcm0tfXR926dXHgwAGF9QcOHECDBg3yjHv06FHcunULffv21fg5QghcuHAB9vb2BcpfQbAnlIiIiIiI6B0watQoBAQEoF69evD19cXKlStx7949DBw4EED28N6HDx9i/fr1CvHWrFkDb29vuLm5KaU5bdo0+Pj4oEqVKkhNTcXChQtx4cIFLFmypMjKwUYoERERERGRBjJZyQ8i7dKlC5KSkjB9+nTExcXBzc0Nu3fvlma7jYuLU3pn6NOnTxEREYEFCxaoTDMlJQUDBgxAfHw8zM3N4enpiWPHjsHLy6vIyiETQogiS52IiIiIiOg9cHH6MK2nWWty0Uz887Yr+eY8ERERERERfTA4HLcYJN+/W9JZoFwsHRxLOgtERERE9A55G4bjvi9Yk0RERERERFRs2BNKRERERESkiU7BXqlC6rERSkREREREpAmH42oNa5KIiIiIiIiKDXtCiYiIiIiINJDJOBxXW9gIJSIiIiIi0oSNUK3hcFwiIiIiIiIqNuwJJSIiIiIi0kSH/XfawpokIiIiIiKiYsOeUCIiIiIiIg04MZH2sBFKRERERESkCd8TqjWsSSIiIiIiIio27AklIiIiIiLSgMNxtYc9oURERERERFRs2BNKRERERESkCZ8J1Ro2QomIiIiIiDTR4XBcbWFznoiIiIiIiIoNe0KJiIiIiIg04MRE2sOeUCIiIiIiIio27AklIiIiIiLShBMTaQ0boURERERERJpwOK7WsDlPRERERERExaZYGqF+fn4YMWJEoeNHRkZCJpMhJSVFa3kiIiIiIiLKL5mOjtaXDxWH434gtu36BRu3bkVS0hM4OVbCyMEDUdvdXW348xcvYcHyFbhz9y/YWFujR5cv8Fm7ttL2169fIzR8M3bvP4i/ExNR0aEChvTrC1+v+v+FyczE6tAN2Hf4MJ48SYa1lRXatGiOwC+7Q+cDPuiIiIiI6B3E4bhaw5bAB+DAkUgEL1uO3t27IXT5UtR2d8PIoImIf5ygMvyjuHiMmjARtd3dELp8KXp174p5S5bh8LHjUpjl60Kw89fdGD10MMLXrMKnbdvgf1On48bNW1KYDZu3YMevv2HM0CEIX7sKQwf0w8aftmHrzl1FXmYiIiIiIno7FVsj9PXr1xg6dCgsLCxgbW2NiRMnQggBAAgLC0O9evVgamoKOzs7dO/eHQkJqhtIAJCUlIRu3bqhQoUKKF26NNzd3REeHq4Qxs/PD8OGDcPYsWNhZWUFOzs7TJ06VSFMSkoKBgwYAFtbWxgaGsLNzQ2//vqrtP3UqVNo3LgxjIyM4ODggGHDhuHFixfaq5RiEh6xHe1atkCH1q3gVKkiRg4ehLJly2D7L7+qDL/9119hW7YsRg4eBKdKFdGhdSu0a+mPTVsjpDB7Dx5Cr+5d0cDbC+XL2aNT+3bwrlcXm7b9F+bK1Wto3MAXDX28Uc7ODs0aN4JX3Tq49sfNIi8zEREREZE2yWQ6Wl8+VMVW8tDQUJQqVQpRUVFYuHAh5s+fj9WrVwMA0tPTMWPGDFy8eBE7d+7EnTt30Lt3b7VpvXr1CnXr1sWvv/6KK1euYMCAAQgICEBUVJTSZxobGyMqKgo//vgjpk+fjgMHDgAAsrKy0KpVK5w6dQphYWG4evUqvv/+e+jq6gIALl++jBYtWuCzzz7DpUuXsGXLFpw4cQJDhw4tmgoqIhkZGbjxx01416ursN67bl1cvnpVZZwrV6/Bu26u8PXq4doff+D169cAgPT0DOjr6yuEMTAwwMUrv0t/13Jzw9nYC7j34AEA4Obt27h45Xc0yDFkl4iIiIiIPizF9kyog4MD5s+fD5lMhmrVquHy5cuYP38++vfvjz59+kjhnJ2dsXDhQnh5eeH58+cwMTFRSqt8+fIYM2aM9PfXX3+NvXv3YuvWrfD29pbWe3h4YMqUKQCAKlWqYPHixTh06BCaN2+OgwcPIjo6GteuXUPVqlWlz5abPXs2unfvLk2oVKVKFSxcuBBNmjTBsmXLYGhoqJSvtLQ0pKWlKawzMDAoRG1pT8rTVGRmZcHK0kJhvZWlBZKeJKuMk/QkWWX4zMxMpDx9Chtra/jUq4vwbRGo7e6OCuXscTY2FsdOnUZWVpYUJ6BrZzx/8QJdAvtBR0cHWVlZGBjYG/7Nmmq7mERERERERYvPhGpNsfWE+vj4QJZjx/n6+uLmzZvIzMxEbGwsOnTogEqVKsHU1BR+fn4AgHv37qlMKzMzEzNnzoSHhwesra1hYmKC/fv3K4X38PBQ+Nve3l4a5nvhwgVUqFBBaoDmFhMTg5CQEJiYmEhLixYtkJWVhTt37qiMM2vWLJibmysss2bNylf9FDVZroNGCJHncSRD7vCK6YwcMggO5cuja59+aNSyDeYuWoq2LfwVJhw6GHkUew8dwvTx/0PosiWYPHYMNm7dht/2H9BOoYiIiIiIiotMR/vLB6rEZ8d99eoV/P394e/vj7CwMJQpUwb37t1DixYtkJ6erjLO3LlzMX/+fAQHB8Pd3R3GxsYYMWKEUng9PT2Fv2UymdRTZ2RklGe+srKy8NVXX2HYsGFK2ypWrKgyTlBQEEaNGqWwzsDAAC8T4vL8rKJkYW4GXR0dpV7P5JSnsLK0VBnH2soSScm5w6dAV1cX5mZmAABLCwv8OH0q0tLT8TQ1FWWsrbFk9RqUs7OV4ixauQo9u3ZB86Z+AAAXZyfEPU7A+vDNaOPfXGtlJCIiIiKid0exNULPnDmj9HeVKlVw/fp1JCYm4vvvv4eDgwMA4Ny5c3mmdfz4cXTo0AE9evQAkN1gvHnzJmrUqJHv/Hh4eODBgwf4448/VPaG1qlTB7///jtcXFzynaaBgYHK4bcv852C9unp6aFa1SqIjjkPv48aSuujY86jcQNflXHcatbAidOKz9dGnYtBjapVUaqU4lfGQF8fZW1s8Pr1a0QeP4GPmzSWtr16labUA6uro4OsLPGmxSIiIiIiKlYyHQ7H1ZZi6wO+f/8+Ro0ahRs3biA8PByLFi3C8OHDUbFiRejr62PRokX4888/8fPPP2PGjBl5puXi4oIDBw7g1KlTuHbtGr766ivEx8cXKD9NmjRB48aN0alTJxw4cAB37tzBnj17sHfvXgDAuHHjcPr0aQwZMgQXLlzAzZs38fPPP+Prr78udB2UlG6dPsPPe/bilz37cOevewheuhyPExLwabs2AIClq9di2vc/SuE/a9sW8QmPEbxsBe78dQ+/7NmHX/buQ/cvOklhrly7jiPHT+DhozhcuHwZI4ImICtLoEeXzlKYj3x9ELJpM06eicKj+HhEnjiJ8IjtaPJRg+IrPBERERERvVWKrSe0Z8+e+Oeff+Dl5QVdXV18/fXXGDBgAGQyGUJCQjB+/HgsXLgQderUwZw5c9C+fXu1aU2aNAl37txBixYtULp0aQwYMAAdO3bE06dPC5SniIgIjBkzBt26dcOLFy/g4uKC77//HkB2T+nRo0cxYcIENGrUCEIIVK5cGV26dHmjeigJzZv64WnqM6wJ24ikJ0/g7FgJ8777Fva22UNnE588QXzC31L4cvZ2mDfzWwQvW4GIn3+BjbUVRg0ZhGaNG0lh0tPTsWJdKB7FxcHIyAgNvOpjyrixMM0xkdTooYOxMiQUsxcuRnJKCmysrdGxTWv0Dfiy2MpORERERKQVnJhIa2RC/rJOKjLJ9++WdBYoF0sHx5LOAhERERG9Q26smaf1NKv1HaU50Hvow52SiYiIiIiIiIpdic+OS0RERERE9LbLPeEmFR4boURERERERJrocBCptrAmiYiIiIiIqNiwJ5SIiIiIiEgDDsfVHvaEEhERERERvSOWLl0KJycnGBoaom7dujh+/LjasJGRkZDJZErL9evXFcJFRESgZs2aMDAwQM2aNbFjx44iLQMboURERERERJrIdLS/FNCWLVswYsQITJgwAbGxsWjUqBFatWqFe/fu5Rnvxo0biIuLk5YqVapI206fPo0uXbogICAAFy9eREBAADp37oyoqKgC5y+/+J7QYsD3hL59+J5QIiIiIiqImxuWaD3NKgFDChTe29sbderUwbJly6R1NWrUQMeOHTFr1iyl8JGRkWjatCmSk5NhYWGhMs0uXbogNTUVe/bskda1bNkSlpaWCA8PL1D+8os9oURERERERCUgLS0NqampCktaWprKsOnp6YiJiYG/v7/Cen9/f5w6dSrPz/H09IS9vT0+/vhjHDlyRGHb6dOnldJs0aKFxjTfBBuhREREREREGsh0dLS+zJo1C+bm5gqLqh5NAEhMTERmZiZsbW0V1tva2iI+Pl5lHHt7e6xcuRIRERHYvn07qlWrho8//hjHjh2TwsTHxxcoTW3g7LhEREREREQlICgoCKNGjVJYZ2BgkGec3LP0CiHUztxbrVo1VKtWTfrb19cX9+/fx5w5c9C4ceNCpakNbIQSERERERFpUoiJhDQxMDDQ2OiUs7Gxga6urlIPZUJCglJPZl58fHwQFhYm/W1nZ/fGaRYUh+MSERERERFpoOpVJ2+6FIS+vj7q1q2LAwcOKKw/cOAAGjRokO90YmNjYW9vL/3t6+urlOb+/fsLlGZBsSeUiIiIiIjoHTBq1CgEBASgXr168PX1xcqVK3Hv3j0MHDgQQPbw3ocPH2L9+vUAgODgYDg6OsLV1RXp6ekICwtDREQEIiIipDSHDx+Oxo0b44cffkCHDh2wa9cuHDx4ECdOnCiycrARSkREREREpIlO0T0jmV9dunRBUlISpk+fjri4OLi5uWH37t2oVKkSACAuLk7hnaHp6ekYM2YMHj58CCMjI7i6uuK3335D69atpTANGjTA5s2bMXHiREyaNAmVK1fGli1b4O3tXWTl4HtCiwHfE/r24XtCiYiIiKggbm9ZpfU0K3fpr/U03wXsCSUiIiIiItKkCCYm+lCxEUpERERERKRBUb6y5EPD5jwREREREREVG/aEEhERERERacLhuFrDRigREREREZEmb8HsuO8LNueJiIiIiIio2LAnlIiIiIiISAMZh+NqDWuSiIiIiIiIig17QouBpYNjSWeBiIiIiIjeBF/RojVshBaD5KfPSjoLlIuluSmepaaWdDYoF1Mzs5LOAhEREZFKHI6rPaxJIiIiIiIiKjbsCSUiIiIiItKEw3G1hj2hREREREREVGzYE0pERERERKSJDntCtYWNUCIiIiIiIg04MZH2sCaJiIiIiIio2LAnlIiIiIiISBNOTKQ17AklIiIiIiKiYsOeUCIiIiIiIk34TKjWsBFKRERERESkgYzDcbWGzXkiIiIiIiIqNuwJJSIiIiIi0kSH/XfawkYoERERERGRBhyOqz1szhMREREREVGxYU8oERERERGRJuwJ1Rr2hBIREREREVGxYU8oERERERGRJnxPqNawEUpERERERKSBTIfDcbWFzXkiIiIiIiIqNuwJJSIiIiIi0oTDcbWGNUlERERERETFhj2hREREREREmvAVLVrzwfWE+vn5YcSIESWdDSIiIiIieofIZDpaXz5UH27JP0BCCKxauQJtW7dEk0YNMWjgAPx5+7bGeIcPH0LXLl+gUUNfdO3yBSKPHFEKs23bVnzaoT0af9QAvXr2wIXYWIXtSUlJmD5tqvTZI4Z9jXv37mmtbO+qrVu3on2HDmjQsCF6BAQgNle95RYTE4MeAQFo0LAhOnTogG0REUphDh0+jC86d4Zvgwb4onNnHMm1v1asXIl69esrLC1atNBquYiIiIiI1GEj9AOyYX0owsM3YfQ3Y7E2JBTW1tYY9vUQvHjxQm2cy5cuYdKE8WjVqjU2bAxHq1atMWH8/3DlyhUpzIED+xE8by56B/ZB6IaNqF3bEyNHDEN8fDyA7MbvuG/G4NHDh/hxzlysD9sIO3s7DBs6GP/880+Rl/tttX//fsydNw99AgOxMSwMnrVrY9jw4VK95fbw4UMMHzECnrVrY2NYGAIDAzFnzhwcOnxYCnPp0iWMHz8erVu1QvimTWjdqhX+FxSksL8AwNnZGXv37JGWzZs3F2lZiYiIiN55Mpn2lw/Ue90IffHiBXr27AkTExPY29tj7ty5CtvDwsJQr149mJqaws7ODt27d0dCQgKA7IaTi4sL5syZoxDnypUr0NHRwe189CC+TYQQ2LI5HL17B6Jp02aoXNkFk6dMw6tXr7B/31618TZvDkd9L2/06h0IR0dH9OodiPr1vbBl8yYpTPimjWjXvgM6dOwIJycnjBw1GmVtbbE9YhsA4P69e7hy5TLGjvsfatZ0RaVKjvhm7P/w8uU/2L9vX5GX/W21cdMmdOjQAR3/rbfRo0fD1tYW27ZtUxk+Yvt22NnZYfTo0XByckLHjh3Rvn17hIWFSWHCw8Ph7eWFwMDs/RUYGAiv+vWxKTxcIa1SurqwsbGRFktLyyItKxEREdG7TqYj0/ryoXqvG6HffPMNjhw5gh07dmD//v2IjIxETEyMtD09PR0zZszAxYsXsXPnTty5cwe9e/cGAMhkMvTp0wfr1q1TSHPt2rVo1KgRKleuXJxFeWOPHj1EUlISvH18pHX6+vrwrFMHly9dUhvvyuVL8Pb2Vljn7eMjxcnIyMCN69fh7e2jGMb7vzDpGRnZn2dgIG3X1dWFnl4pXLx44Y3K9a7KyMjA9evX4ZOrbn28vXFJzf64fPmyUnhfHx9cvXoVr1+/BgBcunxZYR8DgI+vr1Ka9+7fR8tWrdC+QwcEjR+PBw8evGmRiIiIiIjy5b1thD5//hxr1qzBnDlz0Lx5c7i7uyM0NBSZmZlSmD59+qBVq1ZwdnaGj48PFi5ciD179uD58+cAgMDAQNy4cQPR0dEAshsOYWFh6NOnj8rPTEtLQ2pqqsKSlpZW9IXNh6SkJACAlZW1wnorK2tpm7p4ecVJSUlBZmYmrKytcoWxQlJSIgDA0dERdvb2WLZkMVJTU5GRkYH1oSFISkpCUmLiG5ftXSTVm1WuerO2RqKa/ZGUlAQr69z7wgqZmZlISUmRwljnStPaykphH7u5umLatGlYvGgRJowfj6SkJPTt21dKg4iIiIhUkOlof/lAvbclv337NtLT0+Hr6yuts7KyQrVq1aS/Y2Nj0aFDB1SqVAmmpqbw8/MDAGnCHHt7e7Rp0wZr164FAPz666949eoVvvjiC5WfOWvWLJibmysss2bNKqIS5m3v3j1o2qSRtMh7ymS5xp4LIZTW5ZZ7s4ByHBnUp1uqVCl8//2PuHfvHvw/aQa/xh/hfEwMfBs0gI7ue/sVzJeC7o/cW4TqRPNMs2HDhvi4WTO4uLjA29sbC4KDAQC//vZbAXJORERERFQ47+17QoVQeXkuefHiBfz9/eHv74+wsDCUKVMG9+7dQ4sWLZCeni6F69evHwICAjB//nysW7cOXbp0QenSpVWmGRQUhFGjRimsMzAwwMtX6SrDF6VGjRrD1dVN+jvj3zIlJSXCxsZGWp+c/ESpNy4na2vlntLkJ//FsbCwgK6urnKY5GSFHtTqNWpgw8ZNeP78OTIyMmBpaYk+gb1Qo0bNwhfyHaa23p48UerJlFO3L3R1dWFhYaE2zJPk5Dz3sZGRESq7uOD+/fuFKAkRERHRB+IDnkhI297bbigXFxfo6enhzJkz0rrk5GT88ccfAIDr168jMTER33//PRo1aoTq1atLkxLl1Lp1axgbG2PZsmXYs2eP2qG4QHaD08zMTGExyPEcZHEyNjaGg4ODtDg5O8Pa2hrRUVFSmIyMDMSePw93Dw+16bi5eyA6OkphXVRUlBRHT08P1apXVwoTHR2lMl0TExNYWlri3r17uH7tGho3bvImxXxn6enpoXr16oiKylW30dHwULM/3N3dEfXv0HC5M1FRqFmzJkqVyr6f5OHurpzmmTNq0wSyn42+e/cubHIN9SUiIiKi//A9odrz3pbcxMQEffv2xTfffINDhw7hypUr6N27N3R0sotcsWJF6OvrY9GiRfjzzz/x888/Y8aMGUrp6Orqonfv3ggKCoKLi4vC8N53iUwmQ5eu3RAasg6RR47g9u1bmDFtKgwNDeHfoqUUbtqUyVi6ZLH0d5euXREdFYX1oSG4e/cu1oeG4Gx0FLp07S6F6db9S/y8ayd++XkX7ty5g+B5c/E4Ph6fftZJCnPo4EHExJzDw4cPcOxoJIZ9PQSNmzRRmkTnQ/Jl9+7YuWsXdv38M+7cuYO58+YhPj4enTpl19vixYsxecoUKXynzz5DXFwc5s2fjzt37mDXzz9j165d6NGjhxSma9euiIqKQkhoKO7evYuQ0FBERUeje7duUpjg4GDExMTg4cOHuHLlCsaNG4cXL16gbdu2xVd4IiIiIvpgvbfDcQFg9uzZeP78Odq3bw9TU1OMHj0aT58+BQCUKVMGISEhGD9+PBYuXIg6depgzpw5aN++vVI6ffv2xXfffZdnL+i7IKBnL6SlpWH2j9/j2bNncHV1w4JFi2FsbCyFiX8cD5nOf/cmPDxqYca3M7Fi+TKsXLEc5StUwLffzYKb239DfZs398fTp0+xZs1qJCUmwrlyZcybvwD29vZSmMSkRCwIno8nT5JgY2ODVq3boE/ffsVT8LeUv392va1evRqJiYmoXLkyFgQHS/WWmJio8M7Q8uXLY0FwMObNn4+tW7eiTJkyGDNmDD5u1kwKU6tWLcycORPLli3D8uXLUaFCBcz67juF/fU4IQETJk5ESkoKLC0t4ebmhnVr1yrsLyIiIiLK5S15pcrSpUsxe/ZsxMXFwdXVFcHBwWjUqJHKsNu3b8eyZctw4cIFpKWlwdXVFVOnTkWLFi2kMCEhIQgMDFSK+88//8DQ0LBIyiATmh6eJJw8eRJ+fn548OABbG1tCxw/+emzIsgVvQlLc1M8S00t6WxQLqZmZiWdBSIiIiKV4qKPaT1Ne6/GBQq/ZcsWBAQEYOnSpWjYsCFWrFiB1atX4+rVq6hYsaJS+BEjRqBcuXJo2rQpLCwssG7dOsyZMwdRUVHw9PQEkN0IHT58OG7cuKEQ187OrvAF04CN0DykpaXh/v37GDBgAOzt7bFx48ZCpcNG6NuHjdC3ExuhRERE9LaKO3tC62laedRXeqWjgYGB2nllvL29UadOHSxbtkxaV6NGDXTs2DHfb+VwdXVFly5dMHnyZADZjdARI0YU6+v63ttnQrUhPDwc1apVw9OnT/Hjjz+WdHaIiIiIiKiEyGQyrS8FecVjeno6YmJi4O/vr7De398fp06dylcZsrKy8OzZM6U3Jzx//hyVKlVChQoV0LZtW8TGxhaukvLpvX4m9E317t0bvXv3LulsEBERERHRe0jdKx5VSUxMRGZmptLjgba2tgrziORl7ty5ePHiBTp37iytq169OkJCQuDu7o7U1FQsWLAADRs2xMWLF1GlSpUClih/2AglIiIiIiLSpAheqZLX0Fu12cj1vlIhhNI6VcLDwzF16lTs2rULZcuWldb7+PjAJ8cbKxo2bIg6depg0aJFWLhwYYHyll9shBIREREREb3lbGxsoKurq9TrmZCQoHHy1C1btqBv377YunUrPvnkkzzD6ujooH79+rh58+Yb51ntZxRZykRERERERO+JongmtCD09fVRt25dHDhwQGH9gQMH0KBBA7XxwsPD0bt3b2zatAlt2rTR+DlCCFy4cKFIX9/HnlAiIiIiIiJN3oL3hI4aNQoBAQGoV68efH19sXLlSty7dw8DBw4EkP2M6cOHD7F+/XoA2Q3Qnj17YsGCBfDx8ZF6UY2MjGBubg4AmDZtGnx8fFClShWkpqZi4cKFuHDhApYsWVJk5WAjlIiIiIiI6B3QpUsXJCUlYfr06YiLi4Obmxt2796NSpUqAQDi4uJw7949KfyKFSvw+vVrDBkyBEOGDJHW9+rVCyEhIQCAlJQUDBgwAPHx8TA3N4enpyeOHTsGLy+vIisH3xNaDPie0LcP3xP6duJ7QomIiOht9fjiWa2naVurvtbTfBewJ5SIiIiIiEiDgj7DSepxYiIiIiIiIiIqNuwJJSIiIiIi0qQI3hP6oWJNEhERERERUbFhTygREREREZEmb8ErWt4XbIQSERERERFpIONwXK1hTRIREREREVGxYU8oERERERGRJnxFi9awJ5SIiIiIiIiKDXtCiYiIiIiINJCxJ1Rr2AglIiIiIiLShBMTaQ1rkoiIiIiIiIoNe0KJiIiIiIg04XtCtYY9oURERERERFRs2BNKRERERESkgYzPhGoNG6HFwNLctKSzQCqYmpmVdBaIiIiI6F3B2XG1ho3QYpD67FlJZ4FyMTM1xbPk5JLOBuViammJpJTUks4G5WJtwRs2REREpD1shBIREREREWnC4bhaw0YoERERERGRBjIOx9UaNueJiIiIiIio2LAnlIiIiIiISBMd9t9pC2uSiIiIiIiIig17QomIiIiIiDTgM6Haw0YoERERERGRJmyEag2H4xIREREREVGxYU8oERERERGRJnxPqNawJomIiIiIiKjYsCeUiIiIiIhIA5kOnwnVFjZCiYiIiIiINOFwXK1hTRIREREREVGxYU8oERERERGRJnxFi9awEUpERERERKSBjMNxtYY1SURERERERMWGPaFERERERESacDiu1rAnlIiIiIiIiIoNe0KJiIiIiIg04HtCtYeNUCIiIiIiIk04MZHWsCaJiIiIiIio2BRrI9TPzw8jRowozo+UREZGQiaTISUlpUQ+n4iIiIiI3mEymfaXD9R7ORzXz88PtWvXRnBwsLSuQYMGiIuLg7m5ecllrIQJIbBq5Urs2LEDz549g6urK8aOG4fKlSvnGe/woUNYvnw5Hjx4gAoVKmDQ4MFo2rSpQpitW7cibMMGJCYmwtnZGaNGj4anp6e0ferUqfjt118V4ri5uWFdSIjWyvcu2rptGzZs3IjEpCQ4Ozlh9MiR8KxdW234mPPnMX/BAvx55w7K2NggoEcPfP7ZZ9L2HTt34rc9e3D7zz8BADWqVcPgQYPg5uoqhVkXGoojkZG4+9dfMDAwgIe7O74eMgSOlSoVWTnfNUIIrFm9Cj/v3IHUf4+V0d+MhbNz3sfKkcOHsWrFcjx8+ADly1fAV4MGoYnff8fK9oht2LE9AnGP4gAATs7O6NO3L3wbNHzjzyYiIiJ6V7xTw3EzMjIKHVdfXx92dnaQfcB3HNaHhmLTpk34ZuxYhISGwtraGkOHDMGLFy/Uxrl06RLGjx+PVq1bY1N4OFq1bo2g//0PV65ckcLs378f8+bORWCfPgjbuBG1PT0xfNgwxMfHK6Tl26AB9uzdKy3BCxYUWVnfBfsPHMDc4GD06d0bG0ND4Vm7NoaNHKlUb3IPHz3C8FGj4Fm7NjaGhiKwVy/MmTcPhw4flsLEnD+PFs2bY/mSJVi3ahVs7ewwdPhwJCQkSGHOx8bii06dsG71aixZuBCZmZkYOnw4/vnnnyIv87sibMN6bN60CaPGfIM160JgZWWNEV8PzfNYuXz5EiZPHI+WrVohNGwTWrZqhYnjg/B7jmOlbNmyGDR4KNaGhmJtaCjq1quHcd+MwZ9/3n6jzyYiIqKiJ5PpaH35UBVZyV+8eIGePXvCxMQE9vb2mDt3rsJ2mUyGnTt3KqyzsLBAyL89Y3fv3oVMJsNPP/0EPz8/GBoaIiwsDElJSejWrRsqVKiA0qVLw93dHeHh4VIavXv3xtGjR7FgwQLIZDLIZDLcvXtX5XDciIgIuLq6wsDAAI6Ojkp5dHR0xHfffYc+ffrA1NQUFStWxMqVK7VaT8VFCIHw8HAEBgaiWbNmcHFxwdRp0/Dq1Svs27tXbbzw8HB4eXsjMDAQjo6OCAwMRH0vL4Rv2iSF2bRxIzp06ICOHTvCyckJo0ePhq2tLbZt26aQlr6eHmxsbKTlQ+6VBoCN4eHo0K4dOnbokF1vI0fCtmxZbNu+XWX4iO3bYWdri9EjR8LJyQkdO3RA+3btEJZjX3w7fTq++PxzVKtaFY6OjpgYFASRlYXoc+ekMIuCg9GubVtUdnZG1SpVMGXiRMTHx+Pa9etFXuZ3gRACP20OR6/AQPg1bYbKlV0wacpUvHr1Cgf27VMb76fN4ajv5YWevbOPlZ69A1Gvfn1s2fzf+emjRo3RoGFDVKxYCRUrVsLAQYNhVLq01FAt7GcTERFRMdCRaX8phKVLl8LJyQmGhoaoW7cujh8/nmf4o0ePom7dujA0NISzszOWL1+uFCYiIgI1a9aEgYEBatasiR07dhQqb/lVZI3Qb775BkeOHMGOHTuwf/9+REZGIiYmpsDpjBs3DsOGDcO1a9fQokULvHr1CnXr1sWvv/6KK1euYMCAAQgICEBUVBQAYMGCBfD19UX//v0RFxeHuLg4ODg4KKUbExODzp07o2vXrrh8+TKmTp2KSZMmSY1gublz56JevXqIjY3F4MGDMWjQIFx/By/WHz58iKSkJPj4+Ejr9PX1UadOHVy6dEltvMuXLsHH21thna+PjxQnIyMD169fh3eOdAHAO0cYuZiYGPg3b45On32Gb7/9Fk+ePHnTYr2zMjIycP3GDaW69fH2xqXLl1XGuXzlivK+8PbG1WvX8Pr1a5VxXr16hdeZmTA3M1Obl+fPnwMAzPII8yF59Cj7WPHyVjxWanvWweXL6o+VK5cvK8QBAG8fX7VxMjMzcWD/frz65x+4ubm/0WcTERHRh2HLli0YMWIEJkyYgNjYWDRq1AitWrXCvXv3VIa/c+cOWrdujUaNGiE2Nhbjx4/HsGHDEBERIYU5ffo0unTpgoCAAFy8eBEBAQHo3Lmz1L4qCkXyTOjz58+xZs0arF+/Hs2bNwcAhIaGokKFCgVOa8SIEfgsxzNvADBmzBjp/19//TX27t2LrVu3wtvbG+bm5tDX10fp0qVhZ2enNt158+bh448/xqRJkwAAVatWxdWrVzF79mz07t1bCte6dWsMHjwYQHaDeP78+YiMjET16tULXJaSlJSUBACwsrZWWG9lbY34uLg846mKI08vJSUFmZmZsLKyUghjbWWFpMRE6e8GDRrgk08+gZ2dHR49eoTly5dj0MCB2BAWBn19/Tcq27tIXb1ZWVkh8d+6zS0pKUll+MzMTKSkpMDGxkYpzuKlS1GmTBl41a+vMk0hBOYtWIDatWrBRcOzwR+KJ/JjRUVdqxsqDajfP09y7c/bt25hQL8+SE9Ph5GREWb9MBtOzs5v9NlERERUDN6C4bPz5s1D37590a9fPwBAcHAw9u3bh2XLlmHWrFlK4ZcvX46KFStKc+XUqFED586dw5w5c9CpUycpjebNmyMoKAgAEBQUhKNHjyI4OFhhxKk2FUlN3r59G+np6fD19ZXWWVlZoVq1agVOq169egp/Z2ZmYubMmfDw8IC1tTVMTEywf/9+ta1/da5du4aGDRsqrGvYsCFu3ryJzMxMaZ2Hh4f0f5lMBjs7O4Xn63JKS0tDamqqwpKWllagfGnLnj170LhRI2mR95TlfiZWCKFxZq7cW4UQSumoSjfnOn9/f3z00UdwcXFB48aNsXDhQty7dw8nTpwoYMneL5rqLT/h/92gFDZ0wwbsO3AAs2fNgoGBgcr0fpwzB7du3cLMGTMKmPP3x769e/CxX2NpUXusQORjEjvN+7NipUoI3bARK9esxaefdcK306fizr8TSUmpFOqziYiI6F1TkPZDenp69shCf3+F9f7+/jh16pTKOKdPn1YK36JFC5w7d06ab0ddGHVpakOR9IRKF8Z5kMlkSuFUTTxkbGys8PfcuXMxf/58BAcHw93dHcbGxhgxYgTS09MLnEe1F/Q56OnpKeU7KytLZZqzZs3CtGnTFNZNmTIFo0aPLlDetKFx48Zwc3OT/pbXT1JiokKPWfKTJ7DO1euSk3WOXs+cceQ9NRYWFtDV1VUK8yQ5WakHNScbGxvY29vjfgFvHrwv1NVbcnKy2v2hcl8kJ0NXVxcWuZ6v3bBxI9aFhmLpokWoUqWKyvR+nDMHx44fx8rly2FbtuwblObd9lGjxnB1zXGsZPx7rCQl5TpWkmFlpf47bW1tjSdPlPePZa79qaenhwr/PiJQo0ZNXLt2FT9t2YxxQeOlY6agn01ERERFTxTBHWF17YepU6cqhU1MTERmZiZsbW0V1tva2qodMRUfH68y/OvXr5GYmAh7e3u1YYpyFFaR9IS6uLhAT08PZ86ckdYlJyfjjz/+kP4uU6YM4nIMA7158yZevnypMe3jx4+jQ4cO6NGjB2rVqgVnZ2fcvHlTIYy+vr5Cb6YqNWvWVOqFO3XqFKpWrQpdXV2N+VAlKCgIT58+VVjk3drFzdjYGA4ODtLi7OwMa2trhbHdGRkZOH/+vEJvb27uHh5K48HPREVJcfT09FC9enWlMNE5wqiSkpKCx48fqxxC+iHQ09ND9WrVEBUdrbA+KjoaHu7uKuO4u7kphT8TFYWaNWqgVKn/7ietDwvD6rVrsSg4GDVr1FBKRwiBH+bMwZGjR7Fs8WKUL1dOCyV6dxkbG6OCg4O0ODllHytnoxWPlQux5+Hurv477ebujrNKx8GZPOMA2fsj49+Gb7ly5Qv12URERFT0MrO0vxSm/aCtkXQ51xc0zTdVJD2hJiYm6Nu3L7755htYW1vD1tYWEyZMgI7Of23eZs2aYfHixfDx8UFWVhbGjRun1OuoiouLCyIiInDq1ClYWlpi3rx5iI+PR40cF9uOjo6IiorC3bt3YWJiovR8FQCMHj0a9evXx4wZM9ClSxecPn0aixcvxtKlSwtdbgMDA5XDHtMK2EtbFGQyGbp164Z169bBoWJFODg4IGTdOhgaGqJFy5ZSuCmTJ6NM2bIYOnQoAKBr1674asAAhIaEoImfH45GRiI6Kgqr16yR4nT/8ktMmTwZNWvUgLuHB3Zs3474+HhpnPnLly+xcuVKNGvWDDY2Noh79AhLli6FhYUF/HK9b/RD8mW3bpg8bRpq1KgBDzc3bN+1C/GPH6PTp58CyH6eM+HvvzF9yhQAQKfPPsNP27ZhXnAwPu3QAZeuXMGuX37BzOnTpTRDN2zA8pUr8e20abC3t5eeLy1tZITSpUsDAH6YPRt79+/H3B9/RGljYymMibExDA0Ni7MK3koymQydu3bD+pB1cPi3Ybo+JASGhoZo3qKFFG761CkoU6YMBg3JPlY6d+mKwQO/wob1oWjUuAmOHzuKs9HRWL5ytRRn+dIl8PFtAFtbW7x8+RIHDuxH7PnzmBe8sECfTURERO8Hde0HVWxsbKCrq6vUQ5mQkKDUkylnZ2enMnypUqVg/e8ILHVh1KWpDUXSCAWA2bNn4/nz52jfvj1MTU0xevRoPH36VNo+d+5cBAYGonHjxihXrhwWLFiQr9lzJ02ahDt37qBFixYoXbo0BgwYgI4dOyqkPWbMGPTq1Qs1a9bEP//8gzt37iilU6dOHfz000+YPHkyZsyYAXt7e0yfPl1hUqL3Tc9evZCWloYfvv8ez549g6ubGxYtXqww5Dk+Ph6yHDcLatWqhZkzZ2LZsmVYvnw5KlSogO9mzVIY6uvv74+nT59i9erVSExMROXKlRG8YAHs7e0BADo6Orh96xZ2//Ybnj17BhsbG9StVw/fffed0nDrD4l/8+bZ9bZmDRKTklDZ2RkL5s2T6i0xMVHhhFC+XDksmDcP84KDsTUiAmVsbDBm1Ch83KyZFGZbRAQyMjIwbvx4hc/q37cvvurfPzvMv6+A+erfCbfkpkyciHZt2xZJWd81PQJ6Ii0tDXN+/AHPnj1DTVdXzF+4SOH7+vhxPHRyTK3u7lEL02bMxMoVy7BqxXKUr1ABM2Z+B9ccx8qTJ08wfdoUJCUmwtjEBC4uLpgXvBBeOWY9zs9nExERUfET0PzIYVHS19dH3bp1ceDAAXz6b6cFABw4cAAdOnRQGcfX1xe//PKLwrr9+/ejXr16Ugegr68vDhw4gJEjRyqEadCgQRGUIptM5OcBTnojqc+elXQWKBczU1M8S04u6WxQLqaWlkhKSS3pbFAu1hZ8fQ8REVFRXKMU9Dd2y5YtCAgIwPLly+Hr64uVK1di1apV+P3331GpUiUEBQXh4cOHWL9+PYDsV7S4ubnhq6++Qv/+/XH69GkMHDgQ4eHh0qjFU6dOoXHjxpg5cyY6dOiAXbt2YeLEiThx4gS8c70eUFuKrCeUiIiIiIiItKdLly5ISkrC9OnTERcXBzc3N+zevRuVKlUCAMTFxSm8NcTJyQm7d+/GyJEjsWTJEpQrVw4LFy6UGqBA9qsUN2/ejIkTJ2LSpEmoXLkytmzZUmQNUIA9ocWCPaFvH/aEvp3YE/p2Yk8oERER8HfyU82BCqiMpbnmQO+hkn/jKhEREREREX0wOByXiIiIiIhIAw4g1R42QomIiIiIiDTIYiNUazgcl4iIiIiIiIoNe0KJiIiIiIg0YEeo9rAnlIiIiIiIiIoNe0KJiIiIiIg04MRE2sNGKBERERERkQacmEh7OByXiIiIiIiIig17QomIiIiIiDRgR6j2sCeUiIiIiIiIig17QomIiIiIiDTgxETaw0YoERERERGRBpyYSHs4HJeIiIiIiIiKDXtCiYiIiIiINGA/qPawEUpERERERKQBh+NqD4fjEhERERERUbFhTygREREREZEGnB1Xe9gTSkRERERERMWGPaFEREREREQasCNUe9gIJSIiIiIi0oATE2kPh+MSERERERFRsWFPaDEwMzUt6SyQCqaWliWdBVLB2sKspLNAREREpIQTE2kPG6HF4FlqaklngXIxNTND8tNnJZ0NysXS3BSJN34v6WxQLjbVXHH/cVJJZ4NycbC1LuksEBERFQoboURERERERBqwI1R72AglIiIiIiLSgBMTaQ8nJiIiIiIiIqJiw55QIiIiIiIiDTgxkfawEUpERERERKRBFtugWsPhuERERERERFRs2BNKRERERESkgQC7QrWFPaFERERERERUbNgTSkREREREpAEnJtIeNkKJiIiIiIg04MRE2sPhuERERERERFRs2BNKRERERESkAYfjag97QomIiIiIiKjYsCeUiIiIiIhIA/aEag8boURERERERBpksRGqNRyOS0RERERERMWGPaFEREREREQasCNUe9gTSkRERERE9B5JTk5GQEAAzM3NYW5ujoCAAKSkpKgNn5GRgXHjxsHd3R3GxsYoV64cevbsiUePHimE8/Pzg0wmU1i6du1a4PyxEUpERERERKRBlhBaX4pK9+7dceHCBezduxd79+7FhQsXEBAQoDb8y5cvcf78eUyaNAnnz5/H9u3b8ccff6B9+/ZKYfv374+4uDhpWbFiRYHzx+G4REREREREGrwrs+Neu3YNe/fuxZkzZ+Dt7Q0AWLVqFXx9fXHjxg1Uq1ZNKY65uTkOHDigsG7RokXw8vLCvXv3ULFiRWl96dKlYWdn90Z5ZE8oERERERFRCUhLS0NqaqrCkpaW9kZpnj59Gubm5lIDFAB8fHxgbm6OU6dO5Tudp0+fQiaTwcLCQmH9xo0bYWNjA1dXV4wZMwbPnj0rcB7ZCCUiIiIiItJACO0vs2bNkp7blC+zZs16o3zGx8ejbNmySuvLli2L+Pj4fKXx6tUr/O9//0P37t1hZmYmrf/yyy8RHh6OyMhITJo0CREREfjss88KnMcPZjju1KlTsXPnTly4cKGks0JERERERO+YoniGMygoCKNGjVJYZ2BgoDLs1KlTMW3atDzTO3v2LABAJpMpbRNCqFyfW0ZGBrp27YqsrCwsXbpUYVv//v2l/7u5uaFKlSqoV68ezp8/jzp16mhMW+6DaYSOGTMGX3/9dUlno8Rs3boVG8LCkJiYCGdnZ4weNQqenp5qw8fExGB+cDD+/PNPlLGxQUDPnvi8UyeFMIcOH8by5cvx4MEDVKhQAYMHDULTpk2l7StWrsSqVasU4lhbWWHfvn3aLdw7TgiB1atWYtfOHXj27Blqurrim2/Gwbly5TzjHT58CCtXLMfDBw9QvkIFDBw4GH456j/2/HmEhW3AjevXkJiYiB9+nIMmfn4KaUyfNhW7f/tVYZ2rmxvWrA3RVvHeSdt378Gm7buQlJwMp4oOGNavD2q71lQZNvHJEyxeG4rrt2/jwaM4fN62NUb076sU7tnzF1gZthFHT5/Bs+cvYG9bFkP79EaDenUBAOu3RuDo6TP46+FDGOjrw716dQzqFYBKFcoXaVnfJUIIrF+3Brt/+RnPnqWiek1XDBs5Go5OznnGOxZ5BCFrViHu0UPYlyuPPv2/wkeNm0jbM1+/Rui6NTh8YD+ePEmClbUNWrRqjS979oaOTvaAoR+/+xb79+5WSLd6TVcsXq54jiMiIioIAwMDtY3O3IYOHapxJlpHR0dcunQJjx8/Vtr2999/w9bWNs/4GRkZ6Ny5M+7cuYPDhw8r9IKqUqdOHejp6eHmzZtshKpiYmICExOTks5Gidi/fz/mzpuH/40bh1q1amH79u0YNnw4tv70k8qHih8+fIjhI0bg044dMWP6dFy8eBHf//ADLC0t8XGzZgCAS5cuYfz48Rj41Vdo2rQpjhw5gv8FBWHN6tVwc3OT0nJ2dsbSJUukv3V1dYu+wO+YDetDER6+CZMmT0HFihWxbu0aDPt6CLZsjYCxsbHKOJcvXcKkCeMx4KuBaOLXFEcjj2DC+P9hxao1Uv3/8+ofVKlSBW3btUPQuLFqP9/HtwEmTZos/V1KT0+7BXzHHDx+AgtWr8Pogf3hUaMGdu7dhzHTvkXYkgWwK1NGKXxGxmtYmJuh1xedsGXXrypSzD6hj5g8FZYW5vh23Dcoa2ONx4lJKG1kKIW5cOV3fNamFWpUcUFmZiZWbtiEkVOmYeOShTAyNFSZ7odmy6YwRPy0Gd8ETUQFBwdsXB+CcaNGYN3GcJQurfpYuXrlMr6dNhm9+/bHR40a48TxY5gxZSKClyxHjZquAIDNm8Lw6887MXb8RDg6OuOPG9cwe9Z3MDY2xmdfdJHSqu/tg2/+N0H6+0M/VoiIPjQCJTsxkY2NDWxsbDSG8/X1xdOnTxEdHQ0vLy8AQFRUFJ4+fYoGDRqojSdvgN68eRNHjhyBtbW1xs/6/fffkZGRAXt7+/wXBIV4JjQrKws//PADXFxcYGBggIoVK2LmzJkAgHHjxqFq1aooXbo0nJ2dMWnSJGRkZEhxp06ditq1a2Pt2rWoWLEiTExMMGjQIGRmZuLHH3+EnZ0dypYtK6UnJ5PJsGzZMrRq1QpGRkZwcnLC1q1bFcLk97PlXr9+jWHDhsHCwgLW1tYYN24cevXqhY4dO0ph/Pz8MGzYMIwdOxZWVlaws7PD1KlTC1plJW7jpk3o0KEDOnbsCCcnJ4wePRq2trbYtm2byvAR27fDzs4Oo0ePhpOTEzp27Ij27dsjLCxMChMeHg5vLy8EBgbC0dERgYGB8KpfH5vCwxXSKqWrKx0wNjY2sLS0LNKyvmuEENiyORy9eweiadNmqFzZBZOnTMOrV6+wf99etfE2bw5HfS9v9OqdXf+9egeifn0vbNm8SQrToEFDDBw0GE2bNsszD/p6erC2sZEWc3NzrZXvXbRl1y9o+8nHaO/fHI4OFTCif1+UtbHGjt2qe/DtbctiRP++aNWsKUyMS6sM8+vBw0h9/hzfj/8fPGrWgF3ZsqhVswaqODlJYeZNm4w2HzeDc8WKqOLkhPHDh+Lx34m4cet2kZTzXSOEwPatP6F7QC80auIHJ+fKGDt+El6lvcLhXLP55RSx9SfUrVcf3Xv0RMVKjujeoyc869bD9q1bpDBXf7+CBg0bwce3Iezs7dHYrxnq1vfCHzeuK6Slp6cHK2tradF0d5iIiKgk1KhRAy1btkT//v1x5swZnDlzBv3790fbtm0VZsatXr06duzYASC7bfT555/j3Llz2LhxIzIzMxEfH4/4+Hikp6cDAG7fvo3p06fj3LlzuHv3Lnbv3o0vvvgCnp6eaNiwYYHyWOBGaFBQEH744QdMmjQJV69exaZNm6RuXVNTU4SEhODq1atYsGABVq1ahfnz5yvEv337Nvbs2YO9e/ciPDwca9euRZs2bfDgwQMcPXoUP/zwAyZOnIgzZ84oxJs0aRI6deqEixcvokePHujWrRuuXbsmbc/PZ+f0ww8/YOPGjVi3bh1OnjyJ1NRU7Ny5UylcaGgojI2NERUVhR9//BHTp09Xmr74bZaRkYHr16/DJ8fsWADg4+2NS5cuqYxz+fJlpfC+Pj64evUqXr9+DQC4dPkyvH18FNP09VVK8979+2jZqhXad+iAoPHj8eDBgzct0nvl0aOHSEpKUqhLfX19eNapg8tq9g8AXLl8SWHGMwDw9vHJM44658/HoFWL5vii02f4bua3ePLkSYHTeF9kZGTgxq3b8PKspbDey7M2rly/riaWZieiz8KtWjXMXb4KbQMC0WPocIT+tA2ZmZlq47x48RIAYGb6YY7gyC0u7hGePElC3fpe0jp9fX141KqN369cVhvv6u9XFOIAQD0vb4U4bu4eiD1/Dg/u3wMA3L51E1cuX4SXj69CvIsXYvF5+9bo1b0L5v44C8nJH+6xQkT0ISqKiYmKysaNG+Hu7g5/f3/4+/vDw8MDGzZsUAhz48YNPH36FADw4MED/Pzzz3jw4AFq164Ne3t7aZHPqKuvr49Dhw6hRYsWqFatGoYNGwZ/f38cPHiwwKMdCzQc99mzZ1iwYAEWL16MXr16AQAqV66Mjz76CAAwceJEKayjoyNGjx6NLVu2YOzY/4YCZmVlYe3atTA1NUXNmjXRtGlT3LhxA7t374aOjg6qVauGH374AZGRkfDJcWH+xRdfoF+/fgCAGTNm4MCBA1i0aJH0sGx+PjunRYsWISgoCJ9++ikAYPHixdi9e7dSOA8PD0yZMgUAUKVKFSxevBiHDh1C8+bNlcKmpaUpTamc3zHeRSUlJQWZmZmwsrJSWG9lbY3EpCSVcZKSkmCVq/vdysoKmZmZSElJgY2NDZKSkmCdK01rKysk5UjTzdUV06ZNQ6WKFZGUlIQ1a9eib9++2LJli9JUzx8qeX1ZWeWub2vEx8XlGU9VnCQ1+1Qd3wYN8PHHn8DO3g6PHj3CyuXLMXTwQISsD4O+vn6B0nofpKQ+Q2ZWFqxyfT8tzc2RlJJS6HQfxT/G+YTL8G/SGHOmTMSDR3GYu2IlMrOy0KdrZ6XwQggsXLsOHjVrwLlSpUJ/7vskOSm7wWeZ67xjaWWFx3nM9Jf8JAmWlrniWFohOcfNlq5fBuDFixcI7NENOjo6yMrKQmD/r9DsE38pTH1vHzRu2hS2tnaIj4tDyJpV+GbE11i6at0HeawQEX2IimJioqJiZWWlMIpRlZzvPXV0dNT4HlQHBwccPXpUK/krUCP02rVrSEtLw8cff6xy+7Zt2xAcHIxbt27h+fPneP36tdJwJUdHR5iamkp/29raQldXV5r8Qb4uISFBIZ6vr6/S3zlnus3PZ8s9ffoUjx8/lsZIA9nPKtatWxdZWVkKYT08PBT+tre3V8qb3KxZs5RmrJoyZQpG55rxqiTknglL0+xYubeo/EpqSDNnt7yLiws8PDzQsWNH/Prbb+jx5Zf5zfp7Ze/ePfhh1nfS33PnBwMo+P7JjqP4t0D+ZjzLqXnz/y6yK1d2QY0aNdGxfVucPHlC4zDe95mqepQpHRX5J0QWLM3NMXbIQOjq6qK6S2UkPnmCTTt2qmyEzluxCrfv/oVl389UkdqH4dD+fZg/90fp75k/zAGgvB8Kc6xACIWVkYcP4tD+fRg/eSoqOTrj9q0/sHTRAthY28C/VWsAQNOPP5HCOzlXRtVq1fFl588QdfoUGjXxK0QJiYiIPlwFaoQaGRmp3XbmzBl07doV06ZNQ4sWLWBubo7Nmzdj7ty5CuH0ck3kIJPJVK7L3RhURX7hkd/PVhdfTlXrvyB5UzfFcvobvnD2TVhYWEBXV1ephyz5yROlnkw5a2vlHrXkJ0+gq6sr9WCqCvMkOVmpxzUnIyMjVHZxwf379wtRkvdDo0aN4er638RNGf+OsU9KSlR40Dw5+UmedaluH+UVJz9sbGxgZ2+P+/fuvVE67yoLM1Po6uggKTlZYX3y06ewsij8s7LWlpYoVaqUwlCVSg4VkJScgoyMDIXzzLwVq3Ai+iyWfPctyuZj8oH3le9HH6H6vxMHAUBGRvax8uRJEqxz1EtKcrJST2dOllbWSkPMk1OSFZ5PX7l0Cbp+GYCmH2ePcHGuXBmP4+MRvnG91AjNzdrGBra2dnj44MM9nxERfWg09RRS/hXomdAqVarAyMgIhw4dUtp28uRJVKpUCRMmTEC9evVQpUoV/PXXX1rLaO5nRM+cOYPq1asX6rPNzc1ha2uL6OhoaV1mZiZiY2PfKI8GBgYwMzNTWEp6OK6enh6qV6+OqKgohfVR0dFKvbxy7u7uiMpRNwBwJioKNWvWRKlS2fctPNzdldM8c0ZtmgCQnp6Ou3fvwiYfM229r4yNjeHg4CAtTs7OsLa2RnSOuszIyEDs+fNwz6Mu3dw9EB2dq/6jovKMkx9PU1KQ8PhxvmZeex/p6emhmktlnL1wUWH92QsX4fbv+aYw3GtUx4O4OIUbWPcfPoK1laXUABVCYO7yVTh6OgoLv52GcnZ5T6H+vitd2hjlK1SQlkqOTrCyssb5c2elMBkZGbh08QJc3dzVplPT1Q3nz55VWBdzNlohzqu0V5DpKN6U1NHVRVaW+ouNp0+fIuHvBFhZf5jHChER0ZsoUE+ooaEhxo0bh7Fjx0JfXx8NGzbE33//jd9//x0uLi64d+8eNm/ejPr16+O3336TZlvShq1bt6JevXr46KOPsHHjRkRHR2PNmjUAUKjP/vrrrzFr1iy4uLigevXqWLRoEZKTkws8nPFd8GX37pg8ZQpq1KwJD3d3bN+xA/Hx8ej073s/Fy9ejIS//8b0f4cSd/rsM/z000+YN38+Pu3YEZcuX8auXbsUZi3u2rUrBnz1FUJCQ+HXpAkijx5FVHQ01qxeLYUJDg5Go0aNYGdnh+TkZKxZswYvXrxA27Zti7cC3mIymQxdunZDaMg6ODhUhENFB4SuWwdDQ0P4t2gphZs2ZTLKlC2LwUOGAgC6dO2KQV8NwPrQEDRu4odjRyNxNjoKK1atkeK8fPkSD3L00jx69BB//HEDZmbmsLOzw8uXL7F61Uo0bdoM1jY2iIt7hOVLl8LcwgJN/P573+iHpkuHdpgxfyGqu7jArXo17Nq3H4//TsSnrbKHLi8LDUPikyRMGjlcivPHn3cAAC9fvUJKair++PMO9EqVglNFBwDAp61aYttvuxG8ag0+b9sGDx49wvqtEfiiXRspjbnLV+LAseP4fkIQShsZSb2xJqVLl/jNrLeBTCbDZ190xqaw9ShfwQHlK1TAprD1MDQwRLMcz+h/P3M6bGzKoN9XgwAAn33eGSOHDcbmjRvQ4KNGOHXiOM6fO4vgJculOL4NPsKmDaEoa2sLR0dn3Lr5ByK2bEbL1tn755+XL7F+3Ro0auIHK2sbxMfHYe3K5TA3N8dHjRsXb0UQEVGJyePeJBVQgd8TOmnSJJQqVQqTJ0/Go0ePYG9vj4EDB6Jv374YOXIkhg4dirS0NLRp0waTJk3S2itNpk2bhs2bN2Pw4MGws7PDxo0bUbNm9svjO3ToUODPHjduHOLj49GzZ0/o6upiwIABaNGixXv5Hkt/f388ffoUq1evRmJiIipXrowFwcHS+3wSExMRn2Nij/Lly2NBcDDmzZ+PrVu3okyZMhgzZoz0jlAAqFWrFmbOnIlly5Zh+fLlqFChAmZ9953CO0IfJyRgwsSJSElJgaWlJdzc3LBu7doCv0fofRfQsxfS0tIw+8fv8ezZM7i6umHBosUK7wiNfxwPWY7npj08amHGtzOxYvkyrFyxHOUrVMC3381SqP9r165iyKCB0t8LgrNni27dpi0mT5kKHR0d3L51C3t2/4Znz57BxsYGderWw7fffaf2/aQfgk8afYTUZ8+wbstPSHqSDOdKFTFn8gTYlS0LAEhKTsbjvxMV4gSOGC39/8at2zhw9DjsypZBxOoVAADbMjYInjYFC1avRa9hI2FjbYUv2rVBj06fSvF27Ml+BczQ8ZMU0h4/fCjafPzhPp+bU5fuPZCWloaF8+bg2fNnqFGjJr6fO1/hHaEJjx9DR/bfseLq7o6JU6Zh3eqVCFmzCuXKlcfEqTOkd4QCwNARIxGyehUWzpuDlORkWNvYoE37Dgjo3QdAdq/on3/exoF9e/D8+XNYWVujtmddTJw6Q+37SYmI6P3D4bjaIxPvQG3KZDLs2LFD4R2e2paVlYUaNWqgc+fOmDFjhlbTfpaaqtX06M2Zmpkh+emzks4G5WJpborEG7+XdDYoF5tqrrj/uGAzL1PRc7D9cB9tICIqCQdib2g9zeae1TQHeg8VuCf0ffHXX39h//79aNKkCdLS0rB48WLcuXMH3bt3L+msERERERHRW+Yd6Lt7ZxRoYqL3iY6ODkJCQlC/fn00bNgQly9fxsGDB1GjRo2SzhoREREREdF7653oCS2Kuw4ODg44efKk1tMlIiIiIqL3Dycm0p53ohFKRERERERUkjgcV3s+2OG4REREREREVPzYE0pERERERKRBFntCtYaNUCIiIiIiIg3YBNUeDsclIiIiIiKiYsOeUCIiIiIiIg04MZH2sCeUiIiIiIiIig17QomIiIiIiDTgxETaw0YoERERERGRBmyDag+H4xIREREREVGxYU8oERERERGRBpyYSHvYE0pERERERETFhj2hREREREREGnBiIu1hI5SIiIiIiEgDDsfVHg7HJSIiIiIiomLDnlAiIiIiIiINstgRqjVshBIREREREWnA4bjaw+G4REREREREVGzYE0pERERERKQBe0K1hz2hREREREREVGzYE0pERERERKRBVkln4D3CRmgxMDUzK+kskAqW5qYlnQVSwaaaa0lngVRwsLUu6SwQERGVKA7H1R42QotBUkpqSWeBcrG2MMOzVO6Xt42pmRnuxSeWdDYol4p2Nkh99qyks0G5mJma8nh5C1W0synpLBARvfXYCCUiIiIiItKAPaHaw4mJiIiIiIiIqNiwJ5SIiIiIiEiDLHaEag0boURERERERBpwOK72cDguERERERERFRv2hBIREREREWmQxZ5QrWFPKBERERERERUb9oQSERERERFpwI5Q7WEjlIiIiIiISANOTKQ9HI5LRERERET0HklOTkZAQADMzc1hbm6OgIAApKSk5Bmnd+/ekMlkCouPj49CmLS0NHz99dewsbGBsbEx2rdvjwcPHhQ4f2yEEhERERERaZAlhNaXotK9e3dcuHABe/fuxd69e3HhwgUEBARojNeyZUvExcVJy+7duxW2jxgxAjt27MDmzZtx4sQJPH/+HG3btkVmZmaB8sfhuERERERERBq8K6Nxr127hr179+LMmTPw9vYGAKxatQq+vr64ceMGqlWrpjaugYEB7OzsVG57+vQp1qxZgw0bNuCTTz4BAISFhcHBwQEHDx5EixYt8p1H9oQSERERERGVgLS0NKSmpiosaWlpb5Tm6dOnYW5uLjVAAcDHxwfm5uY4depUnnEjIyNRtmxZVK1aFf3790dCQoK0LSYmBhkZGfD395fWlStXDm5ubhrTzY2NUCIiIiIiIg2KYjjurFmzpOc25cusWbPeKJ/x8fEoW7as0vqyZcsiPj5ebbxWrVph48aNOHz4MObOnYuzZ8+iWbNmUqM4Pj4e+vr6sLS0VIhna2ubZ7qqcDguERERERFRCQgKCsKoUaMU1hkYGKgMO3XqVEybNi3P9M6ePQsAkMlkStuEECrXy3Xp0kX6v5ubG+rVq4dKlSrht99+w2effaY2nqZ0VWEjlIiIiIiISIOimEjIwMBAbaMzt6FDh6Jr1655hnF0dMSlS5fw+PFjpW1///03bG1t8503e3t7VKpUCTdv3gQA2NnZIT09HcnJyQq9oQkJCWjQoEG+0wXYCCUiIiIiItKopN8TamNjAxsbG43hfH198fTpU0RHR8PLywsAEBUVhadPnxaosZiUlIT79+/D3t4eAFC3bl3o6enhwIED6Ny5MwAgLi4OV65cwY8//ligsvCZUCIiIiIiovdEjRo10LJlS/Tv3x9nzpzBmTNn0L9/f7Rt21ZhZtzq1atjx44dAIDnz59jzJgxOH36NO7evYvIyEi0a9cONjY2+PTTTwEA5ubm6Nu3L0aPHo1Dhw4hNjYWPXr0gLu7uzRbbn6xJ5SIiIiIiEiDd+UVLQCwceNGDBs2TJrJtn379li8eLFCmBs3buDp06cAAF1dXVy+fBnr169HSkoK7O3t0bRpU2zZsgWmpqZSnPnz56NUqVLo3Lkz/vnnH3z88ccICQmBrq5ugfInEyXdr/wBSEpJLeksUC7WFmZ4lsr98rYxNTPDvfjEks4G5VLRzgapz56VdDYoFzNTUx4vb6GKdpqHyhHRu2nxbwV7DUl+DG1TsGcp3xfsCSUiIiIiItKgKCYm+lCxEUpERERERKSBABuh2vJeTUzk6OiI4ODgks4GERERERERqcGe0A+IEAJrVq/Czzt3IPXZM7i6umL0N2Ph7Fw5z3hHDh/GqhXL8fDhA5QvXwFfDRqEJn5Npe3rQ9YhMvII7v31F/QNDODu7oHBQ4eiUiVHlen9MOs77Nq5A8NHjESXbt21WcR3ztatW7EhLAyJiYlwdnbG6FGj4OnpqTZ8TEwM5gcH488//0QZGxsE9OyJzzt1Ughz6PBhLF++HA8ePECFChUweNAgNG363/7atm0btkVEIC4uDgDg7OyMfn37omHDhkVTyHeQEAIbQtbit1924fmzZ6he0xVfjxgFRyfnPOMdP3oEIWtWI+7RQ9iXK4/AfgPwUeMm0vaXL18gZM0qnDx+DCnJyXCpUhWDvx6BajVqSGHWr1uDyMMH8XdCAkqV0kOVatUQ2G8AatR0LbLyviuEEFi1ciV27NiBZ/+ew8aOG4fKlfM+hx0+dEjhmBg0eLDCMQFkH4thGzZIx+Ko0aOlY/H169dYtnQpTp48iYcPH8LExAReXl4Y+vXXKFOmTJGV911RVMdL5uvXWB+yFocP7MeTJ0mwsraBf8tW+LJnb+joZN9DT37yBKtWLEXM2Wi8eP4c7rVqY8jwkahQwaFIy0xEHyaOxtWe96onlPIWtmE9Nm/ahFFjvsGadSGwsrLGiK+H4sWLF2rjXL58CZMnjkfLVq0QGrYJLVu1wsTxQfj9yhUpTGzseXT6/AusXLMWCxYuRmZmJkYM+xr//POPUnpHj0bi6u9XYMMLN+zfvx9z581Dn8BAbAwLg2ft2hg2fDji4+NVhn/48CGGjxgBz9q1sTEsDIGBgZgzZw4OHT4shbl06RLGjx+P1q1aIXzTJrRu1Qr/CwrClRz7q2zZshg6dCjWh4ZifWgo6tWrh9FjxuD27dtFXuZ3xZbwjYj4aTOGjhiFxSvWwMrKCuNGj8DLl+qPlatXruDbaVPwiX8LLF8Tik/8W+DbqZNw7ervUph5P36P8+fOYtyEyVi5bgPq1vfC2NHDkfj331KYChUcMHT4KKxctx7zFy+FrZ0d/jdmJFJSkou0zO+C9aGh2LRpE74ZOxYhoaGwtrbG0CFD8jyHyY+JVq1bY1N4OFq1bo2g//1P4ZjYv38/5s2di8A+fRC2cSNqe3pi+LBh0rH46tUrXL9+HX379cOGsDD8OHs27t27h9GjRhV5md8FRXW8bA7fiF9/3omhI0ZhzfpN6D9wMLZu3oSd27cByG78TpnwP8Q/eoTpM3/AstXrYGtrh3Gjhqv8/SEiorfHW9sI9fPzw9ChQzF06FBYWFjA2toaEydOlF4Sm5CQgHbt2sHIyAhOTk7YuHGjUhrz5s2Du7s7jI2N4eDggMGDB+P58+cAgBcvXsDMzAzbtm1TiPPLL7/A2NgYz549Q3p6OoYOHQp7e3sYGhrC0dERs2bNKvrCFwEhBH7aHI5egYHwa9oMlSu7YNKUqXj16hUO7NunNt5Pm8NR38sLPXsHwtHRET17B6Je/frYsjlcCjN/wSK0adsOzs6VUaVqVUyYNBmP4+Nx/fo1hbT+TkjAvNmzMWX6DJQqxU74jZs2oUOHDujYsSOcnJwwevRo2NraKn0n5SK2b4ednR1Gjx4NJycndOzYEe3bt0dYWJgUJjw8HN5eXggMzN5fgYGB8KpfH5vC/9tfjRs3xkcNG6JSpUqoVKkShgwejNKlS+NyjovyD5kQAju2/oRuAb3QqLEfnJyd8U3QRKSlpeHwwQNq423ftgV169ZHtx49UbFSJXTr0ROedeth+9afAABpaWk4fuwo+g8cAo9atVG+QgX0DOwLO3t7/LJrh5ROs+b+qFOvPuzLlYejkzMGDhmGly9e4M8P/CaBEALh4eEIDAxEs2bN4OLigqnTpuHVq1fYt3ev2njh4eHw8vZWOCbqe3khfNMmKcymjRvzPBZNTEywZOlSNG/eHI6OjnB3d8eYb77BtWvX1N40+lAU1fECANd+v4IGDRvB27cB7Ozt0djv/+3dd1QVRxsH4N8FadKLFAUFEZFqpdlRFEuMxl5R7F1jRz8FK8YCBhu2CCrWqFFjw9hLKGosYENBsQBKFQRp9/3+IGy8FNGEJrzPOXuSu3dm78yOs+zslHVEcxtbPHn0CADw+tVLPHwQjqkzZsHUzAwGdethyo8zkZGRgYvni/9txhj7t8REpb5VV5W2EQoA/v7+qFGjBoKDg+Hj4wNvb29s374dADBixAg8f/4cFy5cwK+//opNmzbh7du3EvGlpKTg4+ODsLAw+Pv748KFC5gzZw4AQFFREQMHDsTOnTsl4uzcuRN9+/aFsrIyfHx8cPz4cRw8eBCPHz/Gnj17YGhoWC55L21v3rxGQkICbO3shX2ysrJo0rQZ7t+/V2y8sPv3JeIAgJ29w2fjfPi7oa+ioiLsE4vFWOzhjsFDh5Y4/Lc6yM7OxqNHj2BvZyex397ODvfuFX1u79+/Xyi8g709Hjx4gJycHADAvfv3YWcvWV72Dg7FHjM3NxdnAwORkZEBayurf5udKiU25g0SExPQooWtsE9WVhbWjZvgQdj9YuM9CA9HcxsbiX0tbGzxIDwvTm5uDsS5uZCRlZUIIycrh7Bi6lN2djZOnTgGRSUlGBs3+LdZqhJev867htnbS17DmjVrVuy/bwC4f+9ekfUmP05+XSxYb+w+CVOUtLQ0iEQiKCkp/ZvsVBllVV8AwNLKGn/dvolXL6MBAM+eRiDs/j3Y2jsAALKzsoXfyyctLQ2ZGjLF1inGGPsviKjUt+qqUndHGRgYwNvbGyKRCKamprh//z68vb3Rrl07nD59GkFBQbD7++Zix44dMPtkXhUATJ8+Xfh/IyMjLF26FBMmTMCmTZsAAKNHj0bLli3x5s0b1K5dG/Hx8fj9999x7lzeE9To6GiYmJigdevWEIlEqFev3mfTm5mZiczMTIl9cnJy//U0lIrEhAQAgIaGhsR+DQ2Nzz7JT0hIKDJO/vEKIiL4/OyNxo2bSNw079nlD2lpafQfMPDfZqFKSU5ORm5ubuFzq6mJ+GLObUJCAjQ0NSXDa2ggNzcXycnJ0NLSQkJCAjQLHFNTQwMJBY759OlTuI4ciaysLCgoKGD16tWoX//z87eqi8TERACAmoa6xH51dQ3ExRVfV5ISE6CuLnnu1dU1kPT38WrWVIS5hSUCdvmhbr16UFfXwMXzf+DRwweoo68vES/oxnUsX+KOzI8foaGpiZ/WrIOqmlop5O7blf9vuFAd0NRE7N/zm4uLV1Sc/OMVVxc1NTSQEF/0OzgzMzOxccMGOHfpUu0boWVVXwBgwOCh+PAhDSOHDYaUlBTEYjFcR49FB6dOAACDevWgo6uLHVu3YPqs2ZCXV8Dhg/uRmJhQ7N8oxhhjlUOl7gm1t7eHSCQSPjs4OCAiIgIPHz5EjRo10KJFC+G7Ro0aQa3ATdrFixfRqVMn1KlTB8rKynBxcUFCQoIwf8jW1hYWFhbYtWsXAGD37t2oW7cu2rZtCyCvt/XOnTswNTXF1KlTERgY+Nn0enp6QlVVVWKrqOG7Z8+cRsf2bYUtv6fs0/MJ5C01XWBXEQrEISp0nHxrV6/C06dPsXjpMmHfo4cPcfDAfvxvkXux8aqrQuXxmXMLFCwJFL1Q+Bccs169etgbEICdv/yCvn36wMPDA5GRkV+R8qrj/Lmz6NHFSdiKrSsllM3fkSTjgCT2zV2wEESEQX16oVsnR/x2+BA6OHWClJS0RLzGTZvBd7sf1m30hY2tPZZ5LERSUvWaE3r69Gm0bdNG2D5XLiVdxArVmyLK8kvLOycnBwvmz4dYLMbcuXO/MDdVR3nWl0sXzuN8YCDcFnpg87admO32Pxw6sA+BZ04BAGrUqIFFS5bj1ato9P6uK75z7oi7d/6CjZ29sHARY4yVJh6OW3oqdU9ocYr7o/epFy9eoFu3bhg/fjyWLl0KDQ0NXLt2DaNGjUJ2drYQbvTo0diwYQPmzZuHnTt3wtXVVThus2bNEBUVhdOnT+OPP/5A//794eTkVOycPTc3N8wosFCFnJwc0jIyiwxfllq3aQsLC0vhc1Z2FoC8XgEtLS1hf1JiEjQ0NAvFz6epqYnERMknyklJSVAv0GsAAF5rVuPa1SvYtGUrtHV0hP137/yFpKQk9O7ZQ9iXm5uL9T4/48CB/Tjy2/Gvz+A3Tk1NDdLS0oV6KJMSEwv1ZObT/KT35tPw0tLSwgOYosIkJiUV6uWRkZGBgUHe6pHm5uZ48OAB9u3fjwXz5/+XbH2THFq1RiOzf1aezf67riQlJEJT85+6kpycBHV19ULx86lraCKpQF1JTpKMU7uOPrx8NiIjIwPp6R+gqamFZR4LoaunJxFPQUEBdfT1UUdfH+YWlhg+eADOnDyBQUNd/lNevyVt27aFpeUn17Csv69h8fEFrmHF1xmg+HqTXyeKq4uJSUmFelBzcnLgNm8e3rx5g02bN1fLXtDyrC/bNm/EgCFD4djRCQBgZGyMt3Gx2B+wG527dAMANDRthC07/PEhLQ3ZOdlQU1PHlPFjYGLa6L9nljHGCqjGbcZSV6kfFQYFBRX6bGJiAktLS+Tk5ODmzZvCd48fP0ZycrLw+ebNm8jJycHatWthb2+Phg0b4s2bN4V+Y+jQoYiOjoaPjw/Cw8MxfPhwie9VVFQwYMAAbNu2DQcOHMDhw4eF4UcFycnJQUVFRWKrqOG4ioqK0DcwEDYjo/rQ1NREaEiwECY7Oxt3/roNKyvrYo9jaWWF0OBgiX0hwUEScYgIa1evwqVLF7F+42bUrl1HInyXbt2wK2Av/HbvETatWrUweOhQeP/sU0o5/rbIyMigUaNGCC5wboNDQmBtXXR5WFlZITgkRGJfUHAwzM3NhYWerK2sCh8zKKjYY+YjImT/fZNf3dSsqSg0+Oro66OeoRE0NDRx62aoECY7Oxv37t6BuWXx82bNLSwk4gDArdBQmFsUjqOgoABNTS2kpr7HzdAQtGzVpoRUksTDs+ogf0G5/K1+/bxr2Kf/vrOzs3H79u3P/vu2srYuVCeCgoOFOMXVxZBPwgD/NECjo6OxcdOmQiNvqovyrC8fMz9CSiR5m5I3LLfwXaCikhLU1NTx6tVLPHn8CC1bt/63WWSMMVYOKnVP6MuXLzFjxgyMGzcOt2/fxvr167F27VqYmpqiS5cuGDNmDLZu3YoaNWpg+vTpUFBQEOIaGxsjJycH69evR48ePXD9+nX4+voW+g11dXX07t0bs2fPRufOnaH/ydwsb29v6OnpoUmTJpCSksKhQ4egq6v7Td58iEQi9B84CLv8dsLg74bpLj8/yMvLo5OzsxBuiYc7atWqhQmTJgMA+g8YiInjx2H3Ln+0adsOV69cRmhICHy3bhfirFn9E86dPYufVq9BTcWaSEjIm0elpKgEOXl5qKqqQVVVTSI9NWrUgKaGZrHvEq0OhgwejEXu7jAzN4e1lRWOHD2K2NhY9Pn7vZ8bNmzA23fvsGTxYgBAn969cfDgQXh5e+OHXr1w7/59HDt2DMuXLxeOOXDgQIwdNw5+/v5o364dLl2+jOCQEOzY/k95bdy4ES1btoSOjg7S09NxNjAQt27fho9P9XwgUJBIJMIP/fpjX8Cuv2+0DbBvzy7IyckJc9EA4KflS6FVSwujxk4AAPzQtz9mTJ2E/Xv3oGWrNrhx/Spu3wqF94bNQpzQkGCACPp16+LNq1fY6rsRBgZ14dytOwAgIyMDe3f7w6FVa2hqauH9+xQc/+0I3r17h7afvJu3OhKJRBg0aBB27twJg7p1YWBgAL+dOyEvLw/nLl2EcO6LFqHW368hAvLqxLixY+Hv54d27dvj8qVLCAkOxvYdO4Q4g4cMgfuiRTA3M4OVtTWOHjkiURdzcnIwd84cPHr8GN7e3sjNzUX83/NFVVVVISMjU45nonIpy/pi37IV9u7xh7aODuoZGuFpxBMcPnhAqC8AcPniBaipqUFbRwdRkZHYtH4dWrZugxY2kotRMcZYaajOCwmVtkrdCHVxcUFGRgZsbW0hLS2NKVOmYOzYsQDyVrEdPXo02rVrBx0dHSxbtgwLFy4U4jZp0gReXl746aef4ObmhrZt28LT0xMuLoWHs40aNQp79+7FyJEjJfYrKSnhp59+QkREBKSlpWFjY4NTp059s3NNhg5zQWZmJtas+gmpqakwt7CAt896KCoqCmHi4mIhJfXPMGcr68ZYvHQ5tm7ZjG1bfFFHXx9Ll6+AxSfD5I4ePgwAmDRhvMTvLVi4CN2/6wFWtM6dOyMlJQXbt29HfHw8jI2N8fO6ddD7e2hmfHy8xKJRderUwc/r1sHL2xuHDh1CrVq1MGvWLHTs0EEI07hxYyxfvhybN2+Gr68v9PX14blihcSwxoTERCxyd0d8fDyUlJRg0qABfHx8Cq0gWp0NGDQEWZmZWO+9FqlpqWhkZo6Va9ahZs1/6srbt3EQfVJXLCytsGDRYvjt2Ar/HdugV7sOFngsgZn5P0MX09PSsGObL+LfvYOysgpat2uHkaPHCT3Z0lJSeBn9AufOnsb7lBQoq6jAtJEZvH02wdCIF45yGT4cmZmZ+GnlSqSmpsLC0hLrN2yQuIbFxsZC9Mk1uqg6scLTU6JOFFUX1/38s1AX3759iytXrgDIe3j0KV9fXzT/ZH2C6qis6svkaT/Cb8c2+HivQXJSEjS1tND9+54YOtxVCJOYkIAtG9cjKSkRGpqa6OTcBUNc/vmeMcZY5SSiStqkb9++PZo0aYJ169aV+W8FBARg2rRpePPmjcRS76UlIfl9qR+T/TeaaipIfc/lUtkoq6ggOrboFUlZxamrq4X3qakVnQxWgIqyMteXSqiurlbJgRhj3yTPwxdL/ZhufarnSKdK3RNa1tLT0xEVFQVPT0+MGzeuTBqgjDHGGGOMsW9f5ey6+zZ9m+NKS8mqVavQpEkT6OjowM3NraKTwxhjjDHGGGNVXqXtCb106VKZ/4aHhwc8PDzK/HcYY4wxxhhj37ZKOovxm1Ste0IZY4wxxhhjjJWvStsTyhhjjDHGGGOVhZh7QksNN0IZY4wxxhhjrATcBC09PByXMcYYY4wxxli54Z5QxhhjjDHGGCsBD8ctPdwIZYwxxhhjjLES8Oq4pYeH4zLGGGOMMcYYKzfcE8oYY4wxxhhjJeCO0NLDPaGMMcYYY4wxxsoN94QyxhhjjDHGWAl4YaLSw41QxhhjjDHGGCsBL0xUeng4LmOMMcYYY4yxcsM9oYwxxhhjjDFWAu4ILT3cE8oYY4wxxhhjrNxwTyhjjDHGGGOMlYAXJio93AhljDHGGGOMsRLwwkSlh4fjMsYYY4wxxhgrN9wTyhhjjDHGGGMlEHNHaKnhnlDGGGOMMcYYY+WGe0IZY4wxxhhjrAQE7gotLdwIZYwxxhhjjLES8MJEpYcboeVAU02lopPAiqCswuVSGdXV1aroJLAiqCgrV3QSWBG4vjDGGPsWcSO0HKS+f1/RSWAFKKuoIDUpqaKTwQpQVldHbAKXS2Wjq6nO17FKSFlFBe+SUio6GayAWuqqiDz0S0UngxVQv9/Iik4CqwJ4YaLSw41QxhhjjDHGGCsBD8ctPbw6LmOMMcYYY4yxcsONUMYYY4wxxhgrgZio1LeykpSUhGHDhkFVVRWqqqoYNmwYkpOTPxtHJBIVua1evVoI0759+0LfDxw48KvTx8NxGWOMMcYYY6wKGTx4MF69eoUzZ84AAMaOHYthw4bhxIkTxcaJiYmR+Hz69GmMGjUKffr0kdg/ZswYLFmyRPisoKDw1enjRihjjDHGGGOMlaAs5oRmZmYiMzNTYp+cnBzk5OT+9TEfPnyIM2fOICgoCHZ2dgCAbdu2wcHBAY8fP4apqWmR8XR1dSU+Hzt2DI6Ojqhfv77E/po1axYK+7V4OC5jjDHGGGOMlYCo9DdPT09hyGz+5unp+Z/S+eeff0JVVVVogAKAvb09VFVVcePGjS86RlxcHE6ePIlRo0YV+i4gIABaWlqwsLDArFmzkJqa+tVp5J5QxhhjjDHGGKsAbm5umDFjhsS+/9ILCgCxsbHQ1tYutF9bWxuxsbFfdAx/f38oKyujd+/eEvuHDBkCIyMj6OrqIiwsDG5ubrh79y7OnTv3VWnkRihjjDHGGGOMlaAsFhL6mqG3Hh4eWLx48WfDhIaGAshbZKggIipyf1F++eUXDBkyBPLy8hL7x4wZI/y/paUlTExM0KJFC9y+fRvNmjX7omMD3AhljDHGGGOMsUpv8uTJJa5Ea2hoiHv37iEuLq7Qd+/evYOOjk6Jv3P16lU8fvwYBw4cKDFss2bNICMjg4iICG6EMsYYY4wxxlhpKouFib6GlpYWtLS0Sgzn4OCAlJQUhISEwNbWFgAQHByMlJQUtGzZssT4O3bsQPPmzdG4ceMSw4aHhyM7Oxt6enolZ+ATvDARY4wxxhhjjJWgLBYmKgtmZmbo0qULxowZg6CgIAQFBWHMmDH47rvvJFbGbdSoEY4ePSoR9/379zh06BBGjx5d6LjPnj3DkiVLcPPmTTx//hynTp1Cv3790LRpU7Rq1eqr0siNUMYYY4wxxhirQgICAmBlZYXOnTujc+fOsLa2xu7duyXCPH78GCkpKRL79u/fDyLCoEGDCh1TVlYW58+fh7OzM0xNTTF16lR07twZf/zxB6Slpb8qfTwclzHGGGOMMcZKUBYLE5UVDQ0N7Nmz57NhihpePHbsWIwdO7bI8AYGBrh8+XKppI97QhljjDHGGGOMlRvuCWWMMcYYY4yxEhC+nZ7Qyo4boYwxxhhjjDFWAjG3QUsND8dljDHGGGOMMVZuuCeUMcYYY4wxxkpQ0e8JrUq4EcoYY4wxxhhjJeBGaOnh4biMMcYYY4wxxsoN94QyxhhjjDHGWAl4YaLSwz2hjDHGGGOMMcbKTZXqCR0xYgSSk5Px22+/VXRSGGOMMcYYY1UIzwktPVWqEfrzzz/zP45iHDp0CLv37EF8fDzq16+PmTNmoGnTpsWGv3XrFrzXrUNkZCRqaWlhmIsL+vbpIxHm/IUL8PX1xatXr6Cvr4+JEybA0dFR+P727dvYvXs3Hj56hPj4eKxZvRrt27cvqyx+kw79+it2BwQgPiEB9Y2MMPPHH9G0SZNiw9+6fRveP/+MyKiovHIZOhR9e/cWvn8WGQnfrVvx6NEjxMTGYsb06Rg8cKDEMXr06oWY2NhCx+7Xpw/mzp5dann7lhER/HZsx4njx5D6PhXmFuaYPnM2jOrX/2y8yxcvYMe2rXjz+jVq16mD0ePGo2279sL3O7dvg98vOyTiaGho4OjvpwAAOTk52L7FF0F//omYN6+hqKSE5i1sMG7CRGjVqlXq+fzW8HWsciIi/LJ9G44f+w2pqakwN7fAjNmzUb++8WfjXbpwAdu3bsHr169Qp44+xowfj3bt/zn3Rw//it+OHEFMTAwAwKi+EUaMHA2Hli2FMDu2bcX5P87hbVwcasjIwNS0EcaOnwALS8uyyew35Pfg2/j1aggS09JQT1sL47p1hKWhQYnxwl+8wpwde2GoXQsbJ7sK+8/dvg+vI6cKhT/mPhOyMnm3k3vOX0PAxesS36srKWLvvMn/MTeMVQ7czig9lWI4blZWVqkcR1VVFWpqaqVyrKokMDAQa728MNLVFQF79qBpkyaYOm0aYotoiADA69evMW36dDRt0gQBe/bA1dUVa9aswfkLF4Qw9+7dw/z589Gta1fs27sX3bp2xTw3N4SFhQlhMjIyYNKwIeZww6ZIgefOYe26dRg5YgQC/P3zyuXHH4svlzdvMG3GjLxy8feH6/DhWOPlJVEuHz9+hH6dOpg8aRI0NTWLPM6unTtx5uRJYdvo4wMA6NihQ+ln8hu1b89uHNy/D9NnzMSWHb9AQ0MTM6dPRfqHD8XGCbt/H4sXLUTnLl2xw383OnfpCo//LcCD8DCJcEZG9XHkxElh27k7QPju48ePePLkMVxcXbFtpz+WrliJVy+jMX8u1yG+jlVeAbt34cC+fZgxcza2/+IHTU1N/Dh1Sgn15R7cFy6Ac9eu8NsdAOeuXbFowXyEf3Lua2nrYPykSdju54ftfn5o1rwF3ObMQmTkMyGMQd26+HHmbPgH7MOmLVuhp6eHGdOmICkpqUzzXNldvv8QW06dx8D2DtgwcQQs6ulj4a5DeJv8/rPxPnzMxJpfT6JJ/XpFfl9TThYBcydJbPkN0Hz1tLUkvt80ZWSp5YsxVnWUSSO0ffv2mDx5MiZPngw1NTVoamrif//7n/D0wNDQEMuWLcOIESOgqqqKMWPGAABu3LiBtm3bQkFBAQYGBpg6dSo+/P1HzM3NDfb29oV+y9raGu7u7gDyhuP26tVL+C4zMxNTp06FtrY25OXl0bp1a4SGhgrf+/n5FWq0/vbbbxCJRMLnu3fvwtHREcrKylBRUUHz5s1x8+bNUjlP5SVg71707NkTvXr1gpGREWbOnAkdHR38+uuvRYY/fOQIdHV1MXPmTBgZGaFXr174/vvvsWfPHiHMvn37YGdrC1dXVxgaGsLV1RW2NjbYu2+fEKZVq1aYOGECOnDjpkgB+/ahZ48e6NWzZ165/PgjdLS18euRI0WGP3zkCHR1dDDzxx/zyqVnT3zfowf27N0rhLEwN8e0KVPg3KkTZGVkijyOuro6tDQ1he3a9evQ19dH82bNyiSf3xoiwqGDBzBs+Ai0be+I+sbGcFu4CJkfP+KPc4HFxvv14H40t7HBUJfhqGdoiKEuw9G8hQ0OHTggEU66hjQ0NTWFTU1dXfhOSUkJXj+vR4eOTqhbrx4sLC0x9ceZePzoEeKKaWxVF3wdq5yICIcO7IfLiBFo55hXXxYsckfmx48IDDxbbLyD+/ejhY0thg0fgXqGhhg2fASa29jg4IH9QpjWbdrAoWUr1K1bD3Xr1sO4CROhULMmHnzSUO3s3AU2traoU6cO6tc3xpTp0/Hhwwc8expRpvmu7I5eD0Xn5tbo0qIx6mprYXx3J9RSVcbJkL8+G8/n2Bk4NjaDmUGdIr8XiUTQUFaS2AqSlpKS+F5NsWap5ImxykBMpb9VV2XWE+rv748aNWogODgYPj4+8Pb2xvbt24XvV69eDUtLS9y6dQsLFy7E/fv34ezsjN69e+PevXs4cOAArl27hsmT84ZwDBkyBMHBwXj27J8noOHh4bh//z6GDBlSZBrmzJmDw4cPw9/fH7dv30aDBg3g7OyMxMTEL87HkCFDoK+vj9DQUNy6dQvz5s2DTDE395VRdnY2Hj16BHs7O4n99nZ2uHfvXpFx7t+/Xyi8g709Hjx4gJycHADAvfv3YVfgoYC9g0Oxx2SSsrOz8ejx46LL5f79IuPcDwsrXC52dnjw8KFQLv8mHafOnMH3330n8fClOot58waJCQloYfvPuZaVlUXjJk0RVkzZAEB4WBhsbCXLx8bODuEF4rx6+RK9v/8OA/r8gMUL/4c3r19/Nj0fPqRBJBJBSVn5X+SmauDrWOX15s0bJCQkwNbun/MoKyuLJk2bIex+8ecxLOw+bAuUj52dfbFxcnNz8ce5QHzMyICFlVWRYbKzs3Hst9+gpKSEBiYN/0VuqobsnFxEvIlFswZGEvubNTDCg+jirzeBt+4hJjEZQxxbFxsmIysLw1dvxtBVG+G++1c8fRNXKMzrhCQM+WkjRqzxheeBY4hJTP7XeWGMVV1lNifUwMAA3t7eEIlEMDU1xf379+Ht7S30enbo0AGzZs0Swru4uGDw4MGYPn06AMDExAQ+Pj5o164dNm/eDEtLS1hbW2Pv3r1YuHAhACAgIAA2NjZo2LDwH5sPHz5g8+bN8PPzQ9euXQEA27Ztw7lz57Bjxw7M/sKhVdHR0Zg9ezYaNWokpKs4mZmZyMzMlNgnJyf3Rb9TVpKTk5GbmwsNDQ2J/RqamohPSCgyTkJCAjQKDOXU0NBAbm4ukpOToaWlhYSEBGgWOKamhgYSijkmk1RsuWhofL5cigj/abl8rUuXLyMtLQ09unf/6rhVVWJi3vkveK7VNTQ+2xuZmJAA9SLi5B8PAMwsLDB/4SLo162LpMRE7PbbiUnjxsAvYB9UVVULHTMzMxNbN2+CU6fOUFRU/C/Z+qbxdazySkz4XH2J+Wy8IutLgXP/7OlTjB8zCllZWVBQUMCKn1bByEhybvb1a1fhsfB/+PjxIzS1tODts6FaT815n54OsZigriTZA6mmqIiktKKHSL+OT8TOwMtYPWYIpKWL7p/Qr6WBmb27w1CnFtIzM/Hbnzcxa9sebJzkijpaeWVpaqCHWX27o46mBpLTPmDfpRuYuXUPfKeOgkpNhdLNKGMVgOeElp4y6wm1t7eX6FlxcHBAREQEcnNzAQAtWrSQCH/r1i34+flBSUlJ2JydnSEWixEVFQUgr1cyICBv/hQRYd++fcX2gj579gzZ2dlo1aqVsE9GRga2trZ4+PDhF+djxowZGD16NJycnLBy5UqJntiCPD09oaqqKrF5enp+8W+VpYK9XET02Z6vgt8UWeW+8pissK8ulyLC//3Fv/r9YydOoKW9PWpV40Vvzp09gy4dHYUtv5fsa8sGAEQFa06BOPYOLdHOsQOMjRughY0tVq7xAgCcOXWy0LFycnKwZNFCiMVi/Dh7zr/JWpXD17GKF3jmDDo5thM2YRRGwXNGVOJ1qdB5psL76tarh5279mDL9h3o1bsPli9ZjKioSIkwzZq3wM5de7B523bY2dtj0QI3JH3FiKeqquD1iECF6gQA5IrF+OnQCQzt2Br6WhpFhMhjZlAHHZpYoL6eNiwNDTB/QC/U0dTA8aDbQhibhsZobWEKI91aaNrAEEtc+gIA/vir+FEkjH1LxESlvlVXFbY6bsGn+mKxGOPGjcPUqVMLha1bty4AYPDgwZg3bx5u376NjIwMvHz5EgMLrPyZL//m/HM3LVJSUoWeaGRnZ0t89vDwwODBg3Hy5EmcPn0a7u7u2L9/P3744YdCv+nm5oYZM2ZI7JOTk0NWgd7R8qSmpgZpaelCT/aTEhML9QDk09TULDK8tLS08HS5qDCJSUmFnoazohVbLklJX1cuSUl55VJEL1pJYmJiEBIailUrV3513KqkVes2MLOwED5nZ+VdAxISEqD5Se9yclJSoZ6bT2loakr0egJ55aOuXnwcBQUFGBkb49WrlxL7c3Jy4P6/BYiJeQPv9RurdS8owNexyqR1mzYw/6S+ZGXnLSyYmJAgMRojqYTzqKGpWajXMykpsVAdk5GRgb5B3oqujczM8fDBAxw6cABz5rkJYRQUFKBvYAB9AwNYWlphYN8++P3EcQwbPuJf5/NbplKzJqSkREgs0OuZ8iEdakqFryUZmVmIeB2LZzFx2PT7OQB590pEQPdFq7B8+AA0MS68UJGUlAgN6+jiTULxDX55WVkY6mjhdUL1XiiKMVZYmfWEBgUFFfpsYmICaWnpIsM3a9YM4eHhaNCgQaFNVlYWAKCvr4+2bdsiICAAAQEBcHJygo6OTpHHy4937do1YV92djZu3rwJMzMzAECtWrWQmpoqLH4EAHfu3Cl0rIYNG+LHH39EYGAgevfujZ07dxb5m3JyclBRUZHYKno4royMDBo1aoTg4GCJ/cEhIbC2ti4yjpWVFYJDQiT2BQUHw9zcHDVq5D23sLayKnzMoKBij8kkycjIoJGpaaHzHBwSAuti5jtZWVoWXS5mZkK5fI3jv/8OdXV1tP7kdQfVUU1FRejrGwiboZERNDQ1cTP0n3OdnZ2Nu3f+gmUxZQMAFpaWEnEAIDQkuNj5a0DeyuDRz59DU/Ofm/f8Bujrly/h9fP6IofpVjd8Has8aioqCg0+fQMDGBnVh6amJkJD/jmP2dnZuPPXbVhaFX8eLS2tEFqgfEKCgz8bJw8hu4QV9QlUaqvuf4tkakjDpLYu/nr6XGL/7afPYV638IJDNeXksHnKSGyc5Cps3WyaQl9LAxsnuaKRgV6Rv0NEeBb7FupFLE6ULysnB9HvEqChVHwYxr4lVAZbdVVmjdCXL19ixowZePz4Mfbt24f169dj2rRpxYafO3cu/vzzT0yaNAl37txBREQEjh8/jilTpkiEGzJkCPbv349Dhw5h6NChxR5PUVEREyZMwOzZs3HmzBk8ePAAY8aMQXp6OkaNGgUAsLOzQ82aNTF//nw8ffoUe/fuhZ+fn3CMjIwMTJ48GZcuXcKLFy9w/fp1hIaGCo3Yb8WQwYPx27FjOHb8OKKiorDWywuxsbHo8/f78jZs2IBFf68wDAB9evdGTEwMvLy9ERUVhWPHj+PYsWMS53vgwIEIDg6Gn78/nj9/Dj9/fwSHhGDwoEFCmPT0dDx+/BiPHz8GkPeKkcePHxf7SoXqZsigQfjt+HEcO3Eir1zWrUNsXBz6/N3LvmHTJixavFgI36d3b8TExsJr3bq8cjlxAsdOnMDQwYOFMNnZ2Xj85AkeP3mC7JwcvHv3Do+fPMHLl5I9bWKxGCdOnsR33br9qwZsVSYSidCv/wAE7PLHlcuXEPnsGTyXLYWcvDycOnUWwi1fshhbN28SPvftPwA3Q0Kwd/cuvHj+HHt378Kt0FD0GzBACLNpvQ/u/HUbMW/e4EF4GBYtcMOHDx/QpWs3AHkN0EXz3fD40UP8z2MxcsViJCQkICEhodAojeqGr2OVk0gkQr8BA7Hb3w+XL11E5LNnWL50MeTk5dG5s7MQbulid/hu2ih87jdgIEJDgrFnlz9ePH+OPbv8cTM0BP0H/DO6acvmTbh75y/EvHmDZ0+fYsvmTfjr9m10du4CIO9v9JbNmxAWdh+xMTF4/OgRVi5fhndv38KxY8fyOwmV0A+tbHD21l2cvXUP0W/jseXUebxLeY9uNk0AADsDL2PNr78DyOvRNNSpJbGpKdaEbI0aMNSpBfm/OwICLlzDrYhIxCQm41lMHLyPnkZkzFt0t20i/O620xdwLyoasYnJePTyDZbv+w3pmVlwasrvbWVVQ94ogdLdqqsyu/t0cXFBRkYGbG1tIS0tjSlTpmDs2LHFhre2tsbly5exYMECtGnTBkQEY2NjDPjkBg4A+vXrhylTpkBaWlridSxFWblyJcRiMYYNG4bU1FS0aNECZ8+ehfrfr0TQ0NDAnj17MHv2bGzduhVOTk7w8PAQ0pk//MvFxQVxcXHQ0tJC7969sfiThsG3oHPnzkhJScH27dsRHx8PY2Nj/LxuHfT08p5uxsfHS9xQ1alTBz+vWwcvb28cOnQItWrVwqxZsyTeI9m4cWMsX74cmzdvhq+vL/T19eG5YgUsP3lB+IOHDzF+/Hjhs7e3NwDgu+7d4eHhUca5rvw6d+qUVy47diA+IQHG9evjZy+v4suldm387OUFr3XrcOjwYdTS0sKsGTMkyuXdu3cY4uIifN4dEIDdAQFo1rQptm7eLOwPCQ1FbGwsvu/Roxxy+u0ZNHQYMjMz4b1mNdJSU2FmboE13j+j5ifDYt/GxUJK6p/h/pZW1li0eCl2bN2CHdu2onadOvBYugzmFv/UiXdv32KJ+yKkJCdDTU0d5pYW2LxtB3T/LvN3797i+rWrAIBRw4dJpGndho1o2qx5WWa7UuPrWOU1ZJgLMjMz4bV6FVJTU2FuYQHvn9dL1Je42DhIif557m1lbQ2PpcuwbYsvtm/dgjp19LFk2QpYfHLuExMTsNTDAwkJ8VBUUoKxcQOs9f4ZNn+vqislJYUXz5/j9KmTSElOhoqqKszMzLHRdyvq1zcuvxNQCbWzMkNqegb2XryOxNQPMNTRwpJh/aCjnjeyIjE1rcR3hhaU9jETPr+dRWLaByjKy8FYTxurRw+GqX5tIUz8+1T8dPAE3qenQ7VmTTQyqA3vccOE32WMsXwiKoMmePv27dGkSROsW7eutA/9TUp9/3UXelb2lFVUkFrNX2ZeGSmrqyOW5w5VOrqa6nwdq4SUVVTwLimlopPBCqilrorIQ79UdDJYAfX7jazoJLAqoO+avSUH+kq/zhpccqAqqMyG4zLGGGOMMcYYYwXxZDDGGGOMMcYYK0E1nsJZ6sqkEXrp0qWyOCxjjDHGGGOMVYjqvJBQaePhuIwxxhhjjDHGyg0Px2WMMcYYY4yxEoi5J7TUcE8oY4wxxhhjjLFywz2hjDHGGGOMMVYCnhNaergRyhhjjDHGGGMlEHMbtNTwcFzGGGOMMcYYY+WGe0IZY4wxxhhjrAQ8HLf0cE8oY4wxxhhjjLFywz2hjDHGGGOMMVYC7gktPdwIZYwxxhhjjLES8MJEpYeH4zLGGGOMMcYYKzfcE8oYY4wxxhhjJSBwV2hp4UYoY4wxxhhjjJWA54SWHh6OyxhjjDHGGGOs3HBPKGOMMcYYY4yVgBcmKj3cE8oYY4wxxhhjrNxwTyhjjDHGGGOMlYDnhJYe7glljDHGGGOMsRKIiUp9KyvLly9Hy5YtUbNmTaipqX1RHCKCh4cHateuDQUFBbRv3x7h4eESYTIzMzFlyhRoaWlBUVER33//PV69evXV6eNGKGOMMcYYY4xVIVlZWejXrx8mTJjwxXFWrVoFLy8vbNiwAaGhodDV1UWnTp2QmpoqhJk+fTqOHj2K/fv349q1a0hLS8N3332H3Nzcr0ofD8dljDHGGGOMsRJ8S6NxFy9eDADw8/P7ovBEhHXr1mHBggXo3bs3AMDf3x86OjrYu3cvxo0bh5SUFOzYsQO7d++Gk5MTAGDPnj0wMDDAH3/8AWdn5y9OH/eEMsYYY4wxxlgFyMzMxPv37yW2zMzMck9HVFQUYmNj0blzZ2GfnJwc2rVrhxs3bgAAbt26hezsbIkwtWvXhqWlpRDmS3FPaDlQVlGp6CT8Z5mZmfD09ISbmxvk5OQqOjmlQlldvaKT8J9VxXLR1eRyqYz4OlY51VJXregk/GdVsVzq9xtZ0Un4z6piuVQFXC4V6+LiUaV+TA8PD6HXMp+7uzs8PDxK/bc+JzY2FgCgo6MjsV9HRwcvXrwQwsjKykK9wD20jo6OEP9LcU8o+yKZmZlYvHhxhTyZYcXjcqmcuFwqJy6XyonLpXLicqmcuFyqHjc3N6SkpEhsbm5uRYb18PCASCT67Hbz5s3/lB6RSCTxmYgK7SvoS8IUxD2hjDHGGGOMMVYB5OTkvrhXe/LkyRg4cOBnwxgaGv6rdOjq6gLI6+3U09MT9r99+1boHdXV1UVWVhaSkpIkekPfvn2Lli1bftXvcSOUMcYYY4wxxio5LS0taGlplcmxjYyMoKuri3PnzqFp06YA8lbYvXz5Mn766ScAQPPmzSEjI4Nz586hf//+AICYmBiEhYVh1apVX/V73AhljDHGGGOMsSokOjoaiYmJiI6ORm5uLu7cuQMAaNCgAZSUlAAAjRo1gqenJ3744QeIRCJMnz4dK1asgImJCUxMTLBixQrUrFkTgwcPBgCoqqpi1KhRmDlzJjQ1NaGhoYFZs2bByspKWC33S3EjlH0ROTk5uLu78yT4SobLpXLicqmcuFwqJy6XyonLpXLicmFfatGiRfD39xc+5/duXrx4Ee3btwcAPH78GCkpKUKYOXPmICMjAxMnTkRSUhLs7OwQGBgIZWVlIYy3tzdq1KiB/v37IyMjAx07doSfnx+kpaW/Kn0iom/pjTeMMcYYY4wxxr5lvDouY4wxxhhjjLFyw41QxhhjjDHGGGPlhhuhjDHGGGOMMcbKDTdCGWOMMcYYY4yVG26EMsZYBeA14RhjjDFWXXEjlDEmQSwWV3QSqgWRSFTRSWCsWuJrHGOMVTxuhLJSk/+HnXt4vj1EhNzcXACAlJSUxH723xV1HmNiYnD06NEKSA0rbTk5ORWdBPYF8v9GfXqNY6WLiLg+fINyc3P54Qwrd3wlZv9JamoqVq1ahS5dusDNzQ1XrlzhHp5vkEgkgrS0NFJSUrB9+3Zs27YN796947IsJUWdR39/f0yYMKECUsNKw6c3bDVq1AAAREVF4f379wD4AU5llN/4/O2333DixAlkZ2dXcIqqHpFIJNSH8PBwPHv2DADXh8pOWloaUlJSSE5ORlBQEDIyMio6Sawa4EYo+9fi4uLQrVs3HDhwAO3bt8fz588xYMAAXLhwoaKTxorxuafUixcvhr6+vtAI7dq1Kw4dOgSAh699qU97lD915swZ3Lx5U2JfcnIy2rdvz3/sv1H5DZqMjAxs374d2tra6NChAwYOHIhXr17xA5wKVNT16tWrVzh16hQsLCwwdepUTJgwAePHj0dcXFwFpLDqyD/X+f+NjY3FzJkzoauri0GDBuH48eMAePpBZVLU36jff/8drVq1gqGhIVatWoWnT59WQMpYdSMifjzFvlBISAj09fVRu3ZtAMDEiRMLDSls2rQpatasiSNHjkBHR6eiksq+wLt376CmpgYZGRlcunQJ8+bNw7Jly+Dk5AQAWLhwITZt2oTXr19DXl6+glNbuSQnJ+PQoUOwtraGnZ0dsrOzUaNGjWJvtJo2bQpZWVn4+fnBzMwMADB69GgkJibiyJEjEIvFPESwkhKLxRCLxULvTr6srCz07dsXpqamiI+PR/fu3aGpqYkhQ4agU6dOWLlyJfT09Coo1dVPbm4uRCKRRD3Kzs6GjIwMvLy88Msvv8DAwABDhw7FkCFDEBAQgKVLl2LMmDGYOXNmBab825eamgplZWUAwIgRIxAdHY2pU6eiRYsWSE1NRcOGDSEtLV3BqWTFuXfvHlxcXNCpUyeMHTsWRIRatWpBXV29opPGqji+62Gf9fTpUyxduhTXr19Hy5YtsWnTJmFYzdu3b9G+fXs8e/YMAwYMgLa2NpKSktC3b1/hDxKrHPLL7NWrV5g+fTqMjIwwa9YshIWFAQCCgoJgYGAAJycnXLt2DS4uLti4cSO0tbXx+vXrikx6pRQVFYWlS5fi3LlzAAAZGRmIRCJERERg3rx5GDZsGI4ePYrY2FgAwKFDh6Cnp4cJEyYI51NPT0/4nhugFe/OnTtYtmwZAMmhg1JSUkID9M2bN8J+WVlZyMjIYO3atahXrx769u0LR0dHbNy4EaGhofjzzz/LNwPVxM2bNzFgwIBCIwjyhxN++PAB9+7dw5QpU+Dl5QUAcHJyQmZmJl69eoX+/fsDAIYMGQIbGxtcvXoVMTEx5Z6PqiI6Ohq1a9fGpUuXEB0djRs3bmDcuHHo1asXtLW1YWZmxg3QClRUP1NERAQMDAzw5MkTAICvry+UlJSwatUqmJiYoH79+twAZeWC73xYsby8vGBlZYWQkBAcOHAAYrEYly5dQlZWFhISEvDhwwcsWLAAdnZ2kJeXx/79+xEREYEff/wRNWvWrOjkV0tisbjI4bYikQgxMTEYOHAgHj58iBUrVmDMmDFQUVEBkDd3JyIiAvXq1UPv3r1BRDh8+DDu378PY2Pj8s5GpZWbm4vc3Fw0bdoUDg4OCA8PR0xMDDIyMjBr1iy0bt0aYWFhkJaWxrx58zB58mQAQIMGDeDt7Y2PHz9i9uzZAID379/DwsKCh+NWgKJuzB48eIDnz5+DiCR6tJ88eYKBAweiVq1a6NOnDxYsWIDnz58DAEaOHAkVFRUYGRkJ4bt06QIFBQUEBwfj48ePZZ6X6iY+Ph5t2rSBSCSSKMegoCA4ODjAxsYGy5cvx6FDh3D37l0AgLW1NZo1awY1NTUkJiYKcRwdHREbG4vr16+Xez6+BUXVk9jYWIn90dHRMDMzg5qaGrKzs1G/fn34+vpi6tSpWLhwIaZPn47Vq1cjKyurPJNebRUsM5FIhPfv3yMpKUnYJy8vjxo1amDz5s0AAEtLS0RHR2PYsGGYMGEC5s+fj7Fjx+LKlSvlmnZW/XAjlAkSEhKE/3/+/Dl8fX3h4eGBEydOYOnSpdiyZQtu3LiBoKAgaGpqom7dujA1NcWpU6fg7++PDh06QEZGBrdv38auXbsA8FzC8vLpqo/5vTbJyckSYVavXo3ExERs2bIFgwYNQuvWrYUGprm5OV68eIExY8YgNjYWu3fvhqOjI8RiMS5evFiueanMpKWlhaf6rVu3xuvXr3Hnzh1kZmZCXl4eJ0+exO+//w4/Pz9s3LgRR44cEW6EjYyMsHbtWty4cQM+Pj54+fIlPn78CAUFhSLn6LCy82kjM//cDx48GNu3b5f4LiUlBQsWLEBaWhoOHjyI/v3748iRIxg/fjwAoFu3btDW1sbTp0+FhwkKCgpo3bo1bt68KfQ0sNLTpUsXTJ48GfLy8kJZ5eTkYP78+ahXrx6OHj2KQYMGoXbt2ggNDUVwcDAAoH379sjKykJISIhwrI4dO6JmzZrVute64ENLsVgs1ImC0ws8PDzQqlUrBAYGCvvU1dXx4MED1KpVC8bGxvD09ISqqirS09ORk5ODZ8+eYe3atfjpp5+E47PSV1yZvXnzBu3atRMefgKArq4uXFxcEBAQAAAYO3Ys5syZg4yMDKiqquLDhw+4ffs25s2bJzREeeYeKxPEqrWQkBAaMGAAGRoaUocOHWjZsmVERBQREUHS0tL0/PlzIezHjx+pSZMmNGrUKCIiOnXqFDVr1oyGDx9OL1++pMzMTAoNDSUXFxcaPnw4icXiCslTdfby5UsaOnQo6evrk7OzM128eJGys7OJiMjW1pZmzJhBRES5ublERJSVlUVERGFhYWRqaip8T0QkFovp119/pV69etGrV6/KOSeVS/6/5fDwcOrZsye1a9eOxowZQ8bGxrRw4UIiyjv3REQnT54kZ2dnUldXJ5FIRNOmTZM41tGjR6l58+akoaFBP/zwQ7nmg+UJCQmhuXPn0qNHjyT2nz9/nlxdXYXPV69eJZFIRHfv3hX2nT17lpSUlOjUqVNERDR69Ghq164dPXz4UCKemZkZbdmypYxzUrXl5OQI16pP/55cunSJHB0dhTp3/vx5qlOnDp08eVIIc+7cOWrSpAktXryYiIieP39Otra25ObmJvEbkyZNIjs7OwoLCyvr7FQ6kyZNosmTJ1NycnKh75KTk2nfvn10/vx54W/ImzdvaMKECWRsbEzv3r0jIqJnz56Rrq4u/fXXX0QkWU45OTlERNS/f39q3bp1GeemevD396eePXtSQkJCkd/fuHGDzp49K5Tp+/fvyc3NjRo0aCAR7vr161SjRg26evWqxP6MjAwiIgoKCiIjIyM6evRo6WeCsb9xT2g1du7cOUyZMgWysrLw9fVFr169sHr1amzfvh0vX76Eubm58BQ5JycHcnJy6N27N06ePInk5GR07twZnp6eOH36NHr16oUmTZqgXbt2EIvFmDVrFq+GVwaKe5fXwYMHMW/ePKxZswYqKirYtGkTMjMzMXPmTAQFBQEA6tati/DwcAD/PI2WkZEBAFhYWGD27NnYtWsX2rZti8mTJ8PKygrTpk1Dy5YthWG71ZVIJEJmZiYmTpyIrKwsuLu7Q09PDykpKbh06RIiIyOhr6+PDRs2YNasWbC0tMTVq1fh6emJAwcOID4+XjhWr169sGDBAmRkZEBXV5ffqVcB3r59i2PHjiE4OBgHDhxAr169kJKSgqSkJPj5+eHly5cAgPv376N58+bQ0tIS4trZ2aFNmzbYtm0bAGDYsGGIjIwU5lcDeb3kMjIyiI2N5WGI/wL93euSP88zKytL4u9JamoqYmNj8dtvvwHIG8UjFovRqFEjIYydnR2MjY0REhKCtLQ01KtXD5aWlsLUg3wdOnRAixYtoKCgUD6ZqwTye83k5eXx559/IioqCkDeeQ8PD0ffvn1Rp04drF69GqNGjcLw4cORmJgIPT09eHp6Ql1dHVOmTMGLFy8QHx8PbW1tiZ641NRUxMXFITs7G0ePHkVUVBRmzJhRYfmtCvLrhJ6eHmbOnAkNDQ2J3slDhw6hbt266NGjB2bOnAkbGxuEh4dDWVkZnTp1Qnx8vMSbCxo2bAg7Ozts2rQJQN6/icePH0MsFiM6Ohp79+6FiYkJnJ2dyzejrHqp2DYwq0h//PGHxJPjtLQ0atasGXXu3JnOnDlDP/zwA40ePVoizpIlS0gkEtHvv/8u7EtISKCTJ0/SoUOHKDMzs9zSX5WdO3dO4nN+b0C+/CfM+TZs2EAqKipkY2NDMTExREQUHR1NrVq1Eno3vb29ycDAgKKjoyXihoeH0+vXr4ko7ymqj48PDRw4kHx9fYWeUkZ04sQJ0tPTo/Pnzwv7fvnlF6pfvz4dOHCAEhISSEdHh9atWyecN09Pz0L1JV/Pnj1pyJAhRFS4PFnpycnJEXpn8uvR7du3SUdHh6SlpalevXo0a9YsysjIoLi4ODIxMSF3d3ciItqxYwc1bdpUosyJiBYuXEh2dnbCZysrKxoxYgQlJSUJ+/J7iti/t3fvXurQoQN17tyZVqxYQZGRkURE9Pr1a3JxcaHOnTsTUd65lpKSojNnzhDRP71xkydPJj09PQoMDCQiooCAADIwMKAdO3ZUQG4qh5ycHOH6FBkZSXXq1JE4H6tWraJJkyYJPcOhoaESPcpEeSMJunTpQr1796YHDx6QvLw8paSkCN9v3LiRhgwZQvr6+qSlpUULFy6ktLS0csph1ZTfG50vv8eSKK+3M7+XPz09nR49ekTt27enNm3a0N27d+n9+/fUvn17GjRokMQxxo8fT2pqakSUV2cGDRpEDg4OVLNmTWrZsiVdvnxZ+I6xssA9odVYq1at0K1bNwQGBqJt27aoU6cOFBUVceHCBaSnp8PGxganTp0S5gS+f/8eERER0NLSwp49ewDk9ahpaGigW7du6Nu3L2RlZYvtrWNf5tatW+jcuTNu3LgBIO8JaP7qqQcPHkSXLl0wdOhQ+Pr6Cr1o/fr1Q4MGDVCrVi3o6uoCAAwMDNC8eXPcuXNHeIeruro6Jk+ejKtXryI9PR3Hjh2Dl5cXXrx4AQBwcHDAlClTsG/fPowbN07oKWV578WtUaMGHBwchH29evWCmpoarl27hqysLKSlpaF+/fqQkZFBQkIC7t27B2lpaaxfv17iWGKxGIqKisICXrx6ZOnLvwZJS0tDJBIhKytLqEd//vknjIyMYG1tjaNHj2L16tWQl5eHlpYWevbsid27dwPIm/OZlZWF8+fPSxz73LlzaNq0qbDwUL9+/VC7dm2J17h82nvKipabm1vkXLPXr19jwIABWLFiBWxtbdGlSxccOnRImNdWu3ZttGjRAi9evMC9e/egpaUFBwcHbNu2DYmJiRCJRBCLxYiNjUV2drawinWPHj3g5uaG7777TuL3iKja/M2SlpaGjIwMIiIiICcnh3r16uHGjRvCys9t2rTBwoULYWFhgZCQEGzYsAF3794V6gQA2NjYYNOmTbh06RIWL14MNTU1iVXUHR0d0bZtW/j7++Pdu3dYsmQJFBUVyz2vVcmn15YdO3bAyclJmHN+/PhxxMXFoU+fPlBQUICpqSnWrFkjfKesrIzvv/8ep06dEnqsU1JSEB4ejpSUFAQEBEAkEmHw4MEYM2YMoqKicP36dbRt2xYAv+OVlR1uhFZj8vLyiIyMxOLFi2Fra4vQ0FBcuXIFpqamOHv2LIYOHQpnZ2f07dsX7dq1g4WFBTQ0NLBixQqcPXsWgOSrJT696eNXTvx7JiYmaNmyJfz9/YV9z58/R9euXeHh4YFGjRqhYcOGmDNnDtauXYsPHz5AW1sbzZs3R3Z2Nh48eCDE69ixI1JTU3Hu3Dno6elh69atePv2LVxdXWFqagpXV1doa2vD3Ny8IrL6TWnRogVev34t3KyJxWKoq6vDwMAAV65cQUJCAvr374+RI0eie/fuaN68OQwMDHDnzh14e3tLHOvq1as4d+4cevXqVQE5qZoKrgwtJSWFtLQ0eHl5wcHBAWPHjhUWTJs4cSI2btwIkUiEq1evSsTp3bs3nj9/jtDQUOjq6qJPnz44ePAgpk6diocPH8LX1xepqan4/vvvhffnLly4EMuXL4eSklL5ZvoblP/eVeCfBwRxcXESq0RramrCyMgIFy5cgKenJ3788Ue4uLjg7NmzOH36NIC8d+9qamri4MGDAPLK4K+//sKgQYNw6dIlLFu2DHJycujSpQsiIyORkJAAZWVlTJgwAdra2hJpKvh+0arg0wWGPvXo0SO0adMGVlZWcHd3R0REBEJDQxEZGQkAsLe3h5KSEvr374/+/fsjNzcXGzduRFRUlDCcMzs7G0ZGRti0aRPu3bsHkUgk0UgyMzPD2LFj0aFDBwB503mKetjAChOLxUWeq4CAAHz//fdITU0VViLO7yCQkZFBYmKixHD0pk2bok6dOggPD0d6ejr69+8PJSUldOnSBb/++isWL14MOzs7jB8/Xlgx+rvvvhPuCYr798NYqarQflhW4WbNmkUWFhbCEM0nT55Q3bp1ydTUlB49ekSZmZl09uxZ+vHHH+nYsWNElDck19jYmN68eVORSa8yCg61JcobOps/TIaIKDY2lhYuXEhv374V9k2dOpWaNm1KZ8+eJSKiX3/9lZo1a0b+/v5CmOTkZOrRowe5uroKQ6VzcnLo/PnzdOnSpbLKUpWUlpZGjRo1kli86ePHj9SxY0fS09OjTZs2UUZGBm3ZsoVGjhwp1JeCHj58SO3atSNXV1eJ4Zvs3yk4TI2IKD09nW7evElNmzYle3t7Wr58OU2ZMoV0dHQkFgvq2rUrDRs2jOLj44V9ycnJ1KJFCxo7dqxwLF9fX7KxsSFdXV3S19en9evX81D1/yg3N5cOHDhADRs2JF1dXerevTvdvn1b+D41NZVSU1PJw8OD6tevT9ra2qSjo0Pdu3cnorxpIBMnTqRWrVoJcQIDA8nZ2Zl0dXXJysqKoqKiaO7cudSyZUt6//69EK6qDi8Ui8VF/j359N+qu7s7WVlZ0Zs3b+jNmze0YcMGEolE5OXlJcRdunQp2dnZUWhoKBHlLepUq1YtGj9+PBH9U+dycnJo//79VLNmzSKn4hSVFlZYUf8eP378SEQknNe1a9dS3bp1iShvgahevXoJ0zmePXtGsrKydOXKFSL6Z3rH5MmThSHrRHlTsPr27Uu1atWinj17FrkYl1gsrrL1g1U+3Ait5nbs2EEqKip0+fJlSk1NpVWrVtHcuXNJJBLR8ePHC12MHj16RC1atBDmTPHF6t8Ri8WfnQf44MEDUlBQoN9++42I8uZ/pKenU0pKCrm7u5OJiQkpKyuTkZERTZ8+nYjybtratm1LU6ZMEf6AERFNmzaN7Ozs6MGDB2WbqWpg586dpK6uTtOnT6fIyEjy9fWlwYMHU8uWLemnn34qskwL/lHn+Z//XVE3t+/fv6exY8eSiYkJXblyhR4+fCis9p2vR48e1KZNGwoPDyciopUrV5KDg0OhOZ9eXl6kqqoqse/du3f07Nmz0s1IFVfUAwIior59+9L8+fNp4MCB5OPjQ4GBgWRvb082NjZ04sQJIsprOM2bN49at25Nu3btooSEBPL09CQVFRVh/qGfnx8ZGhoKc0GJ8q6VqampwmdnZ2eJG/Hq4uHDh9SrVy+ytramiRMnUlRUFOXk5FDDhg1p+fLlEmE7d+5M3bp1E1bD7927Nzk7Owvf7969m2rWrEkikUhiLiJR3ir55ubm9OTJk7LPVBVS3D1AXFwcTZw4kQYMGCCEIyLav38/NWzYkD58+EBEeQ8THBwchAc3jo6O1LVrV2HV3A8fPhQ5DzQtLa3ENSYYKy/cCGVkZ2dH9evXJ0VFRTI3N6fXr19LLNn++PFjcnd3pzZt2pCioiL179+fb8ZK0eHDh2nhwoV048YN4Wl9amoqfffdd9StWzchnFgsplGjRpGjoyPt2LGDUlNTqV+/ftS6dWthYaE5c+ZQy5Yt6caNG0K8V69eFbucO/t669ato8aNG5O6ujrVqVOHLl++XGjRjZIeMrDSExUVRSYmJuTl5UWurq504cIFSktLo/T0dMrJyaHIyEgaPXo06erqkqamJjVs2JDWrl1LRHmLctnb29OsWbOIKK93LSkpie7du0eKiorCayfYlyvqweTr168lGqSjRo0ikUgk8TqcuLg46tGjh/DaoqioKJKVlaUDBw4IYSZMmEAikYi2bt1KRETBwcHUt29fOnLkiBAmPj6ezp07R8ePH6fvv/+eGjZsSDdv3iz1fFZGYrGYduzYQatXr6YhQ4aQq6srbd68merXry80WGxsbIRGaH6DZv/+/aSmpiYsiOfj40MikYh8fHxo2bJl1LFjRzp8+DCNHz9eWPgu34wZM6hLly6Um5vLD6VLUNz5OX36NJ06dUp4YHns2DGSlpamPXv2UHp6OhGRUA75o9YuXLhALVu2pBUrVhBR3itVDA0NqWnTprR582bq2bMnmZqaFvvqoU9ff8RYReFGKKOkpCQ6e/Ys3blzR2J//k10VlYW+fr60qpVqwqtrMoKE4vFtHXrVvLy8iKioi/2+cM29fX1ydjYmJycnMjY2JhcXFyIKK+nZ8+ePSQrKys89b9y5Qppa2sLvaNERH369CEdHR3avXs3EeW9w7B9+/bCsBxWNl68eFHoyT8PYyodz58/pxkzZtDx48eJiIoc5rdr1y46ePAgZWdnU05ODtWvX59EIpHQGMkvh7dv31LXrl2pb9++dPbsWYqPjycHBwfq27evcH2bO3cumZqako2NDYlEImGlUH6I8HUKXuPEYjFt3LiRatWqRebm5jR16lShh/LBgwckEonI29tbIo6Pjw81a9aMHjx4QBEREWRmZkZr1qwhorxVWgcMGEDNmzcnGxsb4TcKSkxMFFZmHTVqVLV7/+eUKVNIRUWFevfuLfRahoWFUceOHalbt27k4eFBTZo0IaJ/yiw6Oppq1KhBixcvpo8fP1JOTg7NnTuXmjdvTo0bN6bdu3cXOtcfP36kRYsWkZSUFK1bt658M/mNKepvQ05ODi1dupRUVFTIzMyMzM3NydnZWRgCPWfOHLK1tRWmD2zatIkMDQ2F+GlpaTR48GCJcg4NDRVGPo0YMUIY8cFYZcWNUFYI33z9d/lzMfKHNxHl3RAnJiYSUd6cjjlz5tCePXuE7//8808SiUTC8Jro6GjS0tKijRs3EhHRvn37yNbWlgICAoiI6OLFi9ShQweytLSkESNGlFfWWAFcX0pXZGQkmZiY0Pz584sN065dO2rVqpUwAsDd3Z20tLQoKipKIpyfnx/p6OhI3Iw1btyYTE1NhQc1KSkpdOTIEVq3bh29ePGi9DNUjSQmJpK/vz8FBwdTbGwsDR48mA4fPky7du0iWVlZWrt2rTBVwNDQkCZOnCgxdDYwMJAsLCzo2rVrlJaWRrNnzyYNDQ2ysrIidXV1Wrx4Mb1586ZQnSv4uWBvXXXy9OlTUlVVpdmzZwv7cnJyaPfu3SQrKyv0sl28eFFohHp4eJCMjAzZ2dnR3bt3iSjv4XNRr1X59FyHhYVJTP1gnxcREUE+Pj6UkJBAwcHB1Lp1azp8+DARkVBfWrRoQUR5Pfru7u6kpqZGkZGR9Ouvv5KJiYnEKDVvb29q1KiRxCvAihsCz1hlVKPkpYtYdUFEEIlE/LqIUjBp0iQMHToUf/75J16+fInRo0fj/fv3MDMzw6JFi9CuXTsMHjwYjRs3xqtXr+Dr64tDhw4ByFt+fcOGDdDT00OPHj2wfft2TJw4Ea1btxZeU+Dj44Nnz55h5cqVsLGx4dVtKxDXl9KR/7oOIyMjtGvXDvfu3cOLFy9w7do17Nu3D/7+/tDU1AQAuLm5wcXFBY8fP0bt2rXRt29fLFmyBC9evIChoaFwzJiYGJiamuLdu3cAAD8/P6ioqEAsFuPKlSto06YNVFRU8MMPP1RElr85+X8jCrp16xbev3+Pfv36QV1dHYmJiTA3N0e/fv3Qu3dvIczp06fRqlUr2NnZYcSIEdiyZQtcXV3RokULAEB6ejoePXoEExMTKCoqYtWqVbC3t0dcXBx69eoFPT29ItNSsA7mv6aqOjIyMoKFhQUSExORkZEBBQUFSEtLw9zcHNra2pCXl8fEiRMxaNAg9OjRAwoKCnj16hU2b96M2NhYof7IyMhARkZGWK01/xx/eq4tLCwqIouVVsFzRUT4+PEj7t69i4iICCxbtgzNmjVDr169cPDgQdSrVw+9e/fGkydP4O/vjzNnzkBDQwOvXr2Cvr4+PDw8cPbsWSxbtgzZ2dlo0qQJkpOToaqqCiDvdTrR0dHQ19cX0pC/SnFubm6VXPWZVTEV2ABmrMrKzc2lNm3a0OTJk6lfv360evVqun79OnXs2JHMzc2FlVNPnDghDMMJCAiglStXkoaGBsXFxRFRXs+AjIwMPX36lIjyem14aDSr6rZv30729vZ05swZunLlColEIgoJCZEIo6+vTx4eHsJQtMaNG9OkSZOI6J9hmjdv3iRHR0cyNDSkRo0akbGxMR0/fpwXUflKOTk5xQ41X7t2LWloaJCzszP98ccfREQ0ffp0UlBQoH379gnhLl68SE2bNiUfHx8iyuv5EYlEZGdnR6dOnaLg4GBydHSkYcOGCfPgCuJ5h19m48aN1KpVK2FoJ1He3xp9fX26e/cuvX//nvz9/cnR0ZGcnZ0pMDCwAlP7bStuReL8aQTbt28naWlpcnBwEKYLZGRkUJ8+fcjJyYmaNm1KKioq5OzsTPv27RNWTM/v0bx27Rq5uLiQSCSiRo0aERGvOsyqDm6EMlZGfH19SVFRkfr27Svsi4uLowEDBpCDgwMRETVv3pymTp0qzPs8cOCAxNy2uLg4UlVVFRYfYKyqOnXqFJmampKlpSX169eP9PT0aMqUKUREpKurS4sXL5YYajZx4kRq0aIFRUZGElFeY6h27drCkPd8CQkJ5O3tTTt27OChg/9RZGQkXbx4UWKYZmBgIFlbW5OTk5OwLyIighwdHYXyy+fs7EwjRowQXonTsWNHUlRUpDFjxpCBgQF17dqVHj58WOh3ufH5dWJiYqh27drk7OxMt2/fpoSEBJowYQK1bNlSog4UbMzwef5vPl2ReMKECRQdHU2ZmZnUoEEDMjU1lRh6Pm7cOFJTUyM3NzeJhQNzcnLo3r17EseNiIggQ0ND+vHHH4tsgHKjlH2ruJ+esTLyww8/QFFRUWJomJaWFlxcXBAcHIxXr17hxYsXaNWqFVRUVEBEOHPmDADAy8sLAKCtrY2bN2/Czc2tQvLAWHlIT0+Hu7s72rZti8DAQPTu3RsaGho4d+4cUlJSMGLECBw8eFAYVgsAo0aNwu3btxEWFgYAGDhwIGJiYhAYGCiEISJoaGhg+vTpGDlyJOTk5Mo9b98SsViMnJwciX05OTnYt28fGjdujObNm2PKlCno1asXzp49CwCwtraGmZmZ8MJ7AGjQoAGsrKwQFhaG6OhoYX+HDh3w9OlThISEAAAGDBgAFRUVjBs3DtHR0Th16hQaNWpUKF1SUlJFDgNmRdPV1UWzZs1w+/ZtLF68GLa2tvj9998xf/58iTogJSWF3Nxc5ObmCp/5PH8dIsIvv/yCNWvWYNmyZVBXV8eECRNw9uxZ9OvXD/Hx8ejZs6cw7Dnfd999h1q1aqF27drQ0NAQ9p86dQorVqzAhw8fAOTVyQYNGiAqKgpeXl5FDq/lIbfsW8X/chkrI9ra2rCxsUFycrJwgyYlJQUDAwM0aNAAN27cQM+ePTF58mTMmjULzs7OICJcunQJ48aNE47ToEGDisoCY+Xi9OnTiI2NxfDhw6Gnp4eBAwdi7dq1yMzMxMmTJzF+/Hg8ePBAaHACQGRkJKSlpXH58mUkJSWhdu3a+OGHH5Ceni6E4RvqryMlJSXMKbt+/TpevnyJ58+f45dffsHw4cPx5MkTnDhxAg0bNsSiRYsAADo6OmjTpg0+fPiAO3fuCMdq1aoVMjIycOnSJWFfp06dEBsbi5cvXwIARo4cicTERCEeERVqBLN/x9XVFTY2Nujfvz+uXbuG6OhodO/evVA4aWlpntf+H4hEIty5cwdLly5FRkYGNm3ahPHjx+PEiROQlZWFh4cHXF1d8fbtWzx58kSI1717d/Tv3x/z5s2Di4sLfvrpJ7Rv3x6TJ0+GlZUVxGIxAMkGZk5ODoio3PPIWFnhRihjZWjUqFEICwsTnvwDwLNnz5CcnAxzc3MsW7YMkyZNQlBQEJo1a4aVK1eibdu2GDp0aAWmmrHyRURIS0tDkyZNhH1OTk7Q1dXF6dOnUa9ePbRt2xarVq3C9evXER8fj5CQENjY2CAwMBCpqakAgMOHD8PV1bWCcvHtyr+xjY6OhqurK5SVlTFt2jQcPXoUxsbGmDBhAmbMmAEtLS2EhYXh1q1bCA0NxdGjRwEATZs2hZaWFg4fPiwcs3Xr1tDQ0BDC5Ie7ePEixo4dKyzg0q5dO/j6+iIxMREikUhoBLP/xsnJCTExMYiNjYWuri438MvQtGnTIBKJYGxsDHl5eQBAo0aNMHr0aOzZswdmZmbQ1NTEtWvXhIdkIpEIy5Ytw7Zt26ClpYWTJ0+idevWCAoKwvz586GsrFzod2rUqMEP1liVIiJ+rMJYmUlNTYW1tTWUlJSwcOFCNGjQAEuXLsXHjx9x7NgxyMrKIjc3l59Es2otMjISjRo1wtWrV2FnZyfUiREjRuDixYs4f/48EhISMG/ePDx58gQJCQkYNWoUli1bBkVFRcjKylZ0FqqEcePG4cWLF5g7dy4sLS2RnJwMExMTAMCxY8cwd+5ciMVidOrUCffv34eCggLOnj2LpKQkeHh44Pbt27h69apwPHd3d4hEIsyePRuKiooSv5WdnQ0ZGRlcvHgRp06dwtKlS4UbeFY6XF1dkZCQgM2bN6NOnToVnZwqSywWo02bNjAzM8P69euhoKAAAPjrr7/QpUsXBAYG4vfff8fp06exefNmoaczv5fz0//P/ywSibjByao87gllrAwpKyujU6dOiIuLw6FDhzB06FCkpKTA09MTsrKyEsu5M1ZdGRgYwN7eHmvWrEFmZiakpaWRlJSEZ8+eIT09Hbt374adnR2OHTuGzZs34+nTp9i4cSPU1dW5AVpKLl26hKNHj2L48OFwdHSElpaW0AB9/vw5li1bhqFDhyIkJAQbN25E48aNERQUhOTkZKirq8Pa2hrh4eESc3IXL14MDw+PQg1QIO8VIADg6OiI1atXcwO0DLi6ukq81oaVDSkpKQwZMgSPHj1CeHi4sP/169fCq266du2KoKAg3L9/X4jzaXzgn9dU8dxcVl1wTyhjZezy5cs4fvw4Ro8ejQYNGgg3X4yxf1y+fBn9+vVDixYtMHnyZJw/fx5ZWVlITEyEs7MzBg0axHWnDPn7+8PNzQ0PHz4U3kOYf3tw/fp1ODk5ISwsDA0aNEBSUhJ++OEHXLlyBcuXL4ebmxsePXqEsLAwODs7Swwl5F4dVh3ExsaiefPmsLKygqenJ+rVq4f//e9/uHv3Ls6fPw95eXmcOXMGnTp14gfPjP2NG6GMMcYqhVOnTmHr1q0ICQmBiYkJfH19YWZmVtHJqhZCQ0Nhb2+Px48fF1oM7dq1axg/fjxatGiBsWPH4sCBA8jKyoKGhgZUVVUxZ86cCko1Y5VHjx49EBwcjJYtWyIsLAxZWVnYvHkzunfvXmjILWOMG6GMMcYqkbS0NMjKyvIw23KWlpYGW1tbODk5wcfHR9j//Plz3Lp1C1JSUliyZAmeP3+ONm3aYNmyZbC2ti50HCLiXk9WLR05cgQ7duzAkCFD0KFDB4nXszHGCuNl6BhjjFUaSkpKFZ2EaklJSQn/+9//MG3aNLx79w6TJk3C06dPsXfvXtjZ2WHp0qWwtbWFtra2xLDo3NxciTls3ABl1ZWTkxOWLVsmsSJxbm4ur/jMWDG4J5QxxhhjAIADBw5g+/btePnyJTIyMuDq6orp06dDTU1NCJObmwuRSMTDCxkrgFckZuzLcSOUMcYYY4LU1FSkpKRAX1+/opPC2DflypUrCAgIwKJFi7gRylgJuBHKGGOMsSLl5ORAWlqah9kyxhgrVdwIZYwxxhhjjDFWbnhCB2OMMcYYY4yxcsONUMYYY4wxxhhj5YYboYwxxhhjjDHGyg03QhljjDHGGGOMlRtuhDLGGGOMMcYYKzfcCGWMMcYYY4wxVm64EcoYY4wxxhhjrNxwI5QxxhhjjDHGWLnhRihjjDHGGGOMsXLDjVDGGGOMMcYYY+Xm/+R/oIYJss9LAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df[num_features].corr()\n",
    "\n",
    "# plot as a heatmap\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# define the mask to set the values in the upper triangle to True\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "sns.heatmap(\n",
    "    corr, annot=True, cmap=cmap, fmt=\".3f\", mask=mask, vmin=-1, vmax=1, linewidths=0.5\n",
    ")\n",
    "plt.xticks(rotation=20)\n",
    "plt.tight_layout()\n",
    "plt.title(\"Heatmap showing correlation between numerical features\", fontsize=20)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr2 = df[num_features].corr().unstack().sort_values(ascending=True).drop_duplicates()\n",
    "# drop age vs age correlation\n",
    "corr2 = corr2.drop(corr2.index[-1])\n",
    "\n",
    "# Normalize the correlation values\n",
    "max_abs_corr = max(abs(corr2.min()), abs(corr2.max()))\n",
    "norm = plt.Normalize(vmin=-max_abs_corr, vmax=max_abs_corr)\n",
    "\n",
    "# Choose a colormap, e.g., coolwarm\n",
    "cmap = plt.get_cmap(\"coolwarm\")\n",
    "# Generate colors based on the normalized correlation values\n",
    "colors = cmap(norm(corr2.values))\n",
    "# Plot the bar chart with the colormap\n",
    "corr2.plot(kind=\"barh\", color=colors)\n",
    "plt.title(\"Correlation of feature pairs\", loc=\"left\", fontsize=20)\n",
    "plt.xlabel(\"Correlation\")\n",
    "plt.ylabel(\"Feature pairs\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the two features that have the biggest correlation in this dataset?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The two features that have the biggest correlation\n",
      "in this dataset are:\n",
      "\n",
      "pdays and previous\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"\"\"The two features that have the biggest correlation\n",
    "in this dataset are:\n",
    "\n",
    "{corr2.index[-1][1]} and {corr2.index[-1][0]}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target encoding\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now we want to encode the `y` variable.\n",
    "- Let's replace the values `yes`/`no` with `1`/`0`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "no     39922\n",
       "yes     5289\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"y\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"y\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "0    39922\n",
       "1     5289\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"future.no_silent_downcasting\", True)\n",
    "\n",
    "# replace the target column with 0 and 1\n",
    "df[\"y\"] = df[\"y\"].replace({\"no\": 0, \"yes\": 1}).astype(int)\n",
    "df[\"y\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"y\"][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Split your data in train/val/test sets with 60%/20%/20% distribution.\n",
    "- Use Scikit-Learn for that (the `train_test_split` function) and set the seed to `42`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the training set:\t 27126\n",
      "Size of the validation set:\t 9042\n",
      "Size of the test set:\t\t 9043\n"
     ]
    }
   ],
   "source": [
    "SEED = 42\n",
    "\n",
    "df_full_train, df_test = train_test_split(df, test_size=0.2, random_state=SEED)\n",
    "df_train, df_val = train_test_split(df_full_train, test_size=0.25, random_state=SEED)\n",
    "\n",
    "assert len(df) == len(df_train) + len(df_val) + len(df_test)\n",
    "\n",
    "print(f\"Size of the training set:\\t {len(df_train)}\")\n",
    "print(f\"Size of the validation set:\\t {len(df_val)}\")\n",
    "print(f\"Size of the test set:\\t\\t {len(df_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.reset_index(drop=True)\n",
    "df_val = df_val.reset_index(drop=True)\n",
    "df_test = df_test.reset_index(drop=True)\n",
    "\n",
    "y_train = df_train[\"y\"].values\n",
    "y_val = df_val[\"y\"].values\n",
    "y_test = df_test[\"y\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_features = [\n",
    "    \"job\",\n",
    "    \"marital\",\n",
    "    \"education\",\n",
    "    \"housing\",\n",
    "    \"contact\",\n",
    "    \"month\",\n",
    "    \"poutcome\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "0    0.883931\n",
       "1    0.116069\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full_train.y.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In case of binary classification\n",
    "global_subscription_rate = df_full_train.y.mean()\n",
    "round(global_subscription_rate, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>0.278976</td>\n",
       "      <td>742</td>\n",
       "      <td>0.162906</td>\n",
       "      <td>2.403524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>0.222590</td>\n",
       "      <td>1815</td>\n",
       "      <td>0.106520</td>\n",
       "      <td>1.917727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>0.160313</td>\n",
       "      <td>1023</td>\n",
       "      <td>0.044243</td>\n",
       "      <td>1.381180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>0.135467</td>\n",
       "      <td>7500</td>\n",
       "      <td>0.019397</td>\n",
       "      <td>1.167117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self-employed</th>\n",
       "      <td>0.122862</td>\n",
       "      <td>1286</td>\n",
       "      <td>0.006792</td>\n",
       "      <td>1.058518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>admin.</th>\n",
       "      <td>0.119894</td>\n",
       "      <td>4137</td>\n",
       "      <td>0.003824</td>\n",
       "      <td>1.032947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>0.111588</td>\n",
       "      <td>233</td>\n",
       "      <td>-0.004481</td>\n",
       "      <td>0.961390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>0.108760</td>\n",
       "      <td>6096</td>\n",
       "      <td>-0.007310</td>\n",
       "      <td>0.937024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>0.093812</td>\n",
       "      <td>1002</td>\n",
       "      <td>-0.022257</td>\n",
       "      <td>0.808243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>0.090746</td>\n",
       "      <td>3350</td>\n",
       "      <td>-0.025323</td>\n",
       "      <td>0.781827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>0.086587</td>\n",
       "      <td>1178</td>\n",
       "      <td>-0.029482</td>\n",
       "      <td>0.745997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>0.072252</td>\n",
       "      <td>7806</td>\n",
       "      <td>-0.043817</td>\n",
       "      <td>0.622490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean  count      diff      risk\n",
       "job                                               \n",
       "student        0.278976    742  0.162906  2.403524\n",
       "retired        0.222590   1815  0.106520  1.917727\n",
       "unemployed     0.160313   1023  0.044243  1.381180\n",
       "management     0.135467   7500  0.019397  1.167117\n",
       "self-employed  0.122862   1286  0.006792  1.058518\n",
       "admin.         0.119894   4137  0.003824  1.032947\n",
       "unknown        0.111588    233 -0.004481  0.961390\n",
       "technician     0.108760   6096 -0.007310  0.937024\n",
       "housemaid      0.093812   1002 -0.022257  0.808243\n",
       "services       0.090746   3350 -0.025323  0.781827\n",
       "entrepreneur   0.086587   1178 -0.029482  0.745997\n",
       "blue-collar    0.072252   7806 -0.043817  0.622490"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "marital\n"
     ]
    },
    {
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       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>single</th>\n",
       "      <td>0.147637</td>\n",
       "      <td>10221</td>\n",
       "      <td>0.031568</td>\n",
       "      <td>1.271973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>0.119720</td>\n",
       "      <td>4143</td>\n",
       "      <td>0.003651</td>\n",
       "      <td>1.031451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>0.100578</td>\n",
       "      <td>21804</td>\n",
       "      <td>-0.015492</td>\n",
       "      <td>0.866532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean  count      diff      risk\n",
       "marital                                      \n",
       "single    0.147637  10221  0.031568  1.271973\n",
       "divorced  0.119720   4143  0.003651  1.031451\n",
       "married   0.100578  21804 -0.015492  0.866532"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "education\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tertiary</th>\n",
       "      <td>0.148970</td>\n",
       "      <td>10539</td>\n",
       "      <td>0.032901</td>\n",
       "      <td>1.283460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>0.127444</td>\n",
       "      <td>1483</td>\n",
       "      <td>0.011375</td>\n",
       "      <td>1.098001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary</th>\n",
       "      <td>0.105303</td>\n",
       "      <td>18670</td>\n",
       "      <td>-0.010767</td>\n",
       "      <td>0.907238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary</th>\n",
       "      <td>0.086377</td>\n",
       "      <td>5476</td>\n",
       "      <td>-0.029693</td>\n",
       "      <td>0.744183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean  count      diff      risk\n",
       "education                                     \n",
       "tertiary   0.148970  10539  0.032901  1.283460\n",
       "unknown    0.127444   1483  0.011375  1.098001\n",
       "secondary  0.105303  18670 -0.010767  0.907238\n",
       "primary    0.086377   5476 -0.029693  0.744183"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "housing\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>count</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>0.166407</td>\n",
       "      <td>16033</td>\n",
       "      <td>0.050337</td>\n",
       "      <td>1.433683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>0.075987</td>\n",
       "      <td>20135</td>\n",
       "      <td>-0.040082</td>\n",
       "      <td>0.654669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean  count      diff      risk\n",
       "housing                                     \n",
       "no       0.166407  16033  0.050337  1.433683\n",
       "yes      0.075987  20135 -0.040082  0.654669"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "contact\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cellular</th>\n",
       "      <td>0.148270</td>\n",
       "      <td>23464</td>\n",
       "      <td>0.032200</td>\n",
       "      <td>1.277422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>telephone</th>\n",
       "      <td>0.137128</td>\n",
       "      <td>2319</td>\n",
       "      <td>0.021059</td>\n",
       "      <td>1.181431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>0.038613</td>\n",
       "      <td>10385</td>\n",
       "      <td>-0.077456</td>\n",
       "      <td>0.332675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean  count      diff      risk\n",
       "contact                                       \n",
       "cellular   0.148270  23464  0.032200  1.277422\n",
       "telephone  0.137128   2319  0.021059  1.181431\n",
       "unknown    0.038613  10385 -0.077456  0.332675"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "month\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>0.516043</td>\n",
       "      <td>374</td>\n",
       "      <td>0.399973</td>\n",
       "      <td>4.445983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>0.465517</td>\n",
       "      <td>464</td>\n",
       "      <td>0.349448</td>\n",
       "      <td>4.010678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dec</th>\n",
       "      <td>0.464481</td>\n",
       "      <td>183</td>\n",
       "      <td>0.348411</td>\n",
       "      <td>4.001749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>0.437396</td>\n",
       "      <td>599</td>\n",
       "      <td>0.321326</td>\n",
       "      <td>3.768396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apr</th>\n",
       "      <td>0.202645</td>\n",
       "      <td>2344</td>\n",
       "      <td>0.086576</td>\n",
       "      <td>1.745895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feb</th>\n",
       "      <td>0.156723</td>\n",
       "      <td>2112</td>\n",
       "      <td>0.040654</td>\n",
       "      <td>1.350256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aug</th>\n",
       "      <td>0.110954</td>\n",
       "      <td>4966</td>\n",
       "      <td>-0.005115</td>\n",
       "      <td>0.955932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nov</th>\n",
       "      <td>0.101481</td>\n",
       "      <td>3173</td>\n",
       "      <td>-0.014588</td>\n",
       "      <td>0.874315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jun</th>\n",
       "      <td>0.098888</td>\n",
       "      <td>4318</td>\n",
       "      <td>-0.017181</td>\n",
       "      <td>0.851976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jan</th>\n",
       "      <td>0.098678</td>\n",
       "      <td>1135</td>\n",
       "      <td>-0.017391</td>\n",
       "      <td>0.850167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jul</th>\n",
       "      <td>0.091927</td>\n",
       "      <td>5537</td>\n",
       "      <td>-0.024142</td>\n",
       "      <td>0.792000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>may</th>\n",
       "      <td>0.065219</td>\n",
       "      <td>10963</td>\n",
       "      <td>-0.050850</td>\n",
       "      <td>0.561900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean  count      diff      risk\n",
       "month                                     \n",
       "mar    0.516043    374  0.399973  4.445983\n",
       "sep    0.465517    464  0.349448  4.010678\n",
       "dec    0.464481    183  0.348411  4.001749\n",
       "oct    0.437396    599  0.321326  3.768396\n",
       "apr    0.202645   2344  0.086576  1.745895\n",
       "feb    0.156723   2112  0.040654  1.350256\n",
       "aug    0.110954   4966 -0.005115  0.955932\n",
       "nov    0.101481   3173 -0.014588  0.874315\n",
       "jun    0.098888   4318 -0.017181  0.851976\n",
       "jan    0.098678   1135 -0.017391  0.850167\n",
       "jul    0.091927   5537 -0.024142  0.792000\n",
       "may    0.065219  10963 -0.050850  0.561900"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "poutcome\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",
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       "    }\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>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>diff</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>success</th>\n",
       "      <td>0.641322</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.525253</td>\n",
       "      <td>5.525332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>0.170341</td>\n",
       "      <td>1497</td>\n",
       "      <td>0.054271</td>\n",
       "      <td>1.467575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>failure</th>\n",
       "      <td>0.126891</td>\n",
       "      <td>3901</td>\n",
       "      <td>0.010821</td>\n",
       "      <td>1.093229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>0.090392</td>\n",
       "      <td>29560</td>\n",
       "      <td>-0.025677</td>\n",
       "      <td>0.778779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean  count      diff      risk\n",
       "poutcome                                     \n",
       "success   0.641322   1210  0.525253  5.525332\n",
       "other     0.170341   1497  0.054271  1.467575\n",
       "failure   0.126891   3901  0.010821  1.093229\n",
       "unknown   0.090392  29560 -0.025677  0.778779"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for c in cat_features:\n",
    "    print(c)\n",
    "    df_group = df_full_train.groupby(c).y.agg([\"mean\", \"count\"])\n",
    "    df_group[\"diff\"] = df_group[\"mean\"] - global_subscription_rate\n",
    "    df_group[\"risk\"] = df_group[\"mean\"] / global_subscription_rate\n",
    "    df_group.sort_values(\"risk\", ascending=False, inplace=True)\n",
    "    display(df_group)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Calculate the mutual information score between `y` and other categorical variables in our dataset. Use the training set only.\n",
    "- Round the scores to 2 decimals using `round(score, 2)`.\n",
    "\n",
    "Which of these variables has the lowest mutual information score?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>MI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MI\n",
       "month      0.03\n",
       "poutcome   0.03\n",
       "job        0.01\n",
       "housing    0.01\n",
       "contact    0.01\n",
       "marital    0.00\n",
       "education  0.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_mi(series):\n",
    "    return mutual_info_score(series, df_train[\"y\"])\n",
    "\n",
    "\n",
    "df_mi = df_train[cat_features].apply(calculate_mi).round(2)\n",
    "df_mi = df_mi.sort_values(ascending=False).to_frame(name=\"MI\")\n",
    "\n",
    "display(df_mi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The variable with the highest mutual information score is: Month\n",
      "with a MI score of: 0.03\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"The variable with the highest mutual information score is: {df_mi.iloc[0].name.replace(\"_\", \" \").capitalize()}\n",
    "with a MI score of: {df_mi.iloc[0].values[0]}\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But `month` is not included in the list of possible answers. So, the correct answer is `poutcome`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Make sure that the target value `y` is not in your dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.drop(\"y\", axis=1)\n",
    "df_val = df_val.drop(\"y\", axis=1)\n",
    "df_test = df_test.drop(\"y\", axis=1)\n",
    "\n",
    "assert \"y\" not in df_train.columns\n",
    "assert \"y\" not in df_val.columns\n",
    "assert \"y\" not in df_test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now let's train a logistic regression.\n",
    "- Remember that we have several categorical variables in the dataset. Include them using one-hot encoding.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = DictVectorizer(sparse=False)\n",
    "train_dict = df_train.to_dict(orient=\"records\")\n",
    "X_train = dv.fit_transform(train_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Fit the model on the training dataset.\n",
    "  - To make sure the results are reproducible across different versions of Scikit-Learn, fit the model with these parameters:\n",
    "  - `model = LogisticRegression(solver='liblinear', C=1.0, max_iter=1000, random_state=42)`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "print(y_train.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=42, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000, random_state=42, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=42, solver='liblinear')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(solver=\"liblinear\", max_iter=1000, C=1.0, random_state=SEED)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Calculate the accuracy on the validation dataset and round it to 2 decimal digits.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dict = df_val.to_dict(orient=\"records\")\n",
    "X_val = dv.transform(val_dict)\n",
    "\n",
    "y_pred = model.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_val, y_pred)\n",
    "accuracy_rounded = round(accuracy, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What accuracy did you get?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.8998\n",
      "Accuracy rounded = 0.9\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy = {accuracy:.4f}\")\n",
    "print(f\"Accuracy rounded = {accuracy_rounded}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's find the least useful feature using the _feature elimination_ technique.\n",
    "- Train a model with all these features (using the same parameters as in Q4).\n",
    "- Now exclude each feature from this set and train a model without it. Record the accuracy for each model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'job',\n",
       " 'marital',\n",
       " 'education',\n",
       " 'default',\n",
       " 'balance',\n",
       " 'housing',\n",
       " 'loan',\n",
       " 'contact',\n",
       " 'day',\n",
       " 'month',\n",
       " 'duration',\n",
       " 'campaign',\n",
       " 'pdays',\n",
       " 'previous',\n",
       " 'poutcome']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df_train.columns.to_list()\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "orig_score = accuracy\n",
    "orig_score_rounded = accuracy_rounded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- For each feature, calculate the difference between the original accuracy and the accuracy without the feature.\n",
    "\n",
    "Which of following feature has the smallest difference?\n",
    "\n",
    "- `age`\n",
    "- `balance`\n",
    "- `marital`\n",
    "- `previous`\n",
    "\n",
    "> **Note**: the difference doesn't have to be positive\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = pd.DataFrame(columns=[\"eliminated_feature\", \"accuracy\", \"difference\"])\n",
    "\n",
    "for c in features:\n",
    "    subset = features.copy()\n",
    "    subset.remove(c)\n",
    "\n",
    "    train_dict = df_train[subset].to_dict(orient=\"records\")\n",
    "\n",
    "    dv = DictVectorizer(sparse=False)\n",
    "    dv.fit(train_dict)\n",
    "\n",
    "    X_train = dv.transform(train_dict)\n",
    "\n",
    "    model = LogisticRegression(\n",
    "        solver=\"liblinear\", C=1.0, max_iter=1000, random_state=SEED\n",
    "    )\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    val_dict = df_val[subset].to_dict(orient=\"records\")\n",
    "    X_val = dv.transform(val_dict)\n",
    "\n",
    "    y_pred = model.predict(X_val)\n",
    "\n",
    "    score = accuracy_score(y_val, y_pred)\n",
    "    # print(c, orig_score - score, score)\n",
    "    scores.loc[len(scores)] = [c, score, np.abs(orig_score - score)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>eliminated_feature</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>duration</td>\n",
       "      <td>0.890400</td>\n",
       "      <td>0.009401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>0.894492</td>\n",
       "      <td>0.005309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>marital</td>\n",
       "      <td>0.900796</td>\n",
       "      <td>0.000995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>loan</td>\n",
       "      <td>0.900575</td>\n",
       "      <td>0.000774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>default</td>\n",
       "      <td>0.900464</td>\n",
       "      <td>0.000664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>contact</td>\n",
       "      <td>0.900464</td>\n",
       "      <td>0.000664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>job</td>\n",
       "      <td>0.900354</td>\n",
       "      <td>0.000553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.900243</td>\n",
       "      <td>0.000442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>balance</td>\n",
       "      <td>0.900133</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pdays</td>\n",
       "      <td>0.900133</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>previous</td>\n",
       "      <td>0.900133</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>housing</td>\n",
       "      <td>0.899580</td>\n",
       "      <td>0.000221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>month</td>\n",
       "      <td>0.899580</td>\n",
       "      <td>0.000221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>education</td>\n",
       "      <td>0.900022</td>\n",
       "      <td>0.000221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>day</td>\n",
       "      <td>0.900022</td>\n",
       "      <td>0.000221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>campaign</td>\n",
       "      <td>0.900022</td>\n",
       "      <td>0.000221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   eliminated_feature  accuracy  difference\n",
       "11           duration  0.890400    0.009401\n",
       "15           poutcome  0.894492    0.005309\n",
       "2             marital  0.900796    0.000995\n",
       "7                loan  0.900575    0.000774\n",
       "4             default  0.900464    0.000664\n",
       "8             contact  0.900464    0.000664\n",
       "1                 job  0.900354    0.000553\n",
       "0                 age  0.900243    0.000442\n",
       "5             balance  0.900133    0.000332\n",
       "13              pdays  0.900133    0.000332\n",
       "14           previous  0.900133    0.000332\n",
       "6             housing  0.899580    0.000221\n",
       "10              month  0.899580    0.000221\n",
       "3           education  0.900022    0.000221\n",
       "9                 day  0.900022    0.000221\n",
       "12           campaign  0.900022    0.000221"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = scores.sort_values(\"difference\", ascending=False)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature:      age  Difference: 0.00044238000442375913\n",
      "Feature:  balance  Difference: 0.00033178500331776384\n",
      "Feature:  marital  Difference: 0.0009953550099535136\n",
      "Feature: previous  Difference: 0.00033178500331776384\n"
     ]
    }
   ],
   "source": [
    "options = [\"age\", \"balance\", \"marital\", \"previous\"]\n",
    "\n",
    "for opt in options:\n",
    "    # if in options, print the feature and the difference\n",
    "    print(\n",
    "        f\"Feature: {opt:>8}  Difference: {scores[scores.eliminated_feature == opt].difference.values[0]}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`age`, `balance`, `martial`, `previous` are possible answers.\n",
    "\n",
    "`balance` and `previous` have the lowest scores not rounding the accuracy to 2 decimal digits.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_rounded = pd.DataFrame(columns=[\"eliminated_feature\", \"accuracy\", \"difference\"])\n",
    "\n",
    "for c in features:\n",
    "    subset = features.copy()\n",
    "    subset.remove(c)\n",
    "\n",
    "    train_dict = df_train[subset].to_dict(orient=\"records\")\n",
    "\n",
    "    dv = DictVectorizer(sparse=False)\n",
    "    dv.fit(train_dict)\n",
    "\n",
    "    X_train = dv.transform(train_dict)\n",
    "\n",
    "    model = LogisticRegression(\n",
    "        solver=\"liblinear\", C=1.0, max_iter=1000, random_state=SEED\n",
    "    )\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    val_dict = df_val[subset].to_dict(orient=\"records\")\n",
    "    X_val = dv.transform(val_dict)\n",
    "\n",
    "    y_pred = model.predict(X_val)\n",
    "\n",
    "    score = accuracy_score(y_val, y_pred)\n",
    "    # print(c, orig_score - score, score)\n",
    "    scores_rounded.loc[len(scores_rounded)] = [\n",
    "        c,\n",
    "        score,\n",
    "        np.abs(orig_score_rounded - score),\n",
    "    ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>eliminated_feature</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>duration</td>\n",
       "      <td>0.890400</td>\n",
       "      <td>0.009600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>0.894492</td>\n",
       "      <td>0.005508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>marital</td>\n",
       "      <td>0.900796</td>\n",
       "      <td>0.000796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>loan</td>\n",
       "      <td>0.900575</td>\n",
       "      <td>0.000575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>default</td>\n",
       "      <td>0.900464</td>\n",
       "      <td>0.000464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>contact</td>\n",
       "      <td>0.900464</td>\n",
       "      <td>0.000464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>housing</td>\n",
       "      <td>0.899580</td>\n",
       "      <td>0.000420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>month</td>\n",
       "      <td>0.899580</td>\n",
       "      <td>0.000420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>job</td>\n",
       "      <td>0.900354</td>\n",
       "      <td>0.000354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.900243</td>\n",
       "      <td>0.000243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>balance</td>\n",
       "      <td>0.900133</td>\n",
       "      <td>0.000133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pdays</td>\n",
       "      <td>0.900133</td>\n",
       "      <td>0.000133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>previous</td>\n",
       "      <td>0.900133</td>\n",
       "      <td>0.000133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>education</td>\n",
       "      <td>0.900022</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>day</td>\n",
       "      <td>0.900022</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>campaign</td>\n",
       "      <td>0.900022</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   eliminated_feature  accuracy  difference\n",
       "11           duration  0.890400    0.009600\n",
       "15           poutcome  0.894492    0.005508\n",
       "2             marital  0.900796    0.000796\n",
       "7                loan  0.900575    0.000575\n",
       "4             default  0.900464    0.000464\n",
       "8             contact  0.900464    0.000464\n",
       "6             housing  0.899580    0.000420\n",
       "10              month  0.899580    0.000420\n",
       "1                 job  0.900354    0.000354\n",
       "0                 age  0.900243    0.000243\n",
       "5             balance  0.900133    0.000133\n",
       "13              pdays  0.900133    0.000133\n",
       "14           previous  0.900133    0.000133\n",
       "3           education  0.900022    0.000022\n",
       "9                 day  0.900022    0.000022\n",
       "12           campaign  0.900022    0.000022"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_rounded = scores_rounded.sort_values(\"difference\", ascending=False)\n",
    "scores_rounded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`age`, `balance`, `martial`, `previous` are possible answers.\n",
    "\n",
    "`balance` and `previous` have the lowest scores rounding the accuracy to 2 decimal digits.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.11.10 | packaged by conda-forge | (main, Sep 30 2024, 17:58:42) [Clang 17.0.6 ]\n",
      "Python Platform: macOS-14.7-arm64-arm-64bit\n",
      "\n",
      "Pandas 2.2.3\n",
      "NumPy 1.26.4\n",
      "Scikit-Learn 1.5.2\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "import sys\n",
    "import sklearn as sk\n",
    "\n",
    "print(f\"Python {sys.version}\")\n",
    "print(f\"Python Platform: {platform.platform ()}\")\n",
    "print()\n",
    "print(f\"Pandas {pd.__version__}\")\n",
    "print(f\"NumPy {np.__version__}\")\n",
    "print(f\"Scikit-Learn {sk.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[claude.ai](https://claude.ai/chat/6b1f9d62-6dc3-4855-a9f7-33fd2b8e8796)\n",
    "\n",
    "and dobble-check the results with [chatGPT](https://chatgpt.com/c/67099617-d30c-800d-b55f-32d701d13b02)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  eliminated_feature  accuracy  difference\n",
      "0           previous  0.881663    0.018138\n",
      "1                age  0.880447    0.019354\n",
      "2            balance  0.880336    0.019465\n",
      "3            marital  0.880226    0.019575\n",
      "\n",
      "The feature with the smallest difference is: previous\n"
     ]
    }
   ],
   "source": [
    "original_score = accuracy\n",
    "# Assuming df_train, df_val, y_train, y_val, SEED, and original_score are defined\n",
    "\n",
    "features = [\"age\", \"balance\", \"marital\", \"previous\"]  # Focus on these features\n",
    "scores = pd.DataFrame(columns=[\"eliminated_feature\", \"accuracy\", \"difference\"])\n",
    "\n",
    "# Fit DictVectorizer on full training set once\n",
    "full_dv = DictVectorizer(sparse=False)\n",
    "full_train_dict = df_train[features].to_dict(orient=\"records\")\n",
    "full_X_train = full_dv.fit_transform(full_train_dict)\n",
    "\n",
    "# Transform validation data once\n",
    "full_val_dict = df_val[features].to_dict(orient=\"records\")\n",
    "full_X_val = full_dv.transform(full_val_dict)\n",
    "\n",
    "for feature in features:\n",
    "    # Find columns to keep (exclude the current feature)\n",
    "    keep_cols = [\n",
    "        col for col in full_dv.get_feature_names_out() if not col.startswith(feature)\n",
    "    ]\n",
    "\n",
    "    # Select features for training and validation\n",
    "    X_train = full_X_train[:, [full_dv.vocabulary_[col] for col in keep_cols]]\n",
    "    X_val = full_X_val[:, [full_dv.vocabulary_[col] for col in keep_cols]]\n",
    "\n",
    "    model = LogisticRegression(\n",
    "        solver=\"liblinear\", max_iter=1000, C=1.0, random_state=SEED\n",
    "    )\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    y_pred = model.predict(X_val)\n",
    "    score = accuracy_score(y_val, y_pred)\n",
    "    scores.loc[len(scores)] = [feature, score, original_score - score]\n",
    "\n",
    "# Calculate absolute difference\n",
    "scores[\"difference\"] = scores[\"difference\"].abs()\n",
    "scores = scores.sort_values(by=\"difference\", ascending=True).reset_index(drop=True)\n",
    "\n",
    "print(scores)\n",
    "\n",
    "# Identify the feature with the smallest difference\n",
    "smallest_diff_feature = scores.iloc[0][\"eliminated_feature\"]\n",
    "print(f\"\\nThe feature with the smallest difference is: {smallest_diff_feature}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`balance` and `previous` have the smallest difference and are the least useful features.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer seems not to be scored in the leaderboard. They gave the point, but it is marked red.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 6\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now let's train a regularized logistic regression.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((27126,), (9042,))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    y_train.shape,\n",
    "    y_val.shape,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = DictVectorizer(sparse=False)\n",
    "train_dict = df_train.to_dict(orient=\"records\")\n",
    "X_train = dv.fit_transform(train_dict)\n",
    "\n",
    "val_dict = df_val.to_dict(orient=\"records\")\n",
    "X_val = dv.transform(val_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's try the following values of the parameter `C`: `[0, 0.01, 0.1, 1, 10]`.\n",
    "- Train models using all the features as in Q4.\n",
    "- Calculate the accuracy on the validation dataset and round it to 3 decimal digits.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C = 0.01: accuracy_score = 0.898\n",
      "C =  0.1: accuracy_score = 0.901\n",
      "C =    1: accuracy_score = 0.9\n",
      "C =   10: accuracy_score = 0.9\n",
      "C =  100: accuracy_score = 0.9\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "scores = {}\n",
    "for C in [0.01, 0.1, 1, 10, 100]:\n",
    "    # warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "    model = LogisticRegression(\n",
    "        C=C, solver=\"liblinear\", random_state=SEED, max_iter=1000\n",
    "    )\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    y_pred = model.predict(X_val)\n",
    "\n",
    "    score = accuracy_score(\n",
    "        y_val,\n",
    "        y_pred,\n",
    "    )  # root_mean_squared_error\n",
    "    scores[C] = round(score, 3)\n",
    "    print(f\"C = {C:>4}: accuracy_score = {round(score, 3)}\")\n",
    "\n",
    "# scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which of these `C` leads to the best accuracy on the validation set?\n",
    "\n",
    "> **Note**: If there are multiple options, select the smallest `C`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The `C` value of 0.1\n",
      "leads to the best accuracy (= 0.901)\n",
      "of the validation set and is the smallest 'C'.\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"The `C` value of {max(scores, key=scores.get)}\n",
    "leads to the best accuracy (= {scores[max(scores, key=scores.get)]})\n",
    "of the validation set and is the smallest 'C'.\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Update:** the correct answer is `0.1` or `10`. My calculations were right - my conclusion was wrong. I picked the lowest accuracy score, not the best one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean of 10 runs: 6.82834s\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(42)\n",
    "a = np.random.uniform(size=(300, 300))\n",
    "runtimes = 10\n",
    "\n",
    "timecosts = []\n",
    "for _ in range(runtimes):\n",
    "    s_time = time.time()\n",
    "    for i in range(100):\n",
    "        a += 1\n",
    "        np.linalg.svd(a)\n",
    "    timecosts.append(time.time() - s_time)\n",
    "\n",
    "print(f\"mean of {runtimes} runs: {np.mean(timecosts):.5f}s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homework URL\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Homework 03](https://github.com/TillMeineke/ML_Zoomcamp2024_hw/blob/main/03-classification/homework_03_till_meineke.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning in public links (optional)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x] Learn in public 1: weekly learning [LinkedIn](https://www.linkedin.com/posts/tillmeineke_mlzoomcamp-activity-7251516995173568514-G2ok?utm_source=share&utm_medium=member_desktop) on 14 October 2024\n",
    "- [x] Learn in public 2, 3 & 4: Keep the pace\n",
    "  - [x] [medium](https://medium.com/@till.meineke/how-to-keep-up-with-the-ml-zoomcamp-2024-2ba82fdf98db) on 14 October 2024\n",
    "  - [x] [linkedIn](https://www.linkedin.com/posts/tillmeineke_how-to-keep-up-with-the-ml-zoomcamp-2024-activity-7251568053262385152-yvz6?utm_source=share&utm_medium=member_desktop) on 14 October 2024\n",
    "  - [x] [Slack](https://datatalks-club.slack.com/archives/C0288NJ5XSA/p1728915061240629?thread_ts=1728596106.858199&cid=C0288NJ5XSA) on 14 October 2024\n",
    "- [x] Learn in public 5 & 6: Subtitle adventure\n",
    "  - [x] [medium](https://medium.com/@till.meineke/unlocking-the-power-of-ml-zoomcamp-a-subtitle-adventure-60aa7343d988) on 16 October 2024\n",
    "  - [x] [linkedIn](https://www.linkedin.com/posts/tillmeineke_unlocking-the-power-of-ml-zoomcamp-a-subtitle-activity-7252423016293224448--Zhi?utm_source=share&utm_medium=member_desktop) on 16 October 2024\n",
    "- [x] Learn in public 7: Gemini is takeing project lead [Slack](https://datatalks-club.slack.com/archives/C0288NJ5XSA/p1729065197020029) on 16 October 2024\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time spent on lectures (hours) (optional)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time spent on homework (hours) (optional)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FAQ contribution (FAQ document, optional)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "None of the videos have how to install the environment in Mac, does someone have instructions for Mac with M1 chip?\n",
    "\n",
    "https://medium.com/p/eceb6e42e36e\n",
    "\n",
    "I wrote an article on medium describing the setup of a conda ML environment on AppleSilicon\n"
   ]
  }
 ],
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