RainPrediction_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rain Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Place</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albury39783</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>71</td>\n",
       "      <td>22</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albury39784</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>44</td>\n",
       "      <td>25</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albury39785</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albury39786</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albury39787</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>82</td>\n",
       "      <td>33</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID   Place  MinTemp  MaxTemp  Evaporation  Sunshine  \\\n",
       "0  Albury39783  Albury     13.4     22.9          0.0       0.0   \n",
       "1  Albury39784  Albury      7.4     25.1          0.0       0.0   \n",
       "2  Albury39785  Albury     12.9     25.7          0.0       0.0   \n",
       "3  Albury39786  Albury      9.2     28.0          0.0       0.0   \n",
       "4  Albury39787  Albury     17.5     32.3          0.0       0.0   \n",
       "\n",
       "   WindGustSpeed  WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  \\\n",
       "0             44            20            24           71           22   \n",
       "1             44             4            22           44           25   \n",
       "2             46            19            26           38           30   \n",
       "3             24            11             9           45           16   \n",
       "4             41             7            20           82           33   \n",
       "\n",
       "   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0       1007.7       1007.1         8         0     16.9     21.8          0   \n",
       "1       1010.6       1007.8         0         0     17.2     24.3          0   \n",
       "2       1007.6       1008.7         0         2     21.0     23.2          0   \n",
       "3       1017.6       1012.8         0         0     18.1     26.5          0   \n",
       "4       1010.8       1006.0         7         8     17.8     29.7          0   \n",
       "\n",
       "   RainTomorrow  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain = pd.read_excel('RainPrediction.xlsx')\n",
    "rain.columns = rain.columns.str.strip()\n",
    "rain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.131807</td>\n",
       "      <td>23.174186</td>\n",
       "      <td>3.129340</td>\n",
       "      <td>3.988338</td>\n",
       "      <td>37.377592</td>\n",
       "      <td>13.869248</td>\n",
       "      <td>18.292855</td>\n",
       "      <td>67.984915</td>\n",
       "      <td>50.175564</td>\n",
       "      <td>917.357684</td>\n",
       "      <td>915.433845</td>\n",
       "      <td>2.762801</td>\n",
       "      <td>2.695034</td>\n",
       "      <td>16.879510</td>\n",
       "      <td>21.271466</td>\n",
       "      <td>0.221213</td>\n",
       "      <td>0.224181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.440548</td>\n",
       "      <td>7.194768</td>\n",
       "      <td>4.166674</td>\n",
       "      <td>4.688665</td>\n",
       "      <td>16.433198</td>\n",
       "      <td>8.954477</td>\n",
       "      <td>9.075870</td>\n",
       "      <td>20.416069</td>\n",
       "      <td>22.071424</td>\n",
       "      <td>303.403253</td>\n",
       "      <td>302.370443</td>\n",
       "      <td>3.132999</td>\n",
       "      <td>3.050051</td>\n",
       "      <td>6.611499</td>\n",
       "      <td>7.486759</td>\n",
       "      <td>0.415065</td>\n",
       "      <td>0.417043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.500000</td>\n",
       "      <td>-4.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.200000</td>\n",
       "      <td>-5.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1011.000000</td>\n",
       "      <td>1008.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>16.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1016.700000</td>\n",
       "      <td>1014.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.800000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1021.800000</td>\n",
       "      <td>1019.400000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>26.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.900000</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1041.000000</td>\n",
       "      <td>1039.600000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>40.200000</td>\n",
       "      <td>46.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MinTemp        MaxTemp    Evaporation       Sunshine  \\\n",
       "count  142193.000000  142193.000000  142193.000000  142193.000000   \n",
       "mean       12.131807      23.174186       3.129340       3.988338   \n",
       "std         6.440548       7.194768       4.166674       4.688665   \n",
       "min        -8.500000      -4.800000       0.000000       0.000000   \n",
       "25%         7.500000      17.900000       0.000000       0.000000   \n",
       "50%        12.000000      22.600000       1.600000       0.200000   \n",
       "75%        16.800000      28.200000       5.400000       8.700000   \n",
       "max        33.900000      48.100000     145.000000      14.500000   \n",
       "\n",
       "       WindGustSpeed   WindSpeed9am   WindSpeed3pm    Humidity9am  \\\n",
       "count  142193.000000  142193.000000  142193.000000  142193.000000   \n",
       "mean       37.377592      13.869248      18.292855      67.984915   \n",
       "std        16.433198       8.954477       9.075870      20.416069   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%        30.000000       7.000000      11.000000      56.000000   \n",
       "50%        37.000000      13.000000      17.000000      70.000000   \n",
       "75%        46.000000      19.000000      24.000000      83.000000   \n",
       "max       135.000000     130.000000      87.000000     100.000000   \n",
       "\n",
       "         Humidity3pm    Pressure9am    Pressure3pm       Cloud9am  \\\n",
       "count  142193.000000  142193.000000  142193.000000  142193.000000   \n",
       "mean       50.175564     917.357684     915.433845       2.762801   \n",
       "std        22.071424     303.403253     302.370443       3.132999   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%        35.000000    1011.000000    1008.500000       0.000000   \n",
       "50%        51.000000    1016.700000    1014.200000       1.000000   \n",
       "75%        65.000000    1021.800000    1019.400000       6.000000   \n",
       "max       100.000000    1041.000000    1039.600000       9.000000   \n",
       "\n",
       "            Cloud3pm        Temp9am        Temp3pm      RainToday  \\\n",
       "count  142193.000000  142193.000000  142193.000000  142193.000000   \n",
       "mean        2.695034      16.879510      21.271466       0.221213   \n",
       "std         3.050051       6.611499       7.486759       0.415065   \n",
       "min         0.000000      -7.200000      -5.400000       0.000000   \n",
       "25%         0.000000      12.200000      16.300000       0.000000   \n",
       "50%         1.000000      16.600000      20.900000       0.000000   \n",
       "75%         6.000000      21.500000      26.300000       0.000000   \n",
       "max         9.000000      40.200000      46.700000       1.000000   \n",
       "\n",
       "        RainTomorrow  \n",
       "count  142193.000000  \n",
       "mean        0.224181  \n",
       "std         0.417043  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 142193 entries, 0 to 142192\n",
      "Data columns (total 19 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   ID             142193 non-null  object \n",
      " 1   Place          142193 non-null  object \n",
      " 2   MinTemp        142193 non-null  float64\n",
      " 3   MaxTemp        142193 non-null  float64\n",
      " 4   Evaporation    142193 non-null  float64\n",
      " 5   Sunshine       142193 non-null  float64\n",
      " 6   WindGustSpeed  142193 non-null  int64  \n",
      " 7   WindSpeed9am   142193 non-null  int64  \n",
      " 8   WindSpeed3pm   142193 non-null  int64  \n",
      " 9   Humidity9am    142193 non-null  int64  \n",
      " 10  Humidity3pm    142193 non-null  int64  \n",
      " 11  Pressure9am    142193 non-null  float64\n",
      " 12  Pressure3pm    142193 non-null  float64\n",
      " 13  Cloud9am       142193 non-null  int64  \n",
      " 14  Cloud3pm       142193 non-null  int64  \n",
      " 15  Temp9am        142193 non-null  float64\n",
      " 16  Temp3pm        142193 non-null  float64\n",
      " 17  RainToday      142193 non-null  int64  \n",
      " 18  RainTomorrow   142193 non-null  int64  \n",
      "dtypes: float64(8), int64(9), object(2)\n",
      "memory usage: 20.6+ MB\n"
     ]
    }
   ],
   "source": [
    "rain.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_vars = rain.drop(columns = ['ID', 'Place', 'RainToday', 'RainTomorrow']).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2520x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "\n",
    "sns.set(rc={'figure.figsize':(35,8)})\n",
    "sns.countplot(data = rain, x = 'Place', order = rain['Place'].value_counts().index, color = '#3274a1').set_title(\"Distribution of Place\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize = (15,8))\n",
    "\n",
    "sns.countplot(data = rain, x = 'RainToday', ax = axes[0]).set_title(\"Distribution of Rain Today\")\n",
    "sns.countplot(data = rain, x = 'RainTomorrow', ax = axes[1]).set_title(\"Distribution of Rain Tomorrow\")\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x864 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "\n",
    "fig, axes = plt.subplots(5,3, figsize = (25,12))\n",
    "plt.suptitle('Distribution of Numeric Variables', fontsize = 15, y = 1)\n",
    "\n",
    "for col, ax in zip(numeric_vars, axes.flat):\n",
    "    #plot the histogram\n",
    "    sns.histplot(data = rain,\n",
    "                x = col,\n",
    "                kde = True,\n",
    "                hue = 'RainTomorrow',\n",
    "                ax = ax)\n",
    "\n",
    "    def move_legend(ax, new_loc, **kws):\n",
    "        old_legend = ax.legend_\n",
    "        handles = old_legend.legendHandles\n",
    "        labels = [t.get_text() for t in old_legend.get_texts()]\n",
    "        title = old_legend.get_title().get_text()\n",
    "        ax.legend(handles, labels, loc=new_loc, title=title, **kws)\n",
    "    \n",
    "    move_legend(ax, \"upper right\")\n",
    "\n",
    "    #chart formatting\n",
    "    sns.despine()\n",
    "    \n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x864 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "\n",
    "fig, axes = plt.subplots(5,3, figsize = (25,12))\n",
    "plt.suptitle('Distribution of Numeric Variables', fontsize = 15, y = 1)\n",
    "\n",
    "for col, ax in zip(numeric_vars, axes.flat):\n",
    "    #plot the histogram\n",
    "    sns.boxplot(data = rain,\n",
    "                   x = 'RainTomorrow',\n",
    "                   y = col,\n",
    "                   ax = ax)\n",
    "\n",
    "    #chart formatting\n",
    "    sns.despine()\n",
    "    \n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Functions for Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = pd.DataFrame(columns = ['Model', 'Accuracy', 'Precision', 'Recall', 'F-Score', 'AUC'])\n",
    "\n",
    "def eval(model, y_test, y_pred):\n",
    "    model = model\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    pre = precision_score(y_test, y_pred)\n",
    "    rec = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "    eval_df = pd.DataFrame({'Model':model,\n",
    "                        'Accuracy':acc,\n",
    "                        'Precision':pre,\n",
    "                        'Recall':rec,\n",
    "                        'F-Score':f1,\n",
    "                        'AUC':auc}, index = [0])\n",
    "\n",
    "    return eval_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TRAIN TEST SPLIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22314.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate test data based on the distribution of of target variable to avoid bias\n",
    "\n",
    "train_count = round(rain.RainTomorrow.value_counts()[1] * .70,0)\n",
    "train_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = rain['RainTomorrow']\n",
    "y0 = rain[rain['RainTomorrow'] == 0]['RainTomorrow']\n",
    "y1 = rain[rain['RainTomorrow'] == 1]['RainTomorrow']\n",
    "\n",
    "y1_train = y1.sample(n = int(train_count), replace = False, random_state = 123)\n",
    "y0_train = y0.sample(n = int(train_count), replace = False, random_state = 123)\n",
    "\n",
    "y_train = pd.concat([y0_train, y1_train]).sort_index()\n",
    "y_test = y[ ~ y.index.isin(y_train.index)]\n",
    "\n",
    "x = rain.drop(columns = ['ID','RainTomorrow'])\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(x['Place'])\n",
    "x['Place'] = encoder.transform(x['Place'])\n",
    "\n",
    "x_train = x[~x.index.isin(y_test.index)]\n",
    "x_test = x[x.index.isin(y_test.index)]\n",
    "\n",
    "assert len(y_train) + len(y_test) == len(y)\n",
    "assert y_train.index.isin(y_test.index).sum() == 0\n",
    "assert y_train.index.all() == x_train.index.all()\n",
    "assert y_test.index.all() == x_test.index.all()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL TUNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7431887483203006\n",
      "{'logistic__C': 0.1, 'logistic__penalty': 'l2', 'logistic__solver': 'lbfgs'}\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "# Define steps\n",
    "steps = [('scaler', StandardScaler()),\n",
    "         ('logistic', LogisticRegression())]\n",
    "\n",
    "# Instantiate pipeline object\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Define parameters\n",
    "penalty = ['l2']\n",
    "c = [100, 10, 1.0, 0.1, 0.01]\n",
    "solvers = ['newton-cg', 'lbfgs', 'liblinear']\n",
    "\n",
    "parameters = dict(logistic__penalty = penalty, logistic__C = c, logistic__solver = solvers)\n",
    "\n",
    "model_tuning = GridSearchCV(pipeline, parameters, error_score = 'raise', cv = 5)\n",
    "model_tuning.fit(x_train, y_train)\n",
    "\n",
    "print(model_tuning.best_score_)\n",
    "print(model_tuning.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL BUILDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('logistic', LogisticRegression(C=100, solver='newton-cg'))])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define steps\n",
    "steps = [('scaler', StandardScaler()),\n",
    "         ('logistic', LogisticRegression(C = 100,\n",
    "                                         penalty = 'l2',\n",
    "                                         solver = 'newton-cg'))]\n",
    "\n",
    "\n",
    "log = Pipeline(steps)\n",
    "\n",
    "log.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FORECASTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_predictions = pd.DataFrame(log.predict(x_test))\n",
    "log_predictions_prob = pd.DataFrame(log.predict_proba(x_test))[1]\n",
    "\n",
    "log_predictions.index = x_test.index\n",
    "log_predictions_prob.index = x_test.index\n",
    "\n",
    "log_predictions = pd.concat([y_test, log_predictions, log_predictions_prob], axis = 1, ignore_index = True)\n",
    "log_predictions.columns = ['Actual', 'Predicted', 'Predicted Probability of Rain']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(log_predictions['Actual'], log_predictions['Predicted'])\n",
    "\n",
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']); \n",
    "ax.yaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ADJUST PROBABILITY to 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_predictions['Predicted_60'] = 0\n",
    "for i in log_predictions.index:\n",
    "    if log_predictions.loc[i, 'Predicted Probability of Rain'] >= .60:\n",
    "        log_predictions.loc[i, 'Predicted_60'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_60</td>\n",
       "      <td>0.822488</td>\n",
       "      <td>0.308645</td>\n",
       "      <td>0.654083</td>\n",
       "      <td>0.419391</td>\n",
       "      <td>0.747436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy  Precision    Recall   F-Score       AUC\n",
       "0  Logistic Regression_60  0.822488   0.308645  0.654083  0.419391  0.747436"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_metrics = eval('Logistic Regression_60', log_predictions['Actual'], log_predictions['Predicted_60'])\n",
    "metrics = pd.concat([metrics, log_metrics])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EVALUATION METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_60</td>\n",
       "      <td>0.822488</td>\n",
       "      <td>0.308645</td>\n",
       "      <td>0.654083</td>\n",
       "      <td>0.419391</td>\n",
       "      <td>0.747436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_50</td>\n",
       "      <td>0.756419</td>\n",
       "      <td>0.250474</td>\n",
       "      <td>0.745373</td>\n",
       "      <td>0.374951</td>\n",
       "      <td>0.751496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy  Precision    Recall   F-Score       AUC\n",
       "0  Logistic Regression_60  0.822488   0.308645  0.654083  0.419391  0.747436\n",
       "0  Logistic Regression_50  0.756419   0.250474  0.745373  0.374951  0.751496"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_metrics = eval('Logistic Regression_50', log_predictions['Actual'], log_predictions['Predicted'])\n",
    "metrics = pd.concat([metrics, log_metrics])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ADJUST PROBABILITY to 65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_predictions['Predicted_65'] = 0\n",
    "for i in log_predictions.index:\n",
    "    if log_predictions.loc[i, 'Predicted Probability of Rain'] >= .65:\n",
    "        log_predictions.loc[i, 'Predicted_65'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(log_predictions['Actual'], log_predictions['Predicted_65'])\n",
    "\n",
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']); \n",
    "ax.yaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_60</td>\n",
       "      <td>0.822488</td>\n",
       "      <td>0.308645</td>\n",
       "      <td>0.654083</td>\n",
       "      <td>0.419391</td>\n",
       "      <td>0.747436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_50</td>\n",
       "      <td>0.756419</td>\n",
       "      <td>0.250474</td>\n",
       "      <td>0.745373</td>\n",
       "      <td>0.374951</td>\n",
       "      <td>0.751496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_65</td>\n",
       "      <td>0.849290</td>\n",
       "      <td>0.345123</td>\n",
       "      <td>0.598975</td>\n",
       "      <td>0.437920</td>\n",
       "      <td>0.737733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy  Precision    Recall   F-Score       AUC\n",
       "0  Logistic Regression_60  0.822488   0.308645  0.654083  0.419391  0.747436\n",
       "0  Logistic Regression_50  0.756419   0.250474  0.745373  0.374951  0.751496\n",
       "0  Logistic Regression_65  0.849290   0.345123  0.598975  0.437920  0.737733"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_metrics = eval('Logistic Regression_65', log_predictions['Actual'], log_predictions['Predicted_65'])\n",
    "metrics = pd.concat([metrics, log_metrics])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ADJUST PROBABILITY to 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_predictions['Predicted_70'] = 0\n",
    "for i in log_predictions.index:\n",
    "    if log_predictions.loc[i, 'Predicted Probability of Rain'] >= .70:\n",
    "        log_predictions.loc[i, 'Predicted_70'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(log_predictions['Actual'], log_predictions['Predicted_70'])\n",
    "\n",
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']); \n",
    "ax.yaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_60</td>\n",
       "      <td>0.822488</td>\n",
       "      <td>0.308645</td>\n",
       "      <td>0.654083</td>\n",
       "      <td>0.419391</td>\n",
       "      <td>0.747436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_50</td>\n",
       "      <td>0.756419</td>\n",
       "      <td>0.250474</td>\n",
       "      <td>0.745373</td>\n",
       "      <td>0.374951</td>\n",
       "      <td>0.751496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_65</td>\n",
       "      <td>0.849290</td>\n",
       "      <td>0.345123</td>\n",
       "      <td>0.598975</td>\n",
       "      <td>0.437920</td>\n",
       "      <td>0.737733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_70</td>\n",
       "      <td>0.872208</td>\n",
       "      <td>0.390137</td>\n",
       "      <td>0.539370</td>\n",
       "      <td>0.452774</td>\n",
       "      <td>0.723874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy  Precision    Recall   F-Score       AUC\n",
       "0  Logistic Regression_60  0.822488   0.308645  0.654083  0.419391  0.747436\n",
       "0  Logistic Regression_50  0.756419   0.250474  0.745373  0.374951  0.751496\n",
       "0  Logistic Regression_65  0.849290   0.345123  0.598975  0.437920  0.737733\n",
       "0  Logistic Regression_70  0.872208   0.390137  0.539370  0.452774  0.723874"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_metrics = eval('Logistic Regression_70', log_predictions['Actual'], log_predictions['Predicted_70'])\n",
    "metrics = pd.concat([metrics, log_metrics])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ADJUST PROBABILITY to 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_predictions['Predicted_40'] = 0\n",
    "for i in log_predictions.index:\n",
    "    if log_predictions.loc[i, 'Predicted Probability of Rain'] >= .40:\n",
    "        log_predictions.loc[i, 'Predicted_40'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(log_predictions['Actual'], log_predictions['Predicted_40'])\n",
    "\n",
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']); \n",
    "ax.yaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_60</td>\n",
       "      <td>0.822488</td>\n",
       "      <td>0.308645</td>\n",
       "      <td>0.654083</td>\n",
       "      <td>0.419391</td>\n",
       "      <td>0.747436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_50</td>\n",
       "      <td>0.756419</td>\n",
       "      <td>0.250474</td>\n",
       "      <td>0.745373</td>\n",
       "      <td>0.374951</td>\n",
       "      <td>0.751496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_65</td>\n",
       "      <td>0.849290</td>\n",
       "      <td>0.345123</td>\n",
       "      <td>0.598975</td>\n",
       "      <td>0.437920</td>\n",
       "      <td>0.737733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_70</td>\n",
       "      <td>0.872208</td>\n",
       "      <td>0.390137</td>\n",
       "      <td>0.539370</td>\n",
       "      <td>0.452774</td>\n",
       "      <td>0.723874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_40</td>\n",
       "      <td>0.670681</td>\n",
       "      <td>0.205276</td>\n",
       "      <td>0.821813</td>\n",
       "      <td>0.328499</td>\n",
       "      <td>0.738036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy  Precision    Recall   F-Score       AUC\n",
       "0  Logistic Regression_60  0.822488   0.308645  0.654083  0.419391  0.747436\n",
       "0  Logistic Regression_50  0.756419   0.250474  0.745373  0.374951  0.751496\n",
       "0  Logistic Regression_65  0.849290   0.345123  0.598975  0.437920  0.737733\n",
       "0  Logistic Regression_70  0.872208   0.390137  0.539370  0.452774  0.723874\n",
       "0  Logistic Regression_40  0.670681   0.205276  0.821813  0.328499  0.738036"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_metrics = eval('Logistic Regression_40', log_predictions['Actual'], log_predictions['Predicted_40'])\n",
    "metrics = pd.concat([metrics, log_metrics])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DECISION TREE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL TUNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7353235460440058\n",
      "{'dt__max_depth': 10, 'dt__max_features': 11, 'dt__min_samples_leaf': 0.1}\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "# Define steps\n",
    "steps = [('scaler', StandardScaler()),\n",
    "         ('dt', DecisionTreeClassifier())]\n",
    "\n",
    "# Instantiate pipeline object\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Define parameters\n",
    "max_depth = range(1,11,1)\n",
    "min_samples_leaf = np.linspace(0.1, 0.5, 5, endpoint=True)\n",
    "max_features = np.arange(1,18,1)\n",
    "\n",
    "parameters = dict(dt__max_depth = max_depth, dt__min_samples_leaf = min_samples_leaf, dt__max_features = max_features)\n",
    "\n",
    "model_tuning = GridSearchCV(pipeline, parameters, error_score = 'raise')\n",
    "model_tuning.fit(x_train, y_train)\n",
    "\n",
    "print(model_tuning.best_score_)\n",
    "print(model_tuning.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL BUILDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('dt',\n",
       "                 DecisionTreeClassifier(max_depth=10, max_features=11,\n",
       "                                        min_samples_leaf=0.1))])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define steps\n",
    "steps = [('scaler', StandardScaler()),\n",
    "         ('dt', DecisionTreeClassifier(max_depth = 10,\n",
    "                                       max_features = 11,\n",
    "                                       min_samples_leaf = .1))]\n",
    "\n",
    "\n",
    "dt = Pipeline(steps)\n",
    "\n",
    "dt.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FORECASTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_predictions = pd.DataFrame(dt.predict(x_test))\n",
    "\n",
    "dt_predictions.index = x_test.index\n",
    "\n",
    "dt_predictions = pd.concat([y_test, dt_predictions], axis = 1, ignore_index = True)\n",
    "dt_predictions.columns = ['Actual', 'Predicted']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(dt_predictions['Actual'], dt_predictions['Predicted'])\n",
    "\n",
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']); \n",
    "ax.yaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EVALUATION METRICS"
   ]
  },
  {
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_60</td>\n",
       "      <td>0.822488</td>\n",
       "      <td>0.308645</td>\n",
       "      <td>0.654083</td>\n",
       "      <td>0.419391</td>\n",
       "      <td>0.747436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_50</td>\n",
       "      <td>0.756419</td>\n",
       "      <td>0.250474</td>\n",
       "      <td>0.745373</td>\n",
       "      <td>0.374951</td>\n",
       "      <td>0.751496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_65</td>\n",
       "      <td>0.849290</td>\n",
       "      <td>0.345123</td>\n",
       "      <td>0.598975</td>\n",
       "      <td>0.437920</td>\n",
       "      <td>0.737733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_70</td>\n",
       "      <td>0.872208</td>\n",
       "      <td>0.390137</td>\n",
       "      <td>0.539370</td>\n",
       "      <td>0.452774</td>\n",
       "      <td>0.723874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_40</td>\n",
       "      <td>0.670681</td>\n",
       "      <td>0.205276</td>\n",
       "      <td>0.821813</td>\n",
       "      <td>0.328499</td>\n",
       "      <td>0.738036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.741014</td>\n",
       "      <td>0.232899</td>\n",
       "      <td>0.715989</td>\n",
       "      <td>0.351471</td>\n",
       "      <td>0.729861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy  Precision    Recall   F-Score       AUC\n",
       "0  Logistic Regression_60  0.822488   0.308645  0.654083  0.419391  0.747436\n",
       "0  Logistic Regression_50  0.756419   0.250474  0.745373  0.374951  0.751496\n",
       "0  Logistic Regression_65  0.849290   0.345123  0.598975  0.437920  0.737733\n",
       "0  Logistic Regression_70  0.872208   0.390137  0.539370  0.452774  0.723874\n",
       "0  Logistic Regression_40  0.670681   0.205276  0.821813  0.328499  0.738036\n",
       "0           Decision Tree  0.741014   0.232899  0.715989  0.351471  0.729861"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_metrics = eval('Decision Tree', dt_predictions['Actual'], dt_predictions['Predicted'])\n",
    "metrics = pd.concat([metrics, dt_metrics])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RANDOM FOREST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL TUNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7508971506146712\n",
      "{'rf__max_depth': 5, 'rf__max_features': 2, 'rf__min_samples_leaf': 0.1}\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "# Define steps\n",
    "steps = [('scaler', StandardScaler()),\n",
    "         ('rf', RandomForestClassifier())]\n",
    "\n",
    "# Instantiate pipeline object\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Define parameters\n",
    "max_depth = range(1,11,1)\n",
    "min_samples_leaf = np.linspace(0.1, 0.5, 5, endpoint=True)\n",
    "max_features = np.arange(1,18,1)\n",
    "\n",
    "parameters = dict(rf__max_depth = max_depth, rf__min_samples_leaf = min_samples_leaf, rf__max_features = max_features)\n",
    "\n",
    "model_tuning = GridSearchCV(pipeline, parameters, error_score = 'raise', n_jobs = -1)\n",
    "model_tuning.fit(x_train, y_train)\n",
    "\n",
    "print(model_tuning.best_score_)\n",
    "print(model_tuning.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL BUILDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('rf',\n",
       "                 RandomForestClassifier(max_depth=5, max_features=2,\n",
       "                                        min_samples_leaf=0.1))])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define steps\n",
    "steps = [('scaler', StandardScaler()),\n",
    "         ('rf', RandomForestClassifier(max_depth = 5,\n",
    "                                       max_features = 2,\n",
    "                                       min_samples_leaf = .1))]\n",
    "\n",
    "\n",
    "rf = Pipeline(steps)\n",
    "\n",
    "rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FORECASTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_predictions = pd.DataFrame(rf.predict(x_test))\n",
    "\n",
    "rf_predictions.index = x_test.index\n",
    "\n",
    "rf_predictions = pd.concat([y_test, rf_predictions], axis = 1, ignore_index = True)\n",
    "rf_predictions.columns = ['Actual', 'Predicted']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CDA5Fn3zyCXPmzOHixYsAWK1WTCYTR44cMbJaERERkUIzNBQtWrSI2NhYatasaWQ1IiIi8ldoojVgcCi65557qFq1qpFViIiIyF9VwucCOYqhoSgiIoKQkBB8fHxwcnKyl+vdZyIiIncQhSLA4FD0zjvvEBISQq1atYysRkREROQvMzQUubi4EBkZaWQVIiIi8ldZNacIDA5FTZs2ZcqUKfj5+eHs7Gwvf+yxx4ysVkRERApDw2eAwaHo0KFDeX4FMJlMLFq0yMhqxcH++8tx3oqZS2ZmFmXKODH29UE0angfn69cyxdfxnPl6jUebFCfCSOG4uLiYr8u6fRvPP3CIObHTKLxA/djtVqZ9eEi4jdupVzZsjR56AFeH9QXV1cXLl26zOjJMfxy/AQWq4WuQR35R0+9I09Kt0fCWuPfLxisVq5dvsaaNxeSdOBXACrV8CRy1QRiOkVx6Zxt25MHOjzKM+8M4PzpNPs95nYfx9WsK/h2b4t/3yCczE78tP0gq99ciCUn136ek7MTA5a/yf6vdrL1w6+K9kFF7hCGhqLFixcDkJmZicViwd3d3cjqxACXr1yh77BRjI8ail/LZmzatoOocdMY3O95lq5Yw5IP3qGiWwVejX6LRf+K5aXnngbg6tVrRI1/m+ycHPu9YuM2sGX7bj7/53u4V3Tjg48/ZeaHCxke+TIff/YFri4uxC75gMysLEJ798f3kYd46IEGxfXoIsXK654aBI3syXtBI7mYep6GbZvw3AfDmNxqEI+Gt6HjsKeoVN0zzzV1m97Plvlr+WbO6jzl1e6vTcdhT/Fe8Aguncvk2fciafNiZ7bM+9J+TsiYPnjW8S6SZ5M7kJbkAwa/5uPkyZM89dRTdOjQgSeeeIKwsDASEhKMrFIc7Lvd/6FOrRr4tWwGQLvWLZg+YSRfxm+kT49wKrlXpEyZMowZHklIYHv7dRPffZ+wzk9QudL/gvDhoz/T3u9x3Cu6AfCEfys2fPMtAJZcC1mXLpOTk8vVa9lYLVaczc6IlFY517JZ8caHXEw9D8DJA79S0csDj5pVaNTRl39G3LiK9+6m91O/ZSOGrpvCgGVjqdfM9q7JRk/6cvjrfWSlX8RqtbLz0695NKy1/bpHu7amXMVyHP3m+yJ5NrkDWS2OO0owQ0PRmDFjeOmll9i1axe7d++mb9++jB492sgqxcEST56iqmdlRk+O4ekXBvPy0JHk5uaScDKJ9HPn6fdqNF0jBjBnwVIqutnCzoo18eTk5PJUl0557vVQowZs/nYn585fwGKxsCb+a1LPpgPwQq+nOP1bMu1De/FkeASdnvCn4X33FPnzitwpziWl5QkpIdHPcfjrfZw/fZbF/WNIO/7bDddknbvIzqVfM6NTFOumfU7EvFepVN0Tj5pVOH/mrP28C2fSqVTD1stUvUEdWv+jEytG/NP4hxK5wxk6fHbu3DkCAwPtnzt37szcuXPzPT8jI4OMjIwbyquVNaR58idk5+SwbcdeFsyawsONGrJp2w4GvDaGsq4u7NjzPbOmjsHVxYWRE99h5rxP6NLpCZbFxrFwzrQb7tUlsAPJKWm8MDiK8mXL8lRoJ3tv0MR359Cy2aMM6fc8Z9PP89KQETRp/ABPtmt9w31EShPncq48M70/lWpW4aM+U2557uL+MfbfJ+w9RuK+/3Jfm4cwmUx5VheZTCYsuRbKVizHM+++wmdDZpN9+aphzyAlQDENnz333HOkp6djNtviyPjx48nKymLy5MlcvXqVTp06MWzYMACOHDnCqFGjyMrKwtfXl3HjxmE2mzl9+jTDhw/n7Nmz1KtXj+nTp1OhQgUyMjJ47bXXOHnyJJ6ensyYMQMvL69btsfQniIXF5c8k6wPHjxIuXLl8j1/4cKFdOjQ4YZDio931Srcc3cdHm5k64Zv3+ZxLJZcLl+5yhP+LXGrUAFnZ2eCA9rz46GjrFn3NVmXLtG73//Rrc9AUtLSiRo3jW+27eRCxkWCOrZl1aK5LJ0fw9131eau2jUA+HrLdrqHdqZMmTJ4VfWkY/s27P7P/uJ8dJFi51GzCgNXjsNisTCvxwSuZFzK99yy7uVp90ponjKTyURudi7nTp/FvVple7l7tcpc+C2d+/18KFepAj1nRjI0bjIPPtGUNi92puMwLXIobawWi8OOP12n1UpCQgKrV6+2Hw0aNGDkyJHMmTOHuLg4Dh48yJYtWwAYPnw4Y8aMYf369VitVpYtWwbAuHHj6NmzJ/Hx8TRu3Jg5c+YAMGPGDHx9fVm3bh3du3dn0qRJBbbJkFC0fft2AEaOHMmgQYMIDw+na9euDB48mFGjRuV7XZ8+fdi4ceMNhxSfNi18STr9G4eO/gTA3h8OYMJEvz49WL9pG1euXsVqtbJp6w4aN7yfqKH9+erzf/LFwvf5YuH7eFf1ZMrY12nXpgUHj/yXISMmkJ2TQ05OLh8tXkZQx3YAPHh/feI3bgXg0uUrbN+5l4cbNyy25xYpbq4VytLv89EcjN/Dp4NmkXM1+5bnX828TMuIjjQOtM3/q9nobur43MuxLT9y+Ot9PPhEUypUsc3xa/5sew79ey/7v9rJlNaDmdF5BDM6j+Dw1/vY9lEc/45ZYfjzifz6q20l5QsvvECXLl1YsmQJ+/fvp27dutSpUwez2UxISAjx8fGcOnWKK1eu0KRJEwDCw8OJj48nOzubPXv2EBAQkKccYPPmzYSEhAAQHBzM1q1byc6+9c+RIcNn06dPp1WrVjRp0oT169eTkJCAxWKhXr16eZZs/5G7u/tNV6hlp/1qRDPlT6haxZOZU8Yw8Z33uXz5Ci4uzsx4Kxqfxg9w4WImT78wCEuuhQca1Gf4oJduea9WzZuy94cDhEe8gtViob3f40Q80xWAt0a/xsR33mdN/NeUMZUhsIMfIQHtb3k/kb+zln0CqFzLi8YBvjQO8LWXz+85iUvnM28432qxsvDldwgd14eOw57CkpvL0kEzuXTuIpfOXeTrmSvp92k0TmYnTvzwM5s/WFOUjyN3OgcOn+U3FeaP/x+fkZHB448/zujRo8nOziYiIoKXXnopzxCXt7c3ycnJpKSk5Cn38vIiOTmZc+fO4ebmZh9++70cyHON2WzGzc2N9PR0qlWrlm/bDZ1TBODs7Mx9991ndDViIN8mD/HZhzNuKH/lhV688kKvW1777y8W5vk8pN/zDOn3/A3n1apRjbnTx/+VZor8rXwzZ/UNS+v/6PW7n83zOenAr7wfPvam5+5dvoW9y7fc8n7LXvugcI2Uvw8HrhpbuHAhs2fPvqE8MjKSQYMG2T8/8sgjPPLII/bPTz31FDNnzqRp06b/a5bVapsDZ7HY5sb9ofz3X6/3x8/XX1OmzK0HyAwJRQkJCUREROT7vTZvFBER+Xvq06cPXbt2vaH8jyNBe/fuJTs7m8cffxywhZZatWqRmppqPyc1NRVvb2+qV6+epzwtLQ1vb288PT25ePEiubm5ODk52c8HWy9TWloa1atXJycnh6ysLDw8PG7ZdkNCkZeXl955JiIiUlI4cPgsv6kwf3Tx4kVmzpzJ559/TnZ2NqtWrWLcuHEMHTqUxMREateuzdq1a+nWrRu1atXC1dWVffv20bRpU1avXm1/hZivry9xcXGEhIQQGxuLn58fAP7+/sTGxtK/f3/i4uLw9fXN88qxmzEkFFWoUIFmzZoZcWsRERFxtGJ491m7du348ccfCQsLw2Kx0LNnTx555BGmTJnCoEGDuHr1Kv7+/vatfaZPn050dDSZmZk0atTIPiI1duxYoqKimDt3LjVq1ODdd98FYMiQIURFRREUFETFihWZPn16gW0yWa2OfzVuZGTkTccTb5cmWosUvVG++a8UFRFjTUv4rEjry3rz2YJP+pMqvFm0bXckQ3qKrg9EmZmZXLxo21r+dzVr1jSiWhEREbkdevcZYPDqsw8++ID58+fnmdhkMpm095CIiMidpIS/s8xRDA1FK1as4Ouvv8bT07Pgk0VERESKkaGhqEaNGlSqVMnIKkREROSv0vAZYHAouvvuu+nZsyfNmzfPs5O1luuLiIjcOQrzzrK/M0NDUbVq1W65nbaIiIjIncLQUKQeIRERkRJAw2eAQaGoa9eurFq1ioYNG970XSVHjhwxoloRERG5HQpFgEGhaNWqVQAcPXrUiNuLiIiIOJyhw2fp6emsWbOGrKwsrFYrFouFpKQkpk2bZmS1IiIiUhjapwiAMkbefOjQoRw5coQ1a9Zw+fJl1q9fT5kyhlYpIiIihWWxOu4owQxNKCkpKUydOpX27dvTsWNHlixZwuHDh42sUkREROS2GBqKft+4sV69ehw9epTKlSsbWZ2IiIjcBqvF6rCjJDN0TlGLFi0YPHgwb7zxBi+88AKHDh2ibNmyRlYpIiIihVXCw4yjGBqKhg0bxokTJ6hVqxbvvPMOe/fu1d5FIiIickcyZPjMarWybds29u/fz1133QVA48aNefzxxxkxYoQRVYqIiMjtslgcd5RghvQUvfnmm2zdupUrV64wevRo2rdvz9SpU/niiy8ICwszokoRERG5XRo+AwwKRdu2bWPt2rWkp6czYsQI5s+fT5UqVVi5ciX169c3okoRERGRv8SQUFSxYkUqVKhAhQoV+OWXX+jfvz99+vQxoioRERH5q9RTBBgUiq5/31mVKlUUiERERO5gVqtCERg00fr6UOTs7GxEFSIiIiIOZUhP0ZEjR3jggQcAW/ps2LCh/TuTycSRI0eMqFZERERuh4bPAINC0dGjR424rYiIiBhBoQgwePNGq9XKZ599xs6dO8nJyaFFixb07t1bL4UVERGRO46hoWjatGkkJibSrVs3rFYrK1eu5MSJE0RHRxtZrYiIiBRCSX9nmaMYGoq2b99ObGysvWeobdu2hISEGFmliIiIFJZCEWDQ6rPf5ebmkpOTk+ezk5OTkVWKiIiI3BZDe4pCQkKIiIggKCgIgK+++sr+exEREblDlOxXljmMIaHo9OnTAHTp0oVKlSqxc+dOrFYrISEhtGvXzogqRURE5DZpTpGNIaGod+/emEymG3bI3Lx5MxMnTtQ+RSIiInLHMSQUbdq0Kc/nrKwspk6dyrfffsuECROMqFJERERul3qKAIMnWgPs2LGDLl26ALBmzRpatWpldJUiIiJSGBYHHiWYYROtL126xJQpU+y9QwpDIiIiciczpKdox44d9v2IvvzySwUiERGRO5jVYnXYUZIZ0lP0j3/8A7PZzLfffsv27dvt5VarFZPJxMaNG42oVkRERG5HCR/2chRDQpFCj4iIiJQ0hoSiWrVqGXFbERERMUBJH/ZyFEN3tBYREZESQMNngEKRiIhIqWdVKAKKYJ8iERERkZJAPUUiIiKlnXqKAIUiERGRUk/DZzYaPhMRERFBPUUiIiKiniJAoUhERKTU0/CZjYbPRERERFBPkYiISKmnniIbhSIREZFSTqHIRsNnIiIiIqinSERERKym4m7BHUE9RSIiIqWc1eK443ZMnTqVqKgoAL777jtCQkLo2LEjMTEx9nOOHDlCeHg4AQEBjBo1ipycHABOnz5Nr169CAwMZMCAAWRlZQGQkZFB37596dSpE7169SI1NbXAdigUiYiISLHZsWMHq1atAuDKlSuMHDmSOXPmEBcXx8GDB9myZQsAw4cPZ8yYMaxfvx6r1cqyZcsAGDduHD179iQ+Pp7GjRszZ84cAGbMmIGvry/r1q2je/fuTJo0qcC2KBSJiIiUclaLyWFHYZw/f56YmBj69+8PwP79+6lbty516tTBbDYTEhJCfHw8p06d4sqVKzRp0gSA8PBw4uPjyc7OZs+ePQQEBOQpB9i8eTMhISEABAcHs3XrVrKzs2/ZHs0pEhERKeUcufosIyODjIyMG8rd3d1xd3fPUzZmzBiGDRvGmTNnAEhJScHLy8v+vbe3N8nJyTeUe3l5kZyczLlz53Bzc8NsNucp/+O9zGYzbm5upKenU61atXzbrlAkIiIiDrNw4UJmz559Q3lkZCSDBg2yf16+fDk1atTg8ccfZ+XKlQBYLBZMpv/1NlmtVkwmU77lv/96vT9+vv6aMmVuPUCmUCQiIlLKWR24+qxPnz507dr1hvI/9hLFxcWRmppKaGgoFy5c4NKlS5w6dQonJyf7OampqXh7e1O9evU8E6XT0tLw9vbG09OTixcvkpubi5OTk/18sPUypaWlUb16dXJycsjKysLDw+OWbVcoEhERKeUcOXx2s2Gym/n444/tv1+5ciW7d+9m3LhxdOzYkcTERGrXrs3atWvp1q0btWrVwtXVlX379tG0aVNWr16Nn58fzs7O+Pr6EhcXR0hICLGxsfj5+QHg7+9PbGws/fv3Jy4uDl9fX5ydnW/ZJoUiERERuSO4uroyZcoUBg0axNWrV/H39ycwMBCA6dOnEx0dTWZmJo0aNSIiIgKAsWPHEhUVxdy5c6lRowbvvvsuAEOGDCEqKoqgoCAqVqzI9OnTC6zfZLVarcY9nmNkp/1a3E0QKXVG+Y4q7iaIlFrTEj4r0vpOPtbBYfeqs2ejw+5V1P7UkvzMzEwADh8+TGxsbIFL2kRERKTksFodd5RkBQ6fvffee5w4cYL/+7//46WXXqJ+/frs2bPnT22CJCIiIlJSFNhTtGXLFiZOnMi///1vgoKCWLRoEUePHi2KtomIiEgRKK7NG+80f2r4rFy5cnz33Xe0aNECgGvXrhnaKBERESk6CkU2BYaiypUr8+abb3Lw4EFatmzJ9OnT7XsAiIiIiPxdFBiKpk6dire3N/PmzaNcuXKYTCamTp1aFG0TERGRIqCJ1jb5TrQ+f/687QSzmZ49e9rLXnzxxSJpmIiIiBSNkj7s5Sj5hqIWLVrY3yvyRyaTiSNHjhjaMBEREZGilG8o0gozERGR0sGR7z4ryQqcU2SxWPjoo4+IiooiMzOTefPmkZubWxRtExERkSJgtTjuKMkKDEXTpk3j2LFj/Pjjj1itVrZt28bkyZOLom0iIiIiRabAULRjxw6mTJmCq6srFStWZMGCBWzfvr0o2iYiIiJFwGI1OewoyQp8zYfZbKZMmf9lJxcXF8zmAi8TERGREkJzimwKTDf3338/S5cuJTc3l19//ZVPPvmEhg0bFkXbRERERIpMgcNno0aN4tChQ5w9e5Znn32WrKwsRo4cWRRtExERkSKg13zYFNhT5ObmxltvvVUUbREREZFiUNJ3onaUAnuKzp49y6uvvkrz5s1p3bo1I0eOJCMjoyjaJiIiIlJkCgxF0dHR1KlThxUrVrBkyRIqVarEmDFjiqJtIiIiUgQ0fGZT4PDZqVOnmDt3rv3zG2+8QUhIiKGNEhERkaJT0pfSO0qBPUXe3t6cPHnS/vm3337Dy8vL0EaJiIiIFLV8e4r69+8PQHp6OmFhYbRs2ZIyZcqwa9cuGjRoUGQNFBEREWNpnyKbfENRQEDATcvbtm1rVFtERESkGGj1mU2+oahr1643LbdarSQmJhrWIBEREZHiUOBE688//5xp06Zx+fJle5mnp6fefyYiIvI3oYnWNgWGovnz5/Pxxx8zd+5chg4dyjfffMNvv/1WFG0TERGRIqA5RTYFrj7z8PDAx8eHBx54gLNnzzJgwAD27NlTFG0TERERKTIFhiKz2cyFCxeoW7cu+/fvByA3N9fwhomIiEjRsFodd5RkBQ6fPf300/Tr148PPviAsLAwNmzYwD333FMUbRMREZEioDlFNiarteBcd+nSJcqXL09ycjIHDhygTZs2uLq6FkX7ADC71CqyukTEpqabZ3E3QaTUOpF+oEjr21s7zGH38k2Kddi9ilq+PUUff/xxvhd9+umn/OMf/zCkQSIiIlK0NNHaJt9Q9N///rco2yEiIiLFRMNnNvmGosmTJxdlO0RERESKVYETrUVEROTvrYQvGnMYhSIREZFSTsNnNgpFIiIipZwmWtsUuHmjxWLhn//8J2+88QaZmZnMmzdPmzeKiIjI306BPUXTpk0jPT2dAwdseyZs27aN1NRUoqOjDW+ciIiIGM9S3A24QxTYU7Rjxw6mTJmCq6srbm5uLFiwgO3btxdF20RERKQIWDE57CjJ/tS7z8qU+d9pLi4umM2aiiQiIiJ/LwWmm/vvv5+lS5eSm5vLr7/+yieffELDhg2Lom0iIiJSBCxakw/8iZ6iUaNGcejQIc6ePcuzzz5LVlYWI0eOLIq2iYiISBGwYHLYUZIV2FPk5ubGW2+9VRRtERERESk2BYaiiRMn3rRcq89ERET+Hkr6BGlHKXD4zMPDw35UqFCB3bt3F0W7REREpIhYHHiUZAX2FEVGRub5/PLLLzNgwADDGiQiIiJSHAq9tt7NzY2UlBQj2iIiIiLFQMNnNgWGogkTJmAy2f6wrFYrhw4d4p577jG8YSIiIlI0Svqwl6MUGIoqV66c53OXLl3o0qWLYQ0SERERKQ4FhqITJ04wbdq0omiLiIiIFAP1FNkUGIqOHj2K1Wq1D6GJiIjI34vmFNkUGIq8vLwICgrCx8eHChUq2Mu1T5GIiIj8neS7T9G1a9cAeOSRR+jcuTO1atXKs2eRiIiI/D1YTI47CuO9996jc+fOBAUF8fHHHwPw3XffERISQseOHYmJibGfe+TIEcLDwwkICGDUqFHk5OQAcPr0aXr16kVgYCADBgwgKysLgIyMDPr27UunTp3o1asXqampBbbHZLVab/oauK5du7Jq1arCPZ1BzC61irsJIqVOTTfP4m6CSKl1Iv1Akda3unpPh90r9LdP/9R5u3fvJiYmhsWLF5OTk0Pnzp2ZM2cO/fv3Z/HixdSoUYN+/foRERGBv78/wcHBTJw4kSZNmjBy5EgaN25Mz5496devH126dCEoKIj333+fS5cuMXz4cMaPH0/16tXp27cvsbGxbN68mRkzZtyyTfn2FOWTlURERETylZGRQVJS0g1HRkZGnvOaNWvGokWLMJvNnD17ltzcXDIyMqhbty516tTBbDYTEhJCfHw8p06d4sqVKzRp0gSA8PBw4uPjyc7OZs+ePQQEBOQpB9i8eTMhISEABAcHs3XrVrKzs2/Z9nznFF29epXDhw/nG44aNWr05/50RERE5I7myG6QhQsXMnv27BvKIyMjGTRoUJ4yZ2dnZs6cyYIFCwgMDCQlJQUvLy/7997e3iQnJ99Q7uXlRXJyMufOncPNzQ2z2ZynHMhzjdlsxs3NjfT0dKpVq5Zv2/MNRSdPnmTQoEE3DUUmk4mNGzfme1MREREpORy5JL9Pnz507dr1hnJ3d/ebnj948GBefvll+vfvT0JCQp7V7r+vfrdYLDctv9nq+PxWy1utVsqUufUrX/MNRfXr1yc2NvaWF4uIiIhcz93dPd8AdL1ffvmFa9eu8cADD1CuXDk6duxIfHw8Tk5O9nNSU1Px9vamevXqeSZKp6Wl4e3tjaenJxcvXiQ3NxcnJyf7+WDrZUpLS6N69erk5OSQlZVV4EKxW0cmERER+duzmEwOO/6spKQkoqOjuXbtGteuXWPjxo306NGD48ePk5iYSG5uLmvXrsXPz49atWrh6urKvn37AFi9ejV+fn44Ozvj6+tLXFwcALGxsfj5+QHg7+9v79yJi4vD19cXZ2fnW7Yp354iX1/fP/1gIiIiUnIVx9Iqf39/9u/fT1hYGE5OTnTs2JGgoCA8PT0ZNGgQV69exd/fn8DAQACmT59OdHQ0mZmZNGrUiIiICADGjh1LVFQUc+fOpUaNGrz77rsADBkyhKioKIKCgqhYsSLTp08vsE35Lsm/k2hJvkjR05J8keJT1Evyl9fo5bB7dT+z1GH3KmoF7mgtIiIif29695mNQpGIiEgpV9idqP+uNNFaREREBPUUiYiIlHoW1FUECkUiIiKl3h2/4qqIaPhMREREBPUUiYiIlHqaaG2jUCQiIlLKaUm+jYbPRERERFBPkYiISKmnidY2CkUiIiKlnOYU2Wj4TERERAT1FImIiJR6mmhto1AkIiJSyikU2Wj4TERERAT1FImIiJR6Vk20BhSKRERESj0Nn9lo+ExEREQE9RSJiIiUeuopslEoEhERKeW0o7WNhs9EREREUE+RiIhIqafXfNgoFImIiJRymlNko+EzEREREdRTJCIiUuqpp8hGoUhERKSU0+ozGw2fiYiIiKCeIhERkVJPq89sFIpERERKOc0psjF0+Cw9Pd3I24uIiIgDWB14lGSG9hT17t0bd3d3/P39adeuHQ0bNjSyOhEREZHbZmgoiouLIykpia1bt/Lee++RkJBA8+bNefPNN42sVkRERArBUuL7eBzD0FBksVg4d+4cly9fxmq1kpOToyE1ERGRO4zmFNkYGooee+wxypUrR8+ePRk6dKiGz0REROSOZWgoeu+999i5cyfbtm1j+/bt+Pr60qxZM1q1amVktSIiIlIIGjyzMTQUtW7dmtatW5ORkcGGDRuYN28eixYt4vvvvzeyWhERESkEDZ/ZGBqKpk+fzo4dO8jMzKRNmzaMHj2a5s2bG1mliIiIyG0xNBRVqVKF6dOnU69ePSOrERERkb9AO1rbGLp5Y5cuXZgxYwbNmzfH19eXgQMHkpaWZmSVIiIiUkgWrA47SjJDQ9HYsWN5+OGH2bhxI5s2baJJkyaMGjXKyCpFREREbouhoejkyZO8+OKLuLm54e7uzssvv8zp06eNrFJEREQKSa/5sDE0FJlMJs6cOWP/fPr0acxmvYNWRETkTmJx4FGSGZpQhgwZwjPPPIOPjw9Wq5Uff/yRCRMmGFmliIiIyG0xNBTVqFGD2NhY9u/fj8ViYdy4cVSpUsXIKkVERKSQSvoEaUcxNBQNGzaMdevW0bZtWyOrERERkb9AkcjG0FBUv359Zs+ejY+PD2XLlrWXP/bYY0ZWKyIiIlJohoai8+fPs2vXLnbt2mUvM5lMLFq0yMhqRUREpBBK+gRpRzE0FAUFBdGjRw8jqxAREZG/SHOKbAxdkr9kyRIjby8iIiLiMIb2FFWvXp2IiAh8fHxwdXW1l0dGRhpZrYiIiBSC+olsDO0patKkCc2aNcsTiEREROTOUlybN86ePZugoCCCgoKYNm0aAN999x0hISF07NiRmJgY+7lHjhwhPDycgIAARo0aRU5ODmDbGLpXr14EBgYyYMAAsrKyAMjIyKBv37506tSJXr16kZqaWmB7DA1FkZGR9OzZk0aNGtGwYUN69OihXiIRERHhu+++49tvv2XVqlXExsZy6NAh1q5dy8iRI5kzZw5xcXEcPHiQLVu2ADB8+HDGjBnD+vXrsVqtLFu2DIBx48bRs2dP4uPjady4MXPmzAFgxowZ+Pr6sm7dOrp3786kSZMKbJOhoWjbtm2EhoaycuVKVq1aRZcuXfjmm2+MrFJEREQKyerAf/4sLy8voqKicHFxwdnZmXvvvZeEhATq1q1LnTp1MJvNhISEEB8fz6lTp7hy5QpNmjQBIDw8nPj4eLKzs9mzZw8BAQF5ygE2b95MSEgIAMHBwWzdupXs7OxbtsnQOUUxMTF8+umn1KlTB7C9IDYyMpJ27doZWa2IiIgUgiOX5GdkZJCRkXFDubu7O+7u7vbP9913n/33CQkJrFu3jt69e+Pl5WUv9/b2Jjk5mZSUlDzlXl5eJCcnc+7cOdzc3OzvVf29HMhzjdlsxs3NjfT0dKpVq5Zv2w0NRTk5OfZABFCnTh0sFu2GICIi8ne1cOFCZs+efUN5ZGQkgwYNuqH8p59+ol+/frz++us4OTmRkJBg/85qtWIymbBYLJhMphvKf//1en/8fP01ZcrceoDM0FBUs2ZNPvnkE5566ikAVqxYQa1atYysUkRERArJkfsU9enTh65du95Qfn0v0e/27dvH4MGDGTlyJEFBQezevTvPhOjU1FS8vb2pXr16nvK0tDS8vb3x9PTk4sWL5Obm4uTkZD8fbL1MaWlpVK9enZycHLKysvDw8Lhl2w2dUzRp0iR++OEHnnjiCTp06MD333/P+PHjjaxSRERECsnqwMPd3Z3atWvfcPwxFJ05c4aBAwcyffp0goKCAPDx8eH48eMkJiaSm5vL2rVr8fPzo1atWri6urJv3z4AVq9ejZ+fH87Ozvj6+hIXFwdAbGwsfn5+APj7+xMbGwtAXFwcvr6+ODs73/LPwWS1Wu/47QnMLupdEilqNd08i7sJIqXWifQDRVrfgLufdti95iYs+1PnTZw4kS+++IK77rrLXtajRw/uvvtuJk+ezNWrV/H392fEiBGYTCaOHj1KdHQ0mZmZNGrUiMmTJ+Pi4sKpU6eIiori7Nmz1KhRg3fffZdKlSpx/vx5oqKiOHnyJBUrVmT69OnUrl37lm0yNBTFx8czf/58Lly4kKd848aNhbqPQpFI0VMoEik+RR2K+t3d3WH3mpew3GH3KmqGzimaOnUq06ZNo2bNmkZWI0WoZ89w/u/VAVitVi5fuszQYaP56efjfDj/HRo0uJcyZcqwePFy3p4+J891d99dh90719Gpc0/2/Wc/AOPHvU73p7qQdekSO3bs5bXh47h69WpxPJbIHa/bMyG89EqE/XNFdzdq1KyGX9Mghr4xgCaPNsZkMvH9vgNED5/E1Sv/+1l6ulcYgUEdeKHnjZNcX+zfmx7PhfNkq/AieQ65M2kJlI2hoeiuu+6iadOmBc72lpLh/vvvZerkaB5rHshvv6XQKbA9y5f9k9Vr4klKOsMzPfpSvnw59v/wDdu27WLnLtvYr6urK4s+mYWLi4v9Xn0inqZz5ydo0bIzFy5kMGrkUCaMe53XoyYU1+OJ3NG++NeXfPGvLwHb8uLlX33C3BkLeDbiKcxOTnRs3Q2TycR78yYzcNhLvDv5fSp5uPPG6CGEPRXEru/23nBP3+ZN6D/oH5w/f+GG70RKI0ND0QsvvEBERASPPfYYTk5O9nLtal0yXb16lX79h/PbbykA7N33I9Wre/FG1ERyc3MBqFGjGq6uLly4bo+KWTMnsXDRMkZEDbaXPfrow6xZE8+FC7bzVsXGsSZ2kUKRyJ8wYMgLnE1NZ+nC5bRp9zhJJ05jtVqxWq0c2n+U+xveC0BIWADJv6Uyacw7PBHon+ceVb2qMH7qSCaNfZeBw14sjseQO0hhNl38OzO0C2fu3LnUqVMnTyC6lYyMDJKSkm445M6QmJhE3Lr/zQeb/vZYvly7gWvXrpGbm8vCT2by4/cb2bJ1B8eO/QLAC/94FmdnZz5a8Gmee+3e8z3BwR2pUqUyJpOJ53o/RY0a3kX6PCIlUWVPD/oO7MP4Ubb3RG37ZgfHf0kEoFbtGrzYvzdfrf43AEs+Wc57b3/A1WvX8tyjTJkyzJo/hclvxvDbmeSifQC5IxXXu8/uNIb2FGVnZzN58uQ/fX5+Gz7JnaV8+XIs+GgGdWrXpHNwL3t5n+cH88rAKJb/60NGRw9jzZr19O37HO3a3zhXYenSL6hdqwYb/r2MS1mX+fCfS7h27dbbr4sI9OzzFP+O+4YTiXn/g/EhnweZv3gGn/zzMzb+e+st7xE1Zgi7duxj2+YdtGjla2RzRUoUQ0NRq1atWLJkCW3atMmzN0B+E6/z2/Dp7nuaG9ZGKZw6dWoSu2ohR4/+RIcnu3PlyhU6PunPgYNHOXMmmaysS3z+r9WEh3f+/1u6V2Tb1jUA1KxZjUWLZvNG1AS2b9/DZ5+vYuo0Wwh+vIUvP/+SUIxPJlIyhHQNZGzUlLxl4YFMejua0a+/xeov4gq8R/jTIaSlpRMQ1IEKFcpTvYY367Ysp5O/41YgScmi4TMbQ0PR2rVrAViwYIG9zGQy5bsk/4/vRZE7i5tbBTZuWMHiJcuZMDHGXv7UUyGEhXXmlYFv4OLiQvengvl64zbem/kh//faWPt5P/93JxERkez7z36efMKPSZNG0rJVMFarlddfH8hnn60sjscSKTEqVXLn7np12Lf7B3vZEwH+jJscRe9ufdn/w+E/dR/fB9vbf9+ilS8Tpo1UICrlSvqwl6MYGoo2bdpk5O2liA185R/UrVub0NBOhIZ2spd3DHiGWTMn8cP3trC7enU8M2f985b32vD1Vvz8Huf7/3xNmTJlWLMmnhnvfWho+0VKurr31CElOY2cnBx72ajx/4fJZGLqe+PsZXt3/cDo1ycVRxNFSjRDN29MT09n/Pjx7Nixg9zcXFq0aMGbb75J1apVC3Ufbd4oUvS0eaNI8SnqzRufq+u4faoWJ5bcXn9DV5+NGTOGhx56iI0bN7Jp0yZ8fHwYNWqUkVWKiIhIITny3WclmaGh6OTJk7z44ou4ubnh7u7Oyy+/zOnTp42sUkREROS2GBqKTCYTZ86csX8+ffo0ZrOh05hERESkkCxYHXaUZIYklJ9++on77ruPIUOG8Mwzz+Dj44PVauXHH39kwgTtWCwiInIn0ZJ8G0MmWnft2pVVq1YBtsnW+/fvx2Kx4OPjQ5UqVQp9P020Fil6mmgtUnyKeqL1s3XDHHavzxJjHXavomZIT9H1OcvT05O2bdsaUY2IiIg4gPYpsjEkFJ05c4YRI0bk+31hXv0hIiIixirpc4EcxZBQVL58eZo1a2bErUVEREQMYUgo8vDwuOk7zEREROTOo4nWNoaEoutf/ioiIiJ3Ns0psjFkn6Jly5YZcVsRERERwxi6k+LKlSuZOnUqGRkZgG1Vmslk4siRI0ZWKyIiIoVg4GtQSxRDQ9GcOXNYvHgx999/v5HViIiIyF+g1Wc2hr7mw9vbW4FIRERESgRDe4oaNWrE4MGDadWqFa6urvbysLAwI6sVERGRQtBEaxtDQ1FmZiYVKlTghx9+yFOuUCQiInLn0JJ8G0NDkXauFhERufNpTpGNIaGoX79+zJs3j/bt22MymW74fuPGjUZUKyIiInLbDAlFEyZMAGDx4sVG3F5EREQcSEvybQwJRd7e3gB4eXmxZcsWsrKyAMjNzSUpKYkhQ4YYUa2IiIjcBk20tjF0TtGrr77KhQsXOHHiBL6+vuzatYtHH33UyCpFREREbouh+xQdO3aMRYsW8eSTT/LSSy/x2WefcerUKSOrFBERkUKyOvCfkszQUFSlShVMJhP16tXj2LFj1KlTh2vXrhlZpYiIiBSSBavDjpLM0OGz++67jwkTJvDss8/y2muvkZKSctPVaCIiIiLFzbBQ9OuvvxIZGcnJkyepX78+gwcPZvPmzVSuXNmoKkVEROQ2aPWZjSHDZ7NmzaJbt24EBgZy9epVwBaS1q5di9lsaOeUiIiIFJKGz2wMSSixsbGsX7+elJQUZs6cyccff0xycjLvvfcebdq0MaJKERERkb/EkFBUoUIFvL298fb2Zv/+/YSFhTFv3jycnJyMqE5ERET+gpK+asxRDAlFZcr8b1SucuXKREVFGVGNiIiIOIBFc4oAg+YUXb/CrGzZskZUISIiIuJQhvQU/fTTT3To0AGA5ORk+++tVismk0kvhBUREbmDqJ/IxpBQtH79eiNuKyIiIgYo6avGHMWQUFSrVi0jbisiIiJiGG0aJCIiUsqpp8hGoUhERKSU047WNoa+EFZERESkpFBPkYiISCmn4TMbhSIREZFSTjta22j4TERERAT1FImIiJR6mmhto1AkIiJSymlOkY2Gz0RERKTYZGZmEhwcTFJSEgDfffcdISEhdOzYkZiYGPt5R44cITw8nICAAEaNGkVOTg4Ap0+fplevXgQGBjJgwACysrIAyMjIoG/fvnTq1IlevXqRmppaYFsUikREREo5q9XqsKMwfvzxR5599lkSEhIAuHLlCiNHjmTOnDnExcVx8OBBtmzZAsDw4cMZM2YM69evx2q1smzZMgDGjRtHz549iY+Pp3HjxsyZMweAGTNm4Ovry7p16+jevTuTJk0qsD0KRSIiIqWcBavDjsJYtmwZY8eOxdvbG4D9+/dTt25d6tSpg9lsJiQkhPj4eE6dOsWVK1do0qQJAOHh4cTHx5Odnc2ePXsICAjIUw6wefNmQkJCAAgODmbr1q1kZ2ffsj2aUyQiIiIOk5GRQUZGxg3l7u7uuLu75yn7Y+9NSkoKXl5e9s/e3t4kJyffUO7l5UVycjLnzp3Dzc0Ns9mcp/yP9zKbzbi5uZGenk61atXybbtCkYiISCnnyH2KFi5cyOzZs28oj4yMZNCgQbe81mKxYDKZ/tcuqxWTyZRv+e+/Xu+Pn6+/pkyZWw+QKRSJiIiUchYHLsnv06cPXbt2vaH8j71EN1O9evU8E6JTU1Px9va+oTwtLQ1vb288PT25ePEiubm5ODk52c8HWy9TWloa1atXJycnh6ysLDw8PG5Zv+YUiYiIiMO4u7tTu3btG44/E4p8fHw4fvw4iYmJ5ObmsnbtWvz8/KhVqxaurq7s27cPgNWrV+Pn54ezszO+vr7ExcUBEBsbi5+fHwD+/v7ExsYCEBcXh6+vL87Ozres32QtATs2mV1qFXcTREqdmm6exd0EkVLrRPqBIq2vUbXmDrvXoeRdhb6mffv2LFq0iNq1a7Njxw4mT57M1atX8ff3Z8SIEZhMJo4ePUp0dDSZmZk0atSIyZMn4+LiwqlTp4iKiuLs2bPUqFGDd999l0qVKnH+/HmioqI4efIkFStWZPr06dSuXfuW7VAoEpGbUigSKT5FHYoe8G7msHsdSdntsHsVNQ2fiYiIiKCJ1iIiIqWeI1eflWQKRSIiIqWcI1eflWQaPhMRERFBPUUiIiKlnobPbBSKRERESjkNn9lo+ExEREQE9RSJiIiUeho+s1EoEhERKeWsVktxN+GOoOEzEREREdRTJCIiUupZNHwGKBSJiIiUeiXgNahFQsNnIiIiIqinSEREpNTT8JmNQpGIiEgpp+EzGw2fiYiIiKCeIhERkVJPr/mwUSgSEREp5bSjtY2Gz0RERERQT5GIiEipp4nWNgpFIiIipZyW5NsoFImIiJRy6imy0ZwiEREREdRTJCIiUuppSb6NQpGIiEgpp+EzGw2fiYiIiKCeIhERkVJPq89sFIpERERKOQ2f2Wj4TERERAT1FImIiJR6Wn1mo1AkIiJSyumFsDYaPhMRERFBPUUiIiKlnobPbBSKRERESjmtPrPR8JmIiIgI6ikSEREp9TTR2kahSEREpJTT8JmNhs9EREREUE+RiIhIqaeeIhuFIhERkVJOkcjGZFU8FINkZGSwcOFC+vTpg7u7e3E3R6RU0c+fSOFpTpEYJiMjg9mzZ5ORkVHcTREpdfTzJ1J4CkUiIiIiKBSJiIiIAApFIiIiIoBCkYiIiAigUCQGcnd3JzIyUitfRIqBfv5ECk9L8kVERERQT5GIiIgIoFAkIiIiAug1HyVWUlISHTp0YMGCBbRq1cpe3r59exYtWkTt2rVvel2DBg1o2LAhYHvXzcWLF2nTpg1jx47Fyckp3/pCQ0NZvXp1vt8PHDiQpKQkLl26RFpaGnfddRcAr732Gm3atLmdRxQpkZKSkggMDOTee+8FwGKxkJWVRVhYGIMHD77pNcnJyURHR/Phhx/me9/u3btz7do1Lly4wKVLl6hRowYA06ZNo0GDBo5/EJFSSHOKSqjf/8Xr7e3NmjVrcHNzA/5cKDp27Jj9c2ZmJsHBwYwbNw5/f/+/3K5du3Yxe/ZsFi9e/JfvJVISJSUlERERwaZNm+xlycnJBAQE8MUXX9jD0u1auXIlu3fvZsqUKX+1qSLyB+opKsG8vb1p2bIlU6dOZcKECXm+++CDD1izZg1OTk60atWK4cOH37Qn6Ny5c1y+fBkPDw8AYmJi2LFjBxcuXMDb25uYmBiqVq1qD1OzZs0iOTmZxMRETp06Rffu3RkwYEC+bfzhhx+YNGkSV69epXLlyowfP566devy3HPP8eCDD7Jv3z6uXr3Ka6+9xqJFi/jll194/vnnef7557l8+TLR0dEcO3YMk8nEiy++SFhYGCtXrmTVqlWcP3+edu3akZKSwvnz50lMTGT48OF4enreUOfRo0dZt24dM2bM4Pjx4wQGBrJ9+3aqVq3Kiy++yJAhQ3j44Ycd+vcj8rvU1FSsVisVKlQgOjqan376ibS0NBo0aMC7775LWlqaPUhFRUXh5ubGoUOHSE5OZuDAgXTr1i3fe3/zzTfMmDEDi8VCnTp1GD9+PFWrVqV9+/YEBQWxfft2zGYzr7zyCgsWLCAxMZE33niDzp07k5aWxqhRozh9+jRms5lhw4bh5+fHrFmz+OGHHzhz5gy9e/dm3bp1VKpUiZ9++okZM2bw22+/3VDnmjVrOHv2LMOHD+fbb79l8ODB7N69G7PZTKdOnVi8eDFVq1Ytwj91kcLTnKISLioqim+//Zbt27fby7Zu3cqmTZv44osvWLVqFYmJiXz++ef270NDQwkKCqJFixZERUURHR2Nj48PiYmJ/Prrr3z++eesX7+eGjVqsGbNmhvqPHbsGB999BHLly9n/vz5+b5b6dq1a7z66quMHj2aNWvW0KNHD1599VX791arlRUrVhAQEMDEiROZPXs2S5cu5f333wdg1qxZVK5cmbVr17Jw4UJmzZrF0aNHAdt/ea9atcp+Pw8PD9atW0fr1q1vWmerVq3Yt28fVquVnTt3UqVKFXbv3s2VK1c4fvw4Dz300F//yxD5/1JSUggNDSUwMJDmzZszY8YMZs+ezcmTJ3F2duZf//oXGzZs4OLFi2zZsuWG63/77Tc+/fRT5s6dy7Rp0/Kt5+zZs4wZM4b333+fL7/8kkcffZTx48fbv69atSorV67k3nvvZf78+SxYsIC3336b+fPnAzBhwgRatGjBl19+ycyZMxk5ciRpaWmA7ec3Li6Onj17ArZe5vXr1+Pt7X3TOv39/dmxYwcAO3fupGzZshw+fJiTJ09SsWJFBSIpERSKSjg3NzcmTJjA6NGjyczMBGz/QgoKCqJcuXKYzWa6detm/5cVwOrVq/nqq6/o378/Fy9epEOHDgDUrVuXN954g+XLlzNlyhR++OEHLl26dEOdzZs3x8XFhSpVquDh4cHFixdv2raEhATc3d3tPTCdOnXixIkT9vP9/PwAqFmzJj4+PpQrV45atWrZQ9bOnTt56qmnAPD09KRDhw7s3r0bgAcffBCz+X8dnb/XkV+dVquVevXqcezYMXbu3EmfPn3Ys2cPu3btokWLFphMptv54xe5KW9vb1avXk1cXByhoaFYrVZatWrFY489Rs+ePVm6dCmTJk0iISHhpj9jrVq1wmQycf/993P+/Pl869m/fz8PP/ywfbj8mWeeYefOnfbvr/8Ze+yxxzCbzdSsWfOmP2N16tTBx8eHH3/8EeCGntPfP+dX57333ktmZiYXLlxg79699OzZk927d7N161aHDM2LFAWFor+B1q1b24fRwDax849ycnJuKHv++efx8vKy/5fowYMHefHFF7FYLAQEBPDEE09wsylnrq6u9t+bTKabnpNfO6xWK7m5uQA4Ozvby68PONefm9+1ZcuWzfPd759vVWfbtm3Zvn07v/76K08//TR79+5l69attGvX7qbtF/mrypQpw+uvv05ycjIfffQRGzdu5LXXXqNs2bKEh4fz2GOP3fJnrKCw/sf/vVut1jw/60XxM3Z9nW3atGHDhg2YTCbat2/Pnj172LZtm37GpMRQKPqb+H0YLSUlhRYtWvDVV19x5coVcnJy+OKLL2jRokW+161YsYKjR4+yZ88emjVrxrPPPsvdd9/N5s2b7f+CvB333HMP58+fZ//+/QDExcVRs2ZN+/ylgrRo0YIVK1YAkJ6ezsaNG2nWrNlt1+nv78/nn39O/fr1qVy5Ms7OznzzzTe0bNnytp9RpCBms5nXX3+dOXPmsHnzZjp16kS3bt1wd3dn165df+ln7PeenaSkJAD+9a9/0bx58z99/fU/YydPnuQ///kPTZo0ue06/f39mTdvHk2bNuWBBx7gl19+4fjx4zz44IO38XQiRU8Trf8mfh9Ge/HFF2nbti0ZGRl069aNnJwcWrduTe/evW963X333UdYWBhTp05lypQpREZGEhISAkDjxo3t/+K7HS4uLsTExDBhwgQuX75MpUqViImJ+dPXDxw4kDfffJOQkBByc3Pp378/jRo1yrN6rjB13nvvvVitVnuwatasGT/99BMVKlS47WcU+TP8/Px45JFHSEhI4IcffuCrr77C2dmZRx999C/9jFWtWpXx48cTGRlJdnY2NWvWZNKkSX/6+lGjRjFmzBhWrlwJwMSJE/H29r7tOps3b05qairNmjXDZDLxwAMPULly5dt+PpGipiX5IiIiImj4TERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIpcklJSTzwwAOEhobajy5dutj3i/kr+vXrZ19eHRoamu8rWAAuXrxIREREoeuIj4/nueeeu6F8165dBAcHF3h9gwYNSE9PL1SdUVFRfPTRR4W6RkSksLRPkUgxKFu2LKtXr7Z/Tk5OJjg4mMaNG9OwYUOH1HH9/W/mwoULHDhwwCF1iYj8HSgUidwBqlWrRt26dUlISODw4cOsWLGCy5cv4+bmxuLFi1m+fDmfffYZFosFDw8PRo8ezb333ktycjJRUVGkpKRQs2ZNzp49a79ngwYN2LFjB56ensybN49Vq1ZhNpupW7cuU6ZMYcSIEVy5coXQ0FBWrlxJQkICkyZN4vz58+Tm5vLcc8/Z34v13nvv8eWXX+Lh4UHdunULfJ7jx48zfvx4srKySE1NpWHDhsyYMcP++ooZM2Zw4MABLBYLQ4cOtb8GIr/nvN7MmTPZsGEDzs7OVK5cmcmTJxe44aCIyJ+hUCRyB/j+++85ceIEPj4+7Nixg59//plNmzbh5ubG7t27iY2NZenSpZQrV45vv/2WyMhI1q1bx/jx4/Hx8WHo0KEkJiYSFhZ2w703btzIypUrWbZsGZUqVWLy5MksWbKEyZMnExISwurVq8nJyWHw4MFMmzaNRo0acfHiRZ555hnq169PWloa//73v4mNjaVs2bIMHDiwwOdZtmwZYWFhhIaGkp2dTXh4OJs3byYgIACA2rVrM378eP773//y3HPPsW7dOn7++ed8n/N3Z86cYeHChezYsQMXFxcWLFjA/v37eeKJJxz2dyEipZdCkUgx+L2HBiA3N5fKlSvz9ttvU6NGDcDWy+Pm5gbA5s2bSUxMpEePHvbrMzIyOH/+PN999x1vvPEGAHXr1r3pe6927NhBYGAglSpVAmDEiBEAeV4vkZCQwIkTJxg5cmSeNh4+fJhffvmFJ5980t6ebt26sXjx4ls+3/Dhw9m+fTsffvghCQkJpKSk5Hkb/LPPPgvA/fffz7333sv333/Pvn378n3O31WrVo2GDRvStWtX/Pz88PPz4/HHH79lW0RE/iyFIpFi8Mc5RX9Uvnx5++8tFguhoaEMHz7c/jklJYVKlSphMpnyvOn8Zm9Cd3JyyvO29YyMjBsmYOfm5lKxYsU8bUpLS6NixYpMmzYtTx1OTk4FPt+rr75Kbm4unTp1om3btpw5cybPPcqU+d8aD4vFgtlsvuVzXn/dkiVLOHDgADt27OCtt96iTZs2vP766wW2SUSkIFp9JnKHa926NV999RUpKSkAfPbZZ/Tp0weANm3a8K9//QuA06dPs2vXrhuub9myJRs2bCAzMxOAWbNm8cknn2A2m8nNzcVqtVKvXr08Qe3MmTMEBwdz8OBB/Pz8iI+PJyMjA4vFUuAEboBvv/2WgQMH0rlzZwB+/PHHPG+DX7VqFQCHDh2yDxve6jl/d/ToUYKDg7n33nvp168fzz//vCaLi4jDqKdI5A7XunVrXn75ZV544QVMJhNubm7Mnj0bk8nE2LFjGTFiBJ06daJ69eo3Xbnm7+/Pzz//bB+yql+/PhMmTKBcuXI8/PDDBAUFsXTpUubMmcOkSZP45z//SU5ODkOGDKFp06YAHDt2jG7duuHu7k7Dhg05d+7cLds8bNgwBg4cSPny5XFzc+Oxxx7jxIkT9u9PnjxJWFgYJpOJd999Fw8Pj1s+5+8aNmxIp06d6NatG+XLl6ds2bJER0c74o9ZRAST9fo+bREREZFSSsNnIiIiIigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiAAKRSIiIiKAQpGIiIgIAP8P5AIDgzSL5bcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(rf_predictions['Actual'], rf_predictions['Predicted'])\n",
    "\n",
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']); \n",
    "ax.yaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EVALUATION METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_60</td>\n",
       "      <td>0.822488</td>\n",
       "      <td>0.308645</td>\n",
       "      <td>0.654083</td>\n",
       "      <td>0.419391</td>\n",
       "      <td>0.747436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_50</td>\n",
       "      <td>0.756419</td>\n",
       "      <td>0.250474</td>\n",
       "      <td>0.745373</td>\n",
       "      <td>0.374951</td>\n",
       "      <td>0.751496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_65</td>\n",
       "      <td>0.849290</td>\n",
       "      <td>0.345123</td>\n",
       "      <td>0.598975</td>\n",
       "      <td>0.437920</td>\n",
       "      <td>0.737733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_70</td>\n",
       "      <td>0.872208</td>\n",
       "      <td>0.390137</td>\n",
       "      <td>0.539370</td>\n",
       "      <td>0.452774</td>\n",
       "      <td>0.723874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_40</td>\n",
       "      <td>0.670681</td>\n",
       "      <td>0.205276</td>\n",
       "      <td>0.821813</td>\n",
       "      <td>0.328499</td>\n",
       "      <td>0.738036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.741014</td>\n",
       "      <td>0.232899</td>\n",
       "      <td>0.715989</td>\n",
       "      <td>0.351471</td>\n",
       "      <td>0.729861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.755517</td>\n",
       "      <td>0.251201</td>\n",
       "      <td>0.754366</td>\n",
       "      <td>0.376897</td>\n",
       "      <td>0.755004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Accuracy  Precision    Recall   F-Score       AUC\n",
       "0    Logistic Regression_60  0.822488   0.308645  0.654083  0.419391  0.747436\n",
       "0    Logistic Regression_50  0.756419   0.250474  0.745373  0.374951  0.751496\n",
       "0    Logistic Regression_65  0.849290   0.345123  0.598975  0.437920  0.737733\n",
       "0    Logistic Regression_70  0.872208   0.390137  0.539370  0.452774  0.723874\n",
       "0    Logistic Regression_40  0.670681   0.205276  0.821813  0.328499  0.738036\n",
       "0             Decision Tree  0.741014   0.232899  0.715989  0.351471  0.729861\n",
       "0  Random Forest Classifier  0.755517   0.251201  0.754366  0.376897  0.755004"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_metrics = eval('Random Forest Classifier', rf_predictions['Actual'], rf_predictions['Predicted'])\n",
    "metrics = pd.concat([metrics, rf_metrics])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SUPPORT VECTOR MACHINE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL BUILDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()), ('svm', SVC())])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define steps\n",
    "steps = [('scaler', StandardScaler()),\n",
    "         ('svm', SVC())]\n",
    "\n",
    "\n",
    "svm = Pipeline(steps)\n",
    "\n",
    "svm.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FORECASTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_predictions = pd.DataFrame(svm.predict(x_test))\n",
    "\n",
    "svm_predictions.index = x_test.index\n",
    "\n",
    "svm_predictions = pd.concat([y_test, svm_predictions], axis = 1, ignore_index = True)\n",
    "svm_predictions.columns = ['Actual', 'Predicted']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(svm_predictions['Actual'], svm_predictions['Predicted'])\n",
    "\n",
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']); \n",
    "ax.yaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EVALUATION METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_60</td>\n",
       "      <td>0.822488</td>\n",
       "      <td>0.308645</td>\n",
       "      <td>0.654083</td>\n",
       "      <td>0.419391</td>\n",
       "      <td>0.747436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_50</td>\n",
       "      <td>0.756419</td>\n",
       "      <td>0.250474</td>\n",
       "      <td>0.745373</td>\n",
       "      <td>0.374951</td>\n",
       "      <td>0.751496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_65</td>\n",
       "      <td>0.849290</td>\n",
       "      <td>0.345123</td>\n",
       "      <td>0.598975</td>\n",
       "      <td>0.437920</td>\n",
       "      <td>0.737733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_70</td>\n",
       "      <td>0.872208</td>\n",
       "      <td>0.390137</td>\n",
       "      <td>0.539370</td>\n",
       "      <td>0.452774</td>\n",
       "      <td>0.723874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_40</td>\n",
       "      <td>0.670681</td>\n",
       "      <td>0.205276</td>\n",
       "      <td>0.821813</td>\n",
       "      <td>0.328499</td>\n",
       "      <td>0.738036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.741014</td>\n",
       "      <td>0.232899</td>\n",
       "      <td>0.715989</td>\n",
       "      <td>0.351471</td>\n",
       "      <td>0.729861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.755517</td>\n",
       "      <td>0.251201</td>\n",
       "      <td>0.754366</td>\n",
       "      <td>0.376897</td>\n",
       "      <td>0.755004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.791524</td>\n",
       "      <td>0.289372</td>\n",
       "      <td>0.774129</td>\n",
       "      <td>0.421271</td>\n",
       "      <td>0.783772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Accuracy  Precision    Recall   F-Score       AUC\n",
       "0    Logistic Regression_60  0.822488   0.308645  0.654083  0.419391  0.747436\n",
       "0    Logistic Regression_50  0.756419   0.250474  0.745373  0.374951  0.751496\n",
       "0    Logistic Regression_65  0.849290   0.345123  0.598975  0.437920  0.737733\n",
       "0    Logistic Regression_70  0.872208   0.390137  0.539370  0.452774  0.723874\n",
       "0    Logistic Regression_40  0.670681   0.205276  0.821813  0.328499  0.738036\n",
       "0             Decision Tree  0.741014   0.232899  0.715989  0.351471  0.729861\n",
       "0  Random Forest Classifier  0.755517   0.251201  0.754366  0.376897  0.755004\n",
       "0    Support Vector Machine  0.791524   0.289372  0.774129  0.421271  0.783772"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_metrics = eval('Support Vector Machine', svm_predictions['Actual'], svm_predictions['Predicted'])\n",
    "metrics = pd.concat([metrics, svm_metrics])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LGBM CLASSIFIER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL BUILDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()), ('lgbm', LGBMClassifier())])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define steps\n",
    "steps = [('scaler', StandardScaler()),\n",
    "         ('lgbm', LGBMClassifier())]\n",
    "\n",
    "\n",
    "lgbm = Pipeline(steps)\n",
    "\n",
    "lgbm.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FORECASTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_predictions = pd.DataFrame(lgbm.predict(x_test))\n",
    "\n",
    "lgbm_predictions.index = x_test.index\n",
    "\n",
    "lgbm_predictions = pd.concat([y_test, lgbm_predictions], axis = 1, ignore_index = True)\n",
    "lgbm_predictions.columns = ['Actual', 'Predicted']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(lgbm_predictions['Actual'], lgbm_predictions['Predicted'])\n",
    "\n",
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']); \n",
    "ax.yaxis.set_ticklabels(['NoRainTomorrow', 'RainTomorrow']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EVALUATION METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_60</td>\n",
       "      <td>0.822488</td>\n",
       "      <td>0.308645</td>\n",
       "      <td>0.654083</td>\n",
       "      <td>0.419391</td>\n",
       "      <td>0.747436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_50</td>\n",
       "      <td>0.756419</td>\n",
       "      <td>0.250474</td>\n",
       "      <td>0.745373</td>\n",
       "      <td>0.374951</td>\n",
       "      <td>0.751496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_65</td>\n",
       "      <td>0.849290</td>\n",
       "      <td>0.345123</td>\n",
       "      <td>0.598975</td>\n",
       "      <td>0.437920</td>\n",
       "      <td>0.737733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_70</td>\n",
       "      <td>0.872208</td>\n",
       "      <td>0.390137</td>\n",
       "      <td>0.539370</td>\n",
       "      <td>0.452774</td>\n",
       "      <td>0.723874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression_40</td>\n",
       "      <td>0.670681</td>\n",
       "      <td>0.205276</td>\n",
       "      <td>0.821813</td>\n",
       "      <td>0.328499</td>\n",
       "      <td>0.738036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.741014</td>\n",
       "      <td>0.232899</td>\n",
       "      <td>0.715989</td>\n",
       "      <td>0.351471</td>\n",
       "      <td>0.729861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.755517</td>\n",
       "      <td>0.251201</td>\n",
       "      <td>0.754366</td>\n",
       "      <td>0.376897</td>\n",
       "      <td>0.755004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.791524</td>\n",
       "      <td>0.289372</td>\n",
       "      <td>0.774129</td>\n",
       "      <td>0.421271</td>\n",
       "      <td>0.783772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LGBM Classifier</td>\n",
       "      <td>0.802419</td>\n",
       "      <td>0.303995</td>\n",
       "      <td>0.787724</td>\n",
       "      <td>0.438692</td>\n",
       "      <td>0.795870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Accuracy  Precision    Recall   F-Score       AUC\n",
       "0    Logistic Regression_60  0.822488   0.308645  0.654083  0.419391  0.747436\n",
       "0    Logistic Regression_50  0.756419   0.250474  0.745373  0.374951  0.751496\n",
       "0    Logistic Regression_65  0.849290   0.345123  0.598975  0.437920  0.737733\n",
       "0    Logistic Regression_70  0.872208   0.390137  0.539370  0.452774  0.723874\n",
       "0    Logistic Regression_40  0.670681   0.205276  0.821813  0.328499  0.738036\n",
       "0             Decision Tree  0.741014   0.232899  0.715989  0.351471  0.729861\n",
       "0  Random Forest Classifier  0.755517   0.251201  0.754366  0.376897  0.755004\n",
       "0    Support Vector Machine  0.791524   0.289372  0.774129  0.421271  0.783772\n",
       "0           LGBM Classifier  0.802419   0.303995  0.787724  0.438692  0.795870"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_metrics = eval('LGBM Classifier', lgbm_predictions['Actual'], lgbm_predictions['Predicted'])\n",
    "metrics = pd.concat([metrics, lgbm_metrics])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EXPORT METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LGBM Classifier</td>\n",
       "      <td>0.802419</td>\n",
       "      <td>0.303995</td>\n",
       "      <td>0.787724</td>\n",
       "      <td>0.438692</td>\n",
       "      <td>0.79587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Model  Accuracy  Precision    Recall   F-Score      AUC\n",
       "0  LGBM Classifier  0.802419   0.303995  0.787724  0.438692  0.79587"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_metrics = eval('LGBM Classifier', lgbm_predictions['Actual'], lgbm_predictions['Predicted'])\n",
    "lgbm_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics.to_csv('Classification Model Evaluation.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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