australia-rainfall-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "53c65cb8",
   "metadata": {},
   "source": [
    "Data Description\n",
    "\n",
    "* 1. Location - Name of the city from Australia.\n",
    "* 2. MinTemp - The Minimum temperature during a particular day. (degree Celsius)\n",
    "* 3. MaxTemp - The maximum temperature during a particular day. (degree Celsius)\n",
    "* 4. Rainfall - Rainfall during a particular day. (millimeters)\n",
    "* 5. Evaporation - Evaporation during a particular day. (millimeters)\n",
    "* 6. Sunshine - Bright sunshine during a particular day. (hours)\n",
    "* 7. WindGusDir - The direction of the strongest gust during a particular day. (16 compass points)\n",
    "* 8. WindGuSpeed - Speed of strongest gust during a particular day. (kilometers per hour)\n",
    "* 9. WindDir9am - The direction of the wind for 10 min prior to 9 am. (compass points)\n",
    "* 10. WindDir3pm - The direction of the wind for 10 min prior to 3 pm. (compass points)\n",
    "* 11. WindSpeed9am - Speed of the wind for 10 min prior to 9 am. (kilometers per hour)\n",
    "* 12. WindSpeed3pm - Speed of the wind for 10 min prior to 3 pm. (kilometers per hour)\n",
    "* 13. Humidity9am - The humidity of the wind at 9 am. (percent)\n",
    "* 14. Humidity3pm - The humidity of the wind at 3 pm. (percent)\n",
    "* 15. Pressure9am - Atmospheric pressure at 9 am. (hectopascals)\n",
    "* 16. Pressure3pm - Atmospheric pressure at 3 pm. (hectopascals)\n",
    "* 17. Cloud9am - Cloud-obscured portions of the sky at 9 am. (eighths)\n",
    "* 18. Cloud3pm - Cloud-obscured portions of the sky at 3 pm. (eighths)\n",
    "* 19. Temp9am - The temperature at 9 am. (degree Celsius)\n",
    "* 20. Temp3pm - The temperature at 3 pm. (degree Celsius)\n",
    "* 21. RainToday - If today is rainy then ‘Yes’. If today is not rainy then ‘No’.\n",
    "* 22. RainTomorrow - If tomorrow is rainy then 1 (Yes). If tomorrow is not rainy then 0 (No)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "314181d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the tools we need\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import random\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "78735fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#! pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9d5edda",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row ID</th>\n",
       "      <td>Row0</td>\n",
       "      <td>Row1</td>\n",
       "      <td>Row2</td>\n",
       "      <td>Row3</td>\n",
       "      <td>Row4</td>\n",
       "      <td>Row5</td>\n",
       "      <td>Row6</td>\n",
       "      <td>Row7</td>\n",
       "      <td>Row8</td>\n",
       "      <td>Row9</td>\n",
       "      <td>Row10</td>\n",
       "      <td>Row11</td>\n",
       "      <td>Row12</td>\n",
       "      <td>Row13</td>\n",
       "      <td>Row14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MinTemp</th>\n",
       "      <td>13.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.8</td>\n",
       "      <td>14.1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>9.8</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxTemp</th>\n",
       "      <td>22.9</td>\n",
       "      <td>25.1</td>\n",
       "      <td>32.3</td>\n",
       "      <td>29.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>30.1</td>\n",
       "      <td>30.4</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>20.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>22.5</td>\n",
       "      <td>25.6</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rainfall</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evaporation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunshine</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustDir</th>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>N</td>\n",
       "      <td>NNE</td>\n",
       "      <td>SW</td>\n",
       "      <td>WNW</td>\n",
       "      <td>ENE</td>\n",
       "      <td>W</td>\n",
       "      <td>SSE</td>\n",
       "      <td>SSE</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDir9am</th>\n",
       "      <td>W</td>\n",
       "      <td>NNW</td>\n",
       "      <td>ENE</td>\n",
       "      <td>W</td>\n",
       "      <td>SSE</td>\n",
       "      <td>S</td>\n",
       "      <td>SSE</td>\n",
       "      <td>NE</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SSW</td>\n",
       "      <td>N</td>\n",
       "      <td>WSW</td>\n",
       "      <td>SE</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDir3pm</th>\n",
       "      <td>WNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>NW</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>SSE</td>\n",
       "      <td>ESE</td>\n",
       "      <td>ENE</td>\n",
       "      <td>SSW</td>\n",
       "      <td>WNW</td>\n",
       "      <td>E</td>\n",
       "      <td>WNW</td>\n",
       "      <td>SW</td>\n",
       "      <td>NNW</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity9am</th>\n",
       "      <td>71.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity3pm</th>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure9am</th>\n",
       "      <td>1007.7</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1009.2</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>1011.8</td>\n",
       "      <td>1010.5</td>\n",
       "      <td>1001.2</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1012.2</td>\n",
       "      <td>1005.8</td>\n",
       "      <td>1009.4</td>\n",
       "      <td>1019.2</td>\n",
       "      <td>1013.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure3pm</th>\n",
       "      <td>1007.1</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>1005.7</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>1004.2</td>\n",
       "      <td>1001.8</td>\n",
       "      <td>1010.3</td>\n",
       "      <td>1010.4</td>\n",
       "      <td>1002.2</td>\n",
       "      <td>1009.7</td>\n",
       "      <td>1017.1</td>\n",
       "      <td>1008.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud9am</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud3pm</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp9am</th>\n",
       "      <td>16.9</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.8</td>\n",
       "      <td>20.6</td>\n",
       "      <td>16.3</td>\n",
       "      <td>20.1</td>\n",
       "      <td>20.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>15.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.8</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp3pm</th>\n",
       "      <td>21.8</td>\n",
       "      <td>24.3</td>\n",
       "      <td>29.7</td>\n",
       "      <td>28.9</td>\n",
       "      <td>25.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>26.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RainToday</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RainTomorrow</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0       1       2       3       4       5       6       7   \\\n",
       "row ID           Row0    Row1    Row2    Row3    Row4    Row5    Row6    Row7   \n",
       "Location       Albury  Albury  Albury  Albury  Albury  Albury  Albury  Albury   \n",
       "MinTemp          13.4     7.4    17.5    14.6     7.7    13.1    13.4    15.9   \n",
       "MaxTemp          22.9    25.1    32.3    29.7    26.7    30.1    30.4    21.7   \n",
       "Rainfall          0.6     0.0     1.0     0.2     0.0     1.4     0.0     2.2   \n",
       "Evaporation       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "Sunshine          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "WindGustDir         W     WNW       W     WNW       W       W       N     NNE   \n",
       "WindGustSpeed    44.0    44.0    41.0    56.0    35.0    28.0    30.0    31.0   \n",
       "WindDir9am          W     NNW     ENE       W     SSE       S     SSE      NE   \n",
       "WindDir3pm        WNW     WSW      NW       W       W     SSE     ESE     ENE   \n",
       "WindSpeed9am     20.0     4.0     7.0    19.0     6.0    15.0    17.0    15.0   \n",
       "WindSpeed3pm     24.0    22.0    20.0    24.0    17.0    11.0     6.0    13.0   \n",
       "Humidity9am      71.0    44.0    82.0    55.0    48.0    58.0    48.0    89.0   \n",
       "Humidity3pm      22.0    25.0    33.0    23.0    19.0    27.0    22.0    91.0   \n",
       "Pressure9am    1007.7  1010.6  1010.8  1009.2  1013.4  1007.0  1011.8  1010.5   \n",
       "Pressure3pm    1007.1  1007.8  1006.0  1005.4  1010.1  1005.7  1008.7  1004.2   \n",
       "Cloud9am          8.0     NaN     7.0     NaN     NaN     NaN     NaN     8.0   \n",
       "Cloud3pm          NaN     NaN     8.0     NaN     NaN     NaN     NaN     8.0   \n",
       "Temp9am          16.9    17.2    17.8    20.6    16.3    20.1    20.4    15.9   \n",
       "Temp3pm          21.8    24.3    29.7    28.9    25.5    28.2    28.8    17.0   \n",
       "RainToday          No      No      No      No      No     Yes      No     Yes   \n",
       "RainTomorrow        0       0       0       0       0       0       1       1   \n",
       "\n",
       "                   8       9       10      11      12      13      14  \n",
       "row ID           Row8    Row9   Row10   Row11   Row12   Row13   Row14  \n",
       "Location       Albury  Albury  Albury  Albury  Albury  Albury  Albury  \n",
       "MinTemp          12.6     9.8    14.1    13.5    11.2     9.8    17.1  \n",
       "MaxTemp          21.0    27.7    20.9    22.9    22.5    25.6    33.0  \n",
       "Rainfall          3.6     NaN     0.0    16.8    10.6     0.0     0.0  \n",
       "Evaporation       NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "Sunshine          NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "WindGustDir        SW     WNW     ENE       W     SSE     SSE      NE  \n",
       "WindGustSpeed    44.0    50.0    22.0    63.0    43.0    26.0    43.0  \n",
       "WindDir9am          W     NaN     SSW       N     WSW      SE      NE  \n",
       "WindDir3pm        SSW     WNW       E     WNW      SW     NNW       N  \n",
       "WindSpeed9am     24.0     NaN    11.0     6.0    24.0    17.0    17.0  \n",
       "WindSpeed3pm     20.0    22.0     9.0    20.0    17.0     6.0    22.0  \n",
       "Humidity9am      65.0    50.0    69.0    80.0    47.0    45.0    38.0  \n",
       "Humidity3pm      43.0    28.0    82.0    65.0    32.0    26.0    28.0  \n",
       "Pressure9am    1001.2  1013.4  1012.2  1005.8  1009.4  1019.2  1013.6  \n",
       "Pressure3pm    1001.8  1010.3  1010.4  1002.2  1009.7  1017.1  1008.1  \n",
       "Cloud9am          NaN     0.0     8.0     8.0     NaN     NaN     NaN  \n",
       "Cloud3pm          7.0     NaN     1.0     1.0     2.0     NaN     1.0  \n",
       "Temp9am          15.8    17.3    17.2    18.0    15.5    15.8    24.5  \n",
       "Temp3pm          19.8    26.2    18.1    21.5    21.0    23.2    31.6  \n",
       "RainToday         Yes     NaN      No     Yes     Yes      No      No  \n",
       "RainTomorrow        0       0       1       1       0       0       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Data\n",
    "df = pd.read_csv(\"data/weather-training-data.csv\")\n",
    "df.head(15).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88b8ef44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99516 entries, 0 to 99515\n",
      "Data columns (total 23 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   row ID         99516 non-null  object \n",
      " 1   Location       99516 non-null  object \n",
      " 2   MinTemp        99073 non-null  float64\n",
      " 3   MaxTemp        99286 non-null  float64\n",
      " 4   Rainfall       98537 non-null  float64\n",
      " 5   Evaporation    56985 non-null  float64\n",
      " 6   Sunshine       52199 non-null  float64\n",
      " 7   WindGustDir    92995 non-null  object \n",
      " 8   WindGustSpeed  93036 non-null  float64\n",
      " 9   WindDir9am     92510 non-null  object \n",
      " 10  WindDir3pm     96868 non-null  object \n",
      " 11  WindSpeed9am   98581 non-null  float64\n",
      " 12  WindSpeed3pm   97681 non-null  float64\n",
      " 13  Humidity9am    98283 non-null  float64\n",
      " 14  Humidity3pm    97010 non-null  float64\n",
      " 15  Pressure9am    89768 non-null  float64\n",
      " 16  Pressure3pm    89780 non-null  float64\n",
      " 17  Cloud9am       61944 non-null  float64\n",
      " 18  Cloud3pm       59514 non-null  float64\n",
      " 19  Temp9am        98902 non-null  float64\n",
      " 20  Temp3pm        97612 non-null  float64\n",
      " 21  RainToday      98537 non-null  object \n",
      " 22  RainTomorrow   99516 non-null  int64  \n",
      "dtypes: float64(16), int64(1), object(6)\n",
      "memory usage: 17.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f49512b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99516, 23)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "878b9224",
   "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>RainTomorrow</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxTemp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-4.1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-3.8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-3.7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-3.2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-3.1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "RainTomorrow  0  1\n",
       "MaxTemp           \n",
       "-4.1          0  1\n",
       "-3.8          0  1\n",
       "-3.7          0  1\n",
       "-3.2          1  0\n",
       "-3.1          0  1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.MaxTemp, df.RainTomorrow).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f18e4531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>RainTomorrow</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adelaide</th>\n",
       "      <td>1675</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albany</th>\n",
       "      <td>1452</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albury</th>\n",
       "      <td>1699</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AliceSprings</th>\n",
       "      <td>1948</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BadgerysCreek</th>\n",
       "      <td>1641</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "RainTomorrow      0    1\n",
       "Location                \n",
       "Adelaide       1675  503\n",
       "Albany         1452  599\n",
       "Albury         1699  443\n",
       "AliceSprings   1948  171\n",
       "BadgerysCreek  1641  400"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.Location, df.RainTomorrow).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd4dbac",
   "metadata": {},
   "source": [
    "Some of our data is in string datatype. We need to convert it to numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4e4aa91d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row ID\n",
      "Location\n",
      "WindGustDir\n",
      "WindDir9am\n",
      "WindDir3pm\n",
      "RainToday\n"
     ]
    }
   ],
   "source": [
    "# Checking which features are in non-numeric \n",
    "for label, content in df.items():\n",
    "    if pd.api.types.is_string_dtype(content):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "81c986fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting to numeric dtype\n",
    "for label, content in df.items():\n",
    "    if pd.api.types.is_string_dtype(content):\n",
    "        df[label] = content.astype(\"category\").cat.as_ordered()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "100c0e03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row ID           category\n",
       "Location         category\n",
       "MinTemp           float64\n",
       "MaxTemp           float64\n",
       "Rainfall          float64\n",
       "Evaporation       float64\n",
       "Sunshine          float64\n",
       "WindGustDir      category\n",
       "WindGustSpeed     float64\n",
       "WindDir9am       category\n",
       "WindDir3pm       category\n",
       "WindSpeed9am      float64\n",
       "WindSpeed3pm      float64\n",
       "Humidity9am       float64\n",
       "Humidity3pm       float64\n",
       "Pressure9am       float64\n",
       "Pressure3pm       float64\n",
       "Cloud9am          float64\n",
       "Cloud3pm          float64\n",
       "Temp9am           float64\n",
       "Temp3pm           float64\n",
       "RainToday        category\n",
       "RainTomorrow        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f4ed605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    2\n",
       "2    2\n",
       "3    2\n",
       "4    2\n",
       "dtype: int8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Location.cat.codes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a8541a9",
   "metadata": {},
   "source": [
    "The string columns has been converted to category dtype. It has basically assigned a numerical value to each category, under the hood. So now we can treat these columns and its content as numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eaa2b3b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row ID               0\n",
       "Location             0\n",
       "MinTemp            443\n",
       "MaxTemp            230\n",
       "Rainfall           979\n",
       "Evaporation      42531\n",
       "Sunshine         47317\n",
       "WindGustDir       6521\n",
       "WindGustSpeed     6480\n",
       "WindDir9am        7006\n",
       "WindDir3pm        2648\n",
       "WindSpeed9am       935\n",
       "WindSpeed3pm      1835\n",
       "Humidity9am       1233\n",
       "Humidity3pm       2506\n",
       "Pressure9am       9748\n",
       "Pressure3pm       9736\n",
       "Cloud9am         37572\n",
       "Cloud3pm         40002\n",
       "Temp9am            614\n",
       "Temp3pm           1904\n",
       "RainToday          979\n",
       "RainTomorrow         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if data is missing \n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d88f7a11",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row ID</th>\n",
       "      <td>Row0</td>\n",
       "      <td>Row1</td>\n",
       "      <td>Row2</td>\n",
       "      <td>Row3</td>\n",
       "      <td>Row4</td>\n",
       "      <td>Row5</td>\n",
       "      <td>Row6</td>\n",
       "      <td>Row7</td>\n",
       "      <td>Row8</td>\n",
       "      <td>Row9</td>\n",
       "      <td>Row10</td>\n",
       "      <td>Row11</td>\n",
       "      <td>Row12</td>\n",
       "      <td>Row13</td>\n",
       "      <td>Row14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MinTemp</th>\n",
       "      <td>13.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.8</td>\n",
       "      <td>14.1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>9.8</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxTemp</th>\n",
       "      <td>22.9</td>\n",
       "      <td>25.1</td>\n",
       "      <td>32.3</td>\n",
       "      <td>29.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>30.1</td>\n",
       "      <td>30.4</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>20.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>22.5</td>\n",
       "      <td>25.6</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rainfall</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evaporation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunshine</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustDir</th>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>N</td>\n",
       "      <td>NNE</td>\n",
       "      <td>SW</td>\n",
       "      <td>WNW</td>\n",
       "      <td>ENE</td>\n",
       "      <td>W</td>\n",
       "      <td>SSE</td>\n",
       "      <td>SSE</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDir9am</th>\n",
       "      <td>W</td>\n",
       "      <td>NNW</td>\n",
       "      <td>ENE</td>\n",
       "      <td>W</td>\n",
       "      <td>SSE</td>\n",
       "      <td>S</td>\n",
       "      <td>SSE</td>\n",
       "      <td>NE</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SSW</td>\n",
       "      <td>N</td>\n",
       "      <td>WSW</td>\n",
       "      <td>SE</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDir3pm</th>\n",
       "      <td>WNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>NW</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>SSE</td>\n",
       "      <td>ESE</td>\n",
       "      <td>ENE</td>\n",
       "      <td>SSW</td>\n",
       "      <td>WNW</td>\n",
       "      <td>E</td>\n",
       "      <td>WNW</td>\n",
       "      <td>SW</td>\n",
       "      <td>NNW</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity9am</th>\n",
       "      <td>71.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity3pm</th>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure9am</th>\n",
       "      <td>1007.7</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1009.2</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>1011.8</td>\n",
       "      <td>1010.5</td>\n",
       "      <td>1001.2</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1012.2</td>\n",
       "      <td>1005.8</td>\n",
       "      <td>1009.4</td>\n",
       "      <td>1019.2</td>\n",
       "      <td>1013.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure3pm</th>\n",
       "      <td>1007.1</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>1005.7</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>1004.2</td>\n",
       "      <td>1001.8</td>\n",
       "      <td>1010.3</td>\n",
       "      <td>1010.4</td>\n",
       "      <td>1002.2</td>\n",
       "      <td>1009.7</td>\n",
       "      <td>1017.1</td>\n",
       "      <td>1008.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud9am</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud3pm</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp9am</th>\n",
       "      <td>16.9</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.8</td>\n",
       "      <td>20.6</td>\n",
       "      <td>16.3</td>\n",
       "      <td>20.1</td>\n",
       "      <td>20.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>15.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.8</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp3pm</th>\n",
       "      <td>21.8</td>\n",
       "      <td>24.3</td>\n",
       "      <td>29.7</td>\n",
       "      <td>28.9</td>\n",
       "      <td>25.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>26.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RainToday</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RainTomorrow</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0       1       2       3       4       5       6       7   \\\n",
       "row ID           Row0    Row1    Row2    Row3    Row4    Row5    Row6    Row7   \n",
       "Location       Albury  Albury  Albury  Albury  Albury  Albury  Albury  Albury   \n",
       "MinTemp          13.4     7.4    17.5    14.6     7.7    13.1    13.4    15.9   \n",
       "MaxTemp          22.9    25.1    32.3    29.7    26.7    30.1    30.4    21.7   \n",
       "Rainfall          0.6     0.0     1.0     0.2     0.0     1.4     0.0     2.2   \n",
       "Evaporation       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "Sunshine          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "WindGustDir         W     WNW       W     WNW       W       W       N     NNE   \n",
       "WindGustSpeed    44.0    44.0    41.0    56.0    35.0    28.0    30.0    31.0   \n",
       "WindDir9am          W     NNW     ENE       W     SSE       S     SSE      NE   \n",
       "WindDir3pm        WNW     WSW      NW       W       W     SSE     ESE     ENE   \n",
       "WindSpeed9am     20.0     4.0     7.0    19.0     6.0    15.0    17.0    15.0   \n",
       "WindSpeed3pm     24.0    22.0    20.0    24.0    17.0    11.0     6.0    13.0   \n",
       "Humidity9am      71.0    44.0    82.0    55.0    48.0    58.0    48.0    89.0   \n",
       "Humidity3pm      22.0    25.0    33.0    23.0    19.0    27.0    22.0    91.0   \n",
       "Pressure9am    1007.7  1010.6  1010.8  1009.2  1013.4  1007.0  1011.8  1010.5   \n",
       "Pressure3pm    1007.1  1007.8  1006.0  1005.4  1010.1  1005.7  1008.7  1004.2   \n",
       "Cloud9am          8.0     NaN     7.0     NaN     NaN     NaN     NaN     8.0   \n",
       "Cloud3pm          NaN     NaN     8.0     NaN     NaN     NaN     NaN     8.0   \n",
       "Temp9am          16.9    17.2    17.8    20.6    16.3    20.1    20.4    15.9   \n",
       "Temp3pm          21.8    24.3    29.7    28.9    25.5    28.2    28.8    17.0   \n",
       "RainToday          No      No      No      No      No     Yes      No     Yes   \n",
       "RainTomorrow        0       0       0       0       0       0       1       1   \n",
       "\n",
       "                   8       9       10      11      12      13      14  \n",
       "row ID           Row8    Row9   Row10   Row11   Row12   Row13   Row14  \n",
       "Location       Albury  Albury  Albury  Albury  Albury  Albury  Albury  \n",
       "MinTemp          12.6     9.8    14.1    13.5    11.2     9.8    17.1  \n",
       "MaxTemp          21.0    27.7    20.9    22.9    22.5    25.6    33.0  \n",
       "Rainfall          3.6     NaN     0.0    16.8    10.6     0.0     0.0  \n",
       "Evaporation       NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "Sunshine          NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "WindGustDir        SW     WNW     ENE       W     SSE     SSE      NE  \n",
       "WindGustSpeed    44.0    50.0    22.0    63.0    43.0    26.0    43.0  \n",
       "WindDir9am          W     NaN     SSW       N     WSW      SE      NE  \n",
       "WindDir3pm        SSW     WNW       E     WNW      SW     NNW       N  \n",
       "WindSpeed9am     24.0     NaN    11.0     6.0    24.0    17.0    17.0  \n",
       "WindSpeed3pm     20.0    22.0     9.0    20.0    17.0     6.0    22.0  \n",
       "Humidity9am      65.0    50.0    69.0    80.0    47.0    45.0    38.0  \n",
       "Humidity3pm      43.0    28.0    82.0    65.0    32.0    26.0    28.0  \n",
       "Pressure9am    1001.2  1013.4  1012.2  1005.8  1009.4  1019.2  1013.6  \n",
       "Pressure3pm    1001.8  1010.3  1010.4  1002.2  1009.7  1017.1  1008.1  \n",
       "Cloud9am          NaN     0.0     8.0     8.0     NaN     NaN     NaN  \n",
       "Cloud3pm          7.0     NaN     1.0     1.0     2.0     NaN     1.0  \n",
       "Temp9am          15.8    17.3    17.2    18.0    15.5    15.8    24.5  \n",
       "Temp3pm          19.8    26.2    18.1    21.5    21.0    23.2    31.6  \n",
       "RainToday         Yes     NaN      No     Yes     Yes      No      No  \n",
       "RainTomorrow        0       0       1       1       0       0       0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca92e1d",
   "metadata": {},
   "source": [
    "Yes. The dataset has missing values. So we are going to fill the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4b725126",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MinTemp\n",
      "MaxTemp\n",
      "Rainfall\n",
      "Evaporation\n",
      "Sunshine\n",
      "WindGustSpeed\n",
      "WindSpeed9am\n",
      "WindSpeed3pm\n",
      "Humidity9am\n",
      "Humidity3pm\n",
      "Pressure9am\n",
      "Pressure3pm\n",
      "Cloud9am\n",
      "Cloud3pm\n",
      "Temp9am\n",
      "Temp3pm\n"
     ]
    }
   ],
   "source": [
    "# Check columns which are numeric and has null values\n",
    "for label, content in df.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3447e279",
   "metadata": {},
   "source": [
    "We can fill these empty numeric rows with median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cf4d39e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for label, content in df.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            # Fill missing data\n",
    "            df[label] = content.fillna(content.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd0dcd6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chcek if there is any null numeric values\n",
    "for label, content in df.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b069b73",
   "metadata": {},
   "source": [
    "There are no null numeric values. Filling and turning categorical variables into numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4bba29e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WindGustDir\n",
      "WindDir9am\n",
      "WindDir3pm\n",
      "RainToday\n"
     ]
    }
   ],
   "source": [
    "# Check for columns which aren't numeric\n",
    "for label, content in df.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c596c0f4",
   "metadata": {},
   "source": [
    "Trying to the null values in the above rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84501df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0          W\n",
       "1        WNW\n",
       "2          W\n",
       "3        WNW\n",
       "4          W\n",
       "        ... \n",
       "99511    ESE\n",
       "99512      E\n",
       "99513      E\n",
       "99514    NNW\n",
       "99515      N\n",
       "Name: WindGustDir, Length: 99516, dtype: category\n",
       "Categories (16, object): ['E' < 'ENE' < 'ESE' < 'N' ... 'SW' < 'W' < 'WNW' < 'WSW']>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"WindGustDir\"].unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79a9fb0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0        No\n",
       "1        No\n",
       "2        No\n",
       "3        No\n",
       "4        No\n",
       "         ..\n",
       "99511    No\n",
       "99512    No\n",
       "99513    No\n",
       "99514    No\n",
       "99515    No\n",
       "Name: RainToday, Length: 99516, dtype: category\n",
       "Categories (2, object): ['No' < 'Yes']>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"RainToday\"].unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c3742a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_na=df['WindGustDir'].isna().sum()\n",
    "df.loc[df.WindGustDir.isna(),\"WindGustDir\"]=random.choices(['W','WNW','N','NNE','SW','ENE','SSE','NE','WSW','NNW','S','ESE','NW','E','SSW','SE'], k=num_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e1d8fd7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "wind_dir_na=df['WindDir9am'].isna().sum()\n",
    "df.loc[df.WindDir9am.isna(),\"WindDir9am\"]=random.choices(['W','NNW','ENE','SSE','S','NE','SSW','N','WSW','SE','ESE','E','NW','NNE','SW','WNW'], k=wind_dir_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0337002a",
   "metadata": {},
   "outputs": [],
   "source": [
    "wind_dir_three_na=df['WindDir3pm'].isna().sum()\n",
    "df.loc[df.WindDir3pm.isna(),\"WindDir3pm\"]=random.choices(['WNW','WSW','NW','W','SSE','ESE','ENE','SSW','E','SW','NNW','N','S','SE','NNE','NE'], k=wind_dir_three_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "658d43cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "rain_today_na=df['RainToday'].isna().sum()\n",
    "df.loc[df.RainToday.isna(),\"RainToday\"]=random.choices(['No','Yes'], k=rain_today_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ce6aaa48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row ID           0\n",
       "Location         0\n",
       "MinTemp          0\n",
       "MaxTemp          0\n",
       "Rainfall         0\n",
       "Evaporation      0\n",
       "Sunshine         0\n",
       "WindGustDir      0\n",
       "WindGustSpeed    0\n",
       "WindDir9am       0\n",
       "WindDir3pm       0\n",
       "WindSpeed9am     0\n",
       "WindSpeed3pm     0\n",
       "Humidity9am      0\n",
       "Humidity3pm      0\n",
       "Pressure9am      0\n",
       "Pressure3pm      0\n",
       "Cloud9am         0\n",
       "Cloud3pm         0\n",
       "Temp9am          0\n",
       "Temp3pm          0\n",
       "RainToday        0\n",
       "RainTomorrow     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there is anymore missing data\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cdd7448",
   "metadata": {},
   "source": [
    "Now we have got no missing data in our columns and all of our data is numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "449916bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale = 1.2)\n",
    "def plot_and_stats(data,col):\n",
    "    print(data[col].mean())\n",
    "    print(data[col].median())\n",
    "    \n",
    "    fig = plt.figure(figsize=[15, 6])\n",
    "\n",
    "\n",
    "    plt.subplot(1,2,1)\n",
    "    sns.histplot(data=data, x=col,kde='true')\n",
    "    plt.title(\"Mean\")\n",
    "\n",
    "    plt.subplot(1,2,2)\n",
    "    sns.histplot(x=col, data=data)\n",
    "    plt.title(\"Median\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "db3159a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.217083685035647\n",
      "22.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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3714cO3YM1dXVKC0txf333w+1Wo2ioiLMmzcP69atC9bbIiIiGhTmRyIiChdB63ksLCxEYWFh4Pu2tjbs2bMH11xzDb744guYTCZcffXVaGlpQWFhIR599FGkpaWhsrISkydPDjxOp9MhIyMDFRUVUCgUyMjIgF6vD9yfnZ2NAwcOBOttERERDQrzIxERhQtZturo6OjAsmXLMG3aNFx22WXQ6XSYMWMG/ud//gcfffQRtFot7rvvPgCAzWaDTqfr9nitVgu73Q6r1QqtVtvtPp1OB4cjOOPiiYiIhhLzIxERhbKgr7ZaXV2N5cuXIzc3F88++ywUCgUeeeSRbuc8/PDDKCoqwsmTJ3tMdg6HAwaDAZIkobOz+yIjdru925XW/khKMp7bm5FRSkqM3CGEhEhoB51ODbckQKdTITHR2Ofx0x/bYXNBp1MP6LH9ee5wFAmfh6HCtvAJp3YIxfwIhF+ODKf/8+EUKe2gVIpQqZRQKsVu+UqpFAGg1xzmf9zp5/T2fP15zXAXKZ+JwWI7+JxrOwS1eNy9ezeWL1+Om266CQ8++CAEwbeC1erVq1FcXIycnBwAgMvlAgBoNBrk5uaiuro68Bx2ux0nT55ETk4OJElCfX09HA5H4AprVVUVcnNzBxRXS4sFXm/4LMWckhKDpqYOucOQXSS0gygKsNudsFqdUAoSTCYLPB6p1+M9PRbw/dvfx57tNeXYNmSoRMLnYaiwLXxObweFQgjZQihU8yMQXjmSn32fSGkHURTgdnvgcrnhdnu65Su32wMAveZH/+O6ntPb8/X3NeXYNmSoRMpnYrDYDj6DyY9BG7Z64sQJ3HPPPbj//vuxcuXKQGIEgPLycjz99NMwm80wm8144oknMHfuXCQmJqK4uBjbtm1DSUkJnE4nnnvuOeTn5yMrKwvZ2dmYMGECXnjhBTidTuzatQvbtm1DcXFxsN4WUUQJ9rYhRMT8SBQugr1tCFEoClrx+Oabb8JqteL555/vtl/VM888gyeeeAKxsbFYsGAB5s2bB5VKhT/96U8AgLy8PDz55JNYtWoVZs2ahcrKSqxevTrwvGvWrMHRo0dRVFSE3/zmN3jiiScwfvz4YL0tIiKiQWF+JCKicBG0YauPPPLIGXM3unruued6vW/RokVYtGhRj/dlZGTgb3/726DjIyIikgPzIxERhQtZVlslIiIiIiKi8MLikYiIiIiIiPrE4pGIiIiIiIj6xOKRiIiIiIiI+hTUfR6JaPDcHi/2VjRhT3kjWjs6ER+jRd6oWLnDIiIiIqIIx+KRKIw0mGz4r3UH0GCyId6ogcPpxtE6M47Vt6NgXCqSYrVyh0hEREREEYrDVonCxNG6djzxj29htbvwiyVT8MIvLsSPL83BxdNHoMPmwh/+Zzc3LiYiIiKiYcPikSgMNLXZsXrdAeg1SvzmtvNQMD4FCoUAAMhMjcGiWaPgdHmxZsP3cLo8MkdLRERERJGIxSNRiHO5vfivdw/A65XwyxumITVBf8Y58UYN7rlmEo6f6sAbWypkiJKIiIiIIh2LR6IQt7eiCXWNFtx77SSkJ55ZOPrNGJ+CK2aPxo799ahvtgYxQiIiIiKKBiweiUJYc5sdFTXtWDhzFCZnJfV5/rUXZWFEsh5fH2yAy+0NQoREREREFC1YPBIFiSgKga/+cHu82H+0BTF6Fa6bm9Ovx6iUIu4qngi7w42yY6bBhNvNQGMnIiIaiHDOM+EcO9FAcasOoiAQRQElZY3osHUiRq/BrImp8Hiksz7m8PE22BxuzC/MhEYl9nm+X87IOEwcm4CDx1qHZPjqucRORETUX6IoYP2OKpjMDiTGanHdnOywyTPhHDvRuWDPI1GQdNg60W5xosPW2ee5ZqsTpVUmpCfqkJHU+zzH3kzLTYJRp8LXBxtgsbvOJdxuBhI7ERHRQJnMDjS32cNyy6lwjp1ooFg8EoWg976ogtvrxcQxief0eFFUoGBcMhydbvz3ewfh9fIqKBERERENDotHohBjMjuwfV89ckfGwahXnfPzJMRocH5+Kr6vasG67UchSSwgiYiIiOjccc4jUYj5+JsaSBIwOTtx0PMmxo+Kh0GrwkffnIDF4cJti/IgiuIQRUpERERE0YTFI1EIMVud2LG/DhdMSYdRp0K7xTno57zt8jwYdSp8sPMYKk60YeHMUbDYXeyJJCIiIqIBYfFIFEK27qmBy+3FVUVjhmyrDUEQcO3F2RibEYt/fXUMb2ypAABo1SJGpxmRMzIOo1NjhuS1iIiIiChysXgkChEerxef7avDtNxkjEg2DOk+jQAwPTcZ03KSUN9ixaadx3CiwYKj9Wb8/rU9+NElWSi+cOyQvh4RERERRRYWj0Qh4sQpC8xWF+adN3LYXkMQBIxOi0He6HikJ+qh04g4dqoD63dUwWx1Iv0ctgUhIiIioujA1VaJQkR5TRvSE/WYOPbctuc4F2qViGXXTsb8wkx8sqcWJxosQXttIiIiIgovLB6JQkC7pRNNbQ5cVpgJhSAE9bUFQcANl+ZibHoMvi49BUen+5yeRxSFwFd/jhMREUUD5keKJBy2ShQCTjRaoFAIuHByuiyvrxQVWHbtZDyy9mtU1LYjbYDDV0VRQElZIzpsnYjRazBrYio8HqnX40RERNFAFAWs31EFk9mBxFgtrpuTHciPPR0nCnUsHolk5vF6UdtkxehUIww6lWzJIz1Jj9zMOBypbUeBPXnAj++wdfa4tUhvx4mIiKKByexAc5u938eJQhmHrRLJrLbRCpfbi5yRsUF/bQGAQuEbMqNQCJiclQgBwPdVQ7vSKxERUbgRhO45kojY80gku8q6dmjVoiwrnRr0auwqa4DZ0om0RCOMejXGpMfgaF07TGYH4gyaoMdEREQUCuJjtHh3+1GY2h3IGhHru+JKFOXY80gko3ZLJ0422zAq1Rj0hXL8OqxOtFucsDp8Q0uzR8QCEvDp3jpZ4iEiIgoV/qGlbZx+QQSAxSORrL451AgJwMgUg9yhBBi0KmSmGrB9Xx1cbo/c4RARERFRiGDxSCSjkrIGxBvViNWr5Q6lmwmjE9Bhc2HXwQa5QyEiIiKiEMHikUgmLe0OHKltx9iMGLlDOUNaog6jUo34ZE8tJIlLhxMRERERi0ci2Xxz2NerNzZ9YMWjf0Phriu/nb5q6mAJgoAF549CbZMF5SfaBv18REREw63H/MgVU4mGFFdbJZLJ7kONyMqIRYxe3e99EEVRQElZIzpsvtVRBUEApDNXTRWGYPGdoklpeOfTSmzZXYOJYxMG/XxERETDRRQFrN9RBZO5+8qo8UYNV0wlGkLseSSSQUu7A8dOdeD8/NQBP7bD1tltddTA8dNWTR0stUrEnOkjsLeiCRa7a0iek4iIaLj0tjIqV0wlGjosHolk8G1FEwDgvLwUmSM5u0sLRkIhCCg71ip3KEREREQkMxaPRDL4trwRI5MNSE/U93nuUM9nHIjEWC0umJyOytp2OJzuoL42ERHR2XA+I1Hwcc4jUZA5nB6Un2jDVUVj+3X+cMxnHIirLxqLL78/icpaM9L6UewSEREFQ3yMlvMZiYKMPY9EQVbbZIEkAeeN7/+Q1XOdz9i11/JcpSXokZURi2MNHbA52PtIRESh41znM3bttRxMjiSKNiweiYKspsGC5DgtRqcZh/21/L2WJWWNg0qOU3MSAUnC7sONQxgdERGRPPy9lq9sKsP6HVUsIIn6icUjURC53V7Ut9gwY3xK0Iafdlid6LB1Duo5YvRqjB8VjxMNFuwtbxqSuPxXe5mwiYhIDv5eS5PZIXco3TA/Uihj8UgURI1tdni9UsivstqT3JFxiDeq8T8fHUZrx+CKUf9+lVv31Ay6V5SIiChS+PerZI8ohSoWj0RBdLLFBo1KxPhR8XKHMmAKhYCLpmbA4fRg9brvBr36qn+/ysH2ihIREUWSUO0RJQJYPBIFjccroaHVhlGphrBdUjwhRoPlP5qMmkYLXtpYCpfbI3dIRERERBQkLB6JhkB/5iecarHB7ZEwKggL5QynabnJ+NnlE3Cw2oQX1x1Ap4sFJBER9Y5z+IgiR1CLx507d2LJkiWYMWMGFixYgLfffhsA4HQ68dhjj2HmzJmYPXs21q5d2+1xmzdvxoIFCzB9+nTcfffdaGlpCdxXX1+PO+64AwUFBZg/fz527NgRzLdE1O/5ezWNFogKARkRsFfixdNG4M6r8lF2rNVXQDpZQBINBvMjRSrO4SOKLEErHk+ePIlf/OIXWLZsGfbs2YPnnnsOzz//PL744gusWbMG1dXV+OSTT7Bu3Tps3LgR7733HgCgsrISjz76KJ566imUlJRgzJgxWLFiReB5H3zwQeTl5aGkpAR/+MMfsGLFCtTU1ATrbREB6Hv+ntcroabRgrQEHUSx5x+7rnsyhsOw1gunZOCu4ok4fKIVz/1zP9wer9whEYUl5keKdEMxh6/rvozhkCOJIlXQise6ujoUFxdjwYIFUCgUmDp1KmbOnIm9e/di48aNuPfeexEXF4fMzEwsXbo0cNX1gw8+wLx581BYWAiNRoOVK1di7969OHbsGKqrq1FaWor7778farUaRUVFmDdvHtatWxest0XUL5V17XA4PchI6r3X0b8n49Y9Nfiu0hS0rTwGo2hyOn5+9SRUnGjDV6WnIEmS3CERhR3mR6K+dd2XccvuGt8VVyIKOmWwXqiwsBCFhYWB79va2rBnzx5cc801aGpqQm5ubuC+rKwsVFRUAPBdWZ08eXLgPp1Oh4yMDFRUVEChUCAjIwN6/Q9/kGdnZ+PAgQNBeEdE/fdteRMUgoDUhLMPWe2wOtFuccKodwYpssGbNTENbZZO/PPTSmhUImZNTJM7JKKwwvxI1D/+HsyEWK3coRBFraAVj111dHRg2bJlmDZtGiZNmgQA0Gp/+EWg0+ngcPiGNthsNuh0um6P12q1sNvtZzzu9McShQJJkvBteSPSk3RQKSNzjaorZo/G7sONqKhpx9j0GLnDIQpbzI9ERBTKgl48VldXY/ny5cjNzcWzzz4bSGSdnT/MFbPb7YGrpT0lO4fDAYPBAEmSuj3u9Mf2V1JS+K1+mZLCP9CB0GkHnU4NtyRAp1MhMbH756m6vh1NbQ5cPH0EDAbNGef4H6vVquCRAIMXA7rtsrn6PAfAgF6ztxh7en9+c2ZkorHtCL4qbcCd10yFQac662P785wDFSqfh1DAtvAJp3YIxfwIhF+ODKf/8+EUSu2gVIpQqZRQKsVef9/3dk7guKiAUux+u6djp98G0Oc5A3nNs57fy/vr870N8PnOVSh9JuTEdvA513YIavG4e/duLF++HDfddBMefPBBCIIAjUaDlJQUVFVVIS3NN9yturo6MEwnNzcX1dXVgeew2+04efIkcnJyIEkS6uvr4XA4AldYq6qqug3x6Y+WFgu83vCZq5WSEoOmpg65w5BdqLSDKAqw252wWp1QChJMJgs8nh8+T1t3HYMAIC1eC6u1s9s5XR/rMKjgcLhgtXYO6DYAOByus54jSQN7zd5i7On9+dvA7XJjem4Sdh44hZfX7cftl0/o9bH9ec6BCpXPQyhgW/ic3g4KhRCyhVCo5kcgvHIkP/s+odQOoijA7fbA5XLD7fb0nkN6OKfbcY8Xbk/32z0dO/02gD7PGchrnvX8Ht5fv97bAJ7vXIXSZ0JObAefweTHoI2hO3HiBO655x7cf//9WLlyZbfFQBYvXoy//OUvMJlMqK2txauvvorFixcDAIqLi7Ft2zaUlJTA6XTiueeeQ35+PrKyspCdnY0JEybghRdegNPpxK5du7Bt2zYUFxcH620R9WlvRTPGjYqHTiPLKPFh5d+3y7/yXWKMFvljE7Bjfz0q69pljo4oPDA/EkUmrgxLkShof82++eabsFqteP755/H8888Hjv/kJz/BAw88gKeffhrFxcXwer248cYbcfPNNwMA8vLy8OSTT2LVqlVoaGjAtGnTsHr16sDj16xZg8ceewxFRUVISEjAE088gfHjxwfrbRGdVWObHbVNFtw8f5yscfi3AUGXfwfLv79lh60TaYnGwB+8U3OScLLFhjc+Lsfvls4cktciimTMj0Ty8m8DAgxtjly/owpxRjVXhqWIErTi8ZFHHsEjjzzS6/2rVq3CqlWrerxv0aJFWLRoUY/3ZWRk4G9/+9uQxEg01PaWNwEAzstLwXeVzbLF4d8GxGzpXuj1pr/Fpn9/y66rw6qUCtw8fxxe2liK7fvqhiR+okjG/EgkL/82IKZ2B7JGxPZZ7PW32DSZHQiPAd9E/Rd54+iIQsjeI00YnWpESryu75OH2UC2ARlosXm6mfmp+GR3HN77ohpXzB59riETEREFxUC2ARlosUkUSSJz3wCiENBu6cTR2nbMGJ/S7bi/Vy/U50L4i02rY+B7TgqCgBsuzUW71YmyY63DEB0REUUif69eqOdIf7HZZgmffZmJhgJ7HomGyb4jzZCAM4rHwfbqhYuckXEonJCC/UeakZE48O0BiIgo+rBXjyi0seeRaJjsrWhCarwOI1MMZ9w3mF69cHL93By4PRKO1nPlVSIi6h/26hGFLhaPRMPA5nDj0PFWzBifErE9i/2RkWTA2PQYVJ/sQKfTI3c4RERERDQILB6JhsGBo83weCXMyEvp++QINyUnER6vhEPHOfeRiIiIKJyxeCQaBnsrmhBnVCN7RKzcocgu3qhBRpIeh0+0wWp3yR0OEREREZ0jFo9EAySKQuCrJ06XB99XmVAwLgWKKB6y2tX4UfFwub3YsrtG7lCIiGiY9JUfiSj8cbVVogEQRQElZY3osHUiRq/BrImpZ5xzsNqETpcHM8YnyxBhaIozqDEq1YAtu2uwoHAUjHqV3CEREdEQEkUB63dUwWR2IDFWi+vmZMPjkeQOi4iGGHseiQaow9aJdosTHbbOHu/fU94EnUaJCaMTghxZaJuSnQSbw41t39bKHQoREQ0D/yqpJrND7lCIaJiweCQaQl6vhP1HmjEtNwlKkT9eXSXFaTElOwlbv62F082VV4mIiIjCDf+6JRpCja12WOwuzBjHVVZ7cmXRaJitTnxd2tDnuZw7Q0RE1DPmSJIL5zwSDaETjRaolApMyU6SO5SQlD8mAaPTjPio5DjmzRjZ63k9zS3l3BkiIiLOLyV5seeRaIhIkoSaRgsmZyVCoxblDickCYKAy2eORn2zDXXN1rOe29fcUiIiomjF+aUkFxaPREOk3eKEzeFG4YQzV2ClHxROSEVirAZl1a1yh0JEREREA8DikWiInDTZIAjA9HHcouNslKICi2aORkOrHW0d7FUkIiIiChcsHomGyMkWG9ISdDDquIdhX+ZMHwGVUoHK+na5QyEiIiKifmLxSDQEzFYnLHYXMlONcocSFnQaJcZlxqG+2QaL3SV3OERERETUDyweiYZAbZNv8ZdRKUYoFFw+uz8mjImHIACHjnHuIxFRNBAEMEcShTlu1UE0BGqbLIjRq5CebMCusgaYLdxioi8GrQojkw2orGuH1e6CVs1fR0REkSw+Rot3tx+FqZ1bTBCFK/Y8Eg2S1eFCY6sdaQl6AECH1RmxW0wI+OGqsUIx+KvGOSPj4PZI+Gxf3eCDIyKikBfJW0x07VkdihxJFIp4qZ9okL4/2gJJAtITdXKHMuwMenWgZzUt0QhBGFxyjDOokZGkxye7azD/vFFQKfu+ntV1qBOvWBMRUajo2rOaNSLWd8U1yJgjabix55FokPYfaYZGJSIhRiN3KEHh71m1OpxD8nwTxyagzeJESVlDn+eKooCSskZs3VODkrJGzpkhIqKQ4u9ZbbMMTY4cCFEUsH5HFV7ZVIb1O6qYI2lYsHgkGgSP14sDR1swMsUw6F64aJWRpMeoVCM+/uYEJKnvq6Qdts6IHRZMREQ0GJE8LJhCA4tHokE4UtMOq8ONzBSD3KGEjIHOixQEAVfMHo26ZitKq03DHyAREZEMOCeSIgHnPBINwr4jzVCKAkYkG2BzuOUOJyScy7zIWRPT8O5nR/FRyQlMyU4KQpRERETBFQpzIokGiz2PRIOw/0gzJoxJ6HGhl6FemTScDHRepFJUYH5hJg4db8XxUx3DHB0REcktWnvh5JwTSTQUWDwSnSOz1YlTJhum5yb3eL+/B27rnhp8V2ninMg+zJk2Elq1iI+/OSF3KERENMz8vXCvbCrDlt017IUjChMsHonOUW2TFQAwfVzPxSMw9CuTRjK9VolLpo3AN4ca0dxulzscIiIaZuyFIwo/LB6JztHJFisykvRIiY/8/R2DZeH5o6BQCNj4ebXcoRARERHRaVg8Ep0Dj9eLBpMdk7MT5Q4loiTGajH/vEzsPHASrR3cioOIiIgolPS7eKyvr+9xDzaPx4PS0tIhDYoo1JnMnfB4Ja4MOgyuLBoDnVaJvRVNcodC1C/Mj0REFC36XTxedtllaG1tPeN4fX09brnlliENiijUNbbaoRAETBidIHcoEceoU+HqC8eivtmGpjbOfaTQx/xIRETR4qz7PK5btw5vv/02AECSJCxduhSiKHY7p7m5GSNHjhy+CIlCUFObHakJWmjUYt8nE4Afti5Bl397M78wE//aeQxlx1qRMzI2CNERDQzzIxENFf+2JUDf+ZFIbmctHq+88kqcOnUKAFBaWorZs2fDYDB0O8dgMGDRokXDFyFRiLF3umG2uZAzMk7uUMKKf+sSs6UTaYnGs25dolaKmD4uGTu/P4Vj3PeRQhDzIxENFf+2JaZ2B7JGxHLbEgppZy0e9Xo9/uM//gMAMHLkSFx11VVQq9VBCYwoVJ0y2QAAGUl6mSMJP/6tS4z6vpdlz8qIwfdVLdh3pBlOtweiwPW9KHQwPxLRUPJvW5IQq5U7FKKzOmvx2NWPfvQjlJWV4eDBg3C5XGcsDsB5HRQtGkx2KEUBCTEauUOJaIIgYPLYRHx1sAEfl9Tgytlj5A6JqEfMj0REFC36XTy+/PLLWL16NeLi4s4YmiMIApMjRY2GVhuSYrWclxAEyfE6jEo1YtPOYyialI7keF6RpdDD/EhERNGi38Xju+++iwceeADLli0bzniIQlpbRyfMVhcyU4xyhxI1zstLxr++Oo71O47inmsmnfVcUfQV9B7PmdsmEA0X5kciCgfMkTQU+l08trW14YorrhjOWIhC3uETvuX4k+PYAxYsMXo1Fs0ajQ+/Oo75hZk9niOKAj7bU4MmkwUxeg1mTUxlcqSgYX4kolAmigJe23QQjSYrEmO1uG5ONnMknbN+r0CxaNEibNq0aThjIQp5h4+3QaVUIM7AhTGC6eoLxiLOoMabn1T0uBk7AFjsvsV4OmydQY6Ooh3zIxGFOv+CPCazQ+5QKMz1u+dRq9Vi7dq1+OijjzBmzBioVKpu969evXrIgyMKNYdPtCI1QXfWbSZo6Ok0Slw3Jwf/d/MhpCfqkcTV6CiEMD8SEVG06Hfx6HA4cPXVVw9nLEQhrcPmxMkWGwrGJcsdSlS6YEo6PttXi30VzZhbMELucIgCmB+JiCha9Lt4fOqpp4YzDqKQ459YDvgmlx+tNwMAUhLY6yUHhSDgloXj8fj/fIvK2nYkcd4phQjmR4o2p+dHIooe/S4ed+zYcdb758yZM+hgiEKFKAooKWtEh60zsADL0bp2iAoBSbFaWO1uCAC36wiycZnxGJseg8p6MyZnJ8odDhEA5keKLqIoYP2OKpjMjl4XXxGEH/Ij8yRRZOl38XjPPff0eFyj0SA9PX1AyfHAgQO455578PXXXwMAnE4nZsyY0W2eSEFBAf7v//2/AIDNmzfjhRdeQFNTE84//3w8/fTTSEpKAgDU19fj0Ucfxf79+5GUlITHHnuMiZqGRIetE+0WZ+D7ytp2jEmPgVL0rTNl0Kuxq6wBOrWKcyCDqGB8Mk40dGDfkWZcc3G23OEQMT9S1PEvvtKb+Bgt3t1+FKZ2B7JGxAJMkUQRo9/F4+HDh7t97/F4cOLECfz+97/HNddc06/nkCQJ69atwx//+Mdux8vLyxEXF4edO3ee8ZjKyko8+uijeOWVVzBlyhQ888wzWLFiBf7+978DAB588EFMnz4da9euxbfffov77rsP77//PkaNGtXft0bUJ7fHi+qTZsydMbLb8Q6rE14vh+wMxuk9uH0NgTLqVMgeEYfKunZU15sxOi1mmCMkOjvmR6Iz+QvMBC5wds669uACHCJMoaHfW3WcThRFZGVl4aGHHsKLL77Yr8e8+OKLeOutt87YSPngwYOYMGFCj4/54IMPMG/ePBQWFkKj0WDlypXYu3cvjh07hurqapSWluL++++HWq1GUVER5s2bh3Xr1p3r2yLqUU2DBU63F+My4+QOJeL4e3C37qlBSVljt7k0vRmXGQeNWsRb2470unUHkVyYH4loKPh7cF/ZVIb1O6r6lR+Jhts5F49+FosFra2t/Tr3pptuwoYNGzB58uRux8vKymAymXD11VfjggsuwP3334+GhgYAviurOTk5gXN1Oh0yMjJQUVGBo0ePIiMjA3q9PnB/dnY2ysvLB/u2iLo5UtcOAMgdyeJxOHRYB7ZHo0qpwLScJJSfaMP+I83DHB3RuWF+JKLB4v6MFGr6PWz1T3/60xnHLBYLPvzwQ1x88cX9eo60tLQej+t0OsyYMQP33XcflEolHn/8cdx3331Yt24dbDYbdDpdt/O1Wi3sdnvg9unP5XAM7AcsKck4oPNDQUoKh+oBw9sOOp0abkmATqfC4WOtSI7TIndsMg7VtMMtCdBqVfBICPxr8Mpz22Vz9XmO3DH297ZOp0JiovGM9vcf63p8apIBJ002bPiiCvNmjwUAGAyaM86PVvwd4ROMdojk/AiEX47kZ99nONtBqRShUimhVIrdft8GjosKKMXhv92fcwEEJZbBxNif2ypVL23d2//BaccB9Ho82vB3hM+5tkO/i8fvv/++2/eCIEClUuHWW2/FnXfeeU4v7vfII490+/7hhx9GUVERTp482WOyczgcMBgMkCQJnZ3deyrsdnu3K6390dJiCat5aykpMWhq6pA7DNkNZzuIogC73Qmr1QmlIKH8uAlj0mJgMlkCxx0GFRwOF0QBcDhcsFo7A8eCeRvwvf7ZzpE7xv7ejjeo8NFXVdCpfzimFCSYTBZ4PNIZ/y/Xz8nBC+98h/Vby6HXq884P1rxd4TP6e2gUAjDUghFcn4EwitH8rPvM9z50e32wOVyw+32dPv9HDju8cLtGf7b/TkXQFBiGUyM/blt1KnwynsHAosQBc7p7f/gtOMAzjgejfg7wmcw+bHfxeM//vGPgUfWT6tXr0ZxcXFg+I3L5fuDWKPRIDc3F9XV1YFz7XY7Tp48iZycHEiShPr6ejgcjsAV1qqqKuTm5g5brBR9Ol0eNLTacdHUDLlDiQoDWYRoWm4S8sck4L0vqnHdpX3/3HNvMhoOzI9EFAzDvQiRP0cyP9LZDGjOY3l5OX7961/jRz/6Ea655hqsWLECe/fuHXQQ5eXlePrpp2E2m2E2m/HEE09g7ty5SExMRHFxMbZt24aSkhI4nU4899xzyM/PR1ZWFrKzszFhwgS88MILcDqd2LVrF7Zt24bi4uJBx0Tk19Luu7I/NiNW5kjodIIg4MZ5ubDaXdhX0XTWc/17dw5kYR6i/mJ+JKJw5t+/kwvzUF/6XTzu2LEDS5YsQVtbGxYtWoTLL78cVqsVt956K7788stBBfHEE08gNjYWCxYswLx586BSqQJzSPLy8vDkk09i1apVmDVrFiorK7F69erAY9esWYOjR4+iqKgIv/nNb/DEE09g/Pjxg4qHqKsWs2/o19h0jpEPRaPTYnDh1AyUVrXAYned9Vz/3p39XZiHqD+YH4koEpjMDi7MQ33q97DV//qv/8KyZcvwH//xH92Ov/TSS1i9ejUuuuiifr/orFmzsGfPnsD3CQkJeO6553o9f9GiRVi0aFGP92VkZOBvf/tbv1+baKBa2h1IS9DBoFX1fTINua77QHbd76qr6+fmoKSsAd9XtWBE8sDndBENBvMjEcml616QveVIoqHU757Ho0eP4uqrrz7j+FVXXYWKioohDYoolLSYfZPTSR5d94H8rtIEQTgzOSbEaHD+xDQ0tTlw7BQnwlNwMT8SkVy67gW5ZXeN74or0TDqd/GYkZGBsrKyM44fPHgQSUlJQxoUUahwON2wOdzI4nxHWfn3gbQ6nL2eMzErEfFGNfYcboLVcfbhq0RDifmRiOTkX0inzdJ7jiQaKv0etnrLLbfgt7/9LRoaGjBt2jQAwP79+/Hf//3fWLp06bAFSCSn9n//Is7K4HzHUKcQBEzNScIX353Eu58dxa0L8+QOiaIE8yMREUWLfhePt912G6xWK9auXYvW1lYIgoDU1FT84he/wE9/+tPhjJFINu1WX/E4Oo3FYziIN2qQNyYe2/fW4YJJ6cgZGSd3SBQFmB+JiCha9Dls1el04q233kJ7ezuWLVuGr7/+Gl9++SXuu+8+3H333bjhhhuCESeRLNqtTsToVNBp+n2dhWQ2PTcZCTEavP7RYbg9XrnDoQjG/EhERNHmrMVjR0cHbrnlFjz11FPdNiJOTk6GxWLBc889h9tvvx0Wi2XYAyWSg9nqREKsRu4waABUSgVuuzwPdU1WbN51XO5wKEIxPxIRUTQ6a/H40ksvweFwYMuWLZg+fXq3+x5++GFs2rQJJpMJa9euHc4YiWThcnthdbiRGKOBQiFAFAUugx0mCsanYGZ+KjbtPIa6Zqvc4VAEYn4k+mGbCOZHouhx1uJxy5YtePjhh5Gent7j/ZmZmfj1r3+Njz/+eFiCI5JTa4dvI/mMZEOfW0VQ6PnJ/PHQaZR4/X8PweuV5A6HIgzzIxG3iSCKRmctHpubmzF27NizPsGECRPQ2Ng4lDERhQR/8ZgUp+3XVhEUWmINatx82TgcrTNj297as54rikLgi6g/mB+JfLhNRGRjfqTTnbV4TE9Px7Fjx876BMePH0dycvJQxkQUElo7OqESFTDqVHKHQudo9qQ0TMlOwvodR9HUZu/xHFEUUFLWiK17alBS1sgESf3C/EhEkU4UBazfUYVXNpVh/Y4q5kcC0EfxeMUVV2DNmjVwOnu+muR0OrFmzRrMnTt3OGIjklVrRydiDWoOUw1jgiDgtkV5EAQBr//vYUhSz8NXO2ydaLc40WHrDHKEFK6YH4koGvh7lk1mh9yhUIg46/4D99xzD7Zt24brrrsOt956KyZPnoyYmBi0t7fj+++/xxtvvAGPx4Ply5cHK16ioPB6JbRaOjEm1Sh3KDRISXFaXD8nB29+UoFYvQrJcTq5Q6IIwPxIRETR6KzFo06nw1tvvYVnn30Wf/rTn2C1+lYtlCQJ8fHxuOaaa7B8+XLExXEjboosDa02eDwSYg1quUOhXghAYHW/vlb5u3TGSHxzqAF7ypswr2BkEKKjSMf8SEShzL8SLtB3jiQaiD53Pjcajfjtb3+L//zP/0RNTQ3MZjMSEhIwZswYDuejiHWiwbc3WxyLx5Bl0Kuxq6wBZksn0hLP3kOsEAT87IoJ+M3fSnDoeCtSEtj7SIPH/EhEocq/Eq6p3YGsEbFyh0MRpM/i0U+tViMnJ2c4YyEKGTUNFggCYNSzeAxl/lVwjfq+V/nLTDViwuh4HDrehuZ2zt2gocP8SEShyD9fMSFWK3coFEHOumAOUbQ60dCBOIMaIod6RJSpuUnQqETsOdzY6+I5RERERNQzFo9EPTjRaEFCjEbuMOgc+OdC9rQvlVopYsLoeDS1OfBteZM8ARIREcnEPxeSezfSuer3sFWiaNFhc6K1oxNZGTFyh0LnoOtcyBi9BrMmpsLj+aGXcVSaEcdOdeCdzyoxJTsJoijKGC0REVHwdJ0LmRirxXVzsrvlSKK+sOeR6DQ1jb7FchJj2fMYrvxzIXvat1EhCJgxPhkNJju+PHBShuiIiIjkw70baTBYPBKdxl88cthq5BqZYkDOiFhs3nUcbo9X7nCIiIiIwgKLR6LTnGiwIN6ogVbNUd2RShAELL4oC83tDnxVekrucIiIiIjCAotHotPUNFowOu3s+wZS+JuWm4QxaTHYtPMYvF7O9yAiIiLqC4tHoi7cHi9OmazITGXxGOkEQcBVRWPQ2GpHbZNF7nCIiIiIQh6LR6IuGlvtcHskZKYY5A6FhkDXbTsUPezZOWN8CpLjtDh0vC3osREREcmp67YdPeVIop5wUhdRF3XNVgBAZooRR2rb5A2GBq3rth1piUYIQvfkqFAIWHD+KLy19QjaLJ2IM6q73d91DywuZU5ERJGk67YdWSNifVdc+5nqmB+jF3seibqoa7JAEICMJL3codAQ8W/bYXU4e7z/kmkjoBQFVNWbux0XRQElZY3YuqcGJWWN3EyZiIgijn/bjjZLzzmyJ6IoYP2OKryyqQzrd1QxP0YZ9jwSdVHXZEVagh5qFTeOjxZ6rRI5I2JxpLYdnU5Pt/s6bJ1oH0BCJSIiigb+opOiD3seibqobbZiJOc7RrzT50LmZsbDKwFVJ819PpaIiChScR4k9YU9j0T/5nR50Nhqw6z8VLlDoWF2+lzIpDgt4o1qVNa2Q5I4d4OIiKJTj/MgibpgzyPRv51ssUGSfIvlUOQ7fS7kmLQYtFmcOFrP3kciIope5zIPkqIHi0eif/Pv9cdhq9FpZLIBSlHA5/vr5Q6FiIiIKCSxeCT6t7pmK5SiAqkJOrlDIRkolQqMTovB7kONcLk9fT+AiIiIKMqweCT6t7omK0Yk6SEq+GMRrbIyYmDrdOO7yha5QyEiIiIKOfwrmejf6potHLIa5dKT9Ig3qvH1wVNyh0JEREQUclg8EgGwOdwwmTsxkovlRDWFIGD2pHQcONoCi80ldzhEREREIYXFIxGA+mYrAN+iKRTdLpicDo9Xwu7DjXKHQkRERBRSWDwSgSut0g9GpxmRmqDDnnIWj0RERERdsXgkgm+xHI1aRFKsVu5QSGaCIOC8vBQcOtaKTidXXSUiIiLyY/FIBN9iOZnJBiiVCoiiAIVCkDskklFhXio8XinQI01EFO1EUWB+JCIo5Q6ASG6SJKG2yYrz8lJQUtaIDlsn0hKNEAQmyGg1Nj0GyXFaHG+wIDmO+34SUXQTRQHrd1TBZHYga0QswPRIFLXY80hRz2xzwWJ3ITPViA5bJ9otTlgdTrnDIhkJgoDCCak42WyDy+2VOxwiItmZzA40t9nRZmF+JIpmLB4p6tX5F8vhSqvUReGEFHglCQ2tNrlDISIiIgoJLB4p6tU1+bbpGJXKPR7pBzkj46DTiKhvZvFIREREBLB4JEJtkwUxehViDWq5Q6EQohAEjE6LQWObnUNXiYiIiCBT8XjgwAEUFRUFvnc6nXjssccwc+ZMzJ49G2vXru12/ubNm7FgwQJMnz4dd999N1paWgL31dfX44477kBBQQHmz5+PHTt2BO19UGSoa7ZyyCr1aEyaEV6vhPpmq9yhUJRgfiQiolAW1OJRkiS8++67uPPOO+FyuQLH16xZg+rqanzyySdYt24dNm7ciPfeew8AUFlZiUcffRRPPfUUSkpKMGbMGKxYsSLw2AcffBB5eXkoKSnBH/7wB6xYsQI1NTXBfFsUhvxLjguKfxePKRyySr4FBBWKH5ajT0nQQa1S4HhDh9yhUYRjfqRQ4c+PosglVekHgtA9P1L0Cmrx+OKLL+Ktt97CsmXLuh3fuHEj7r33XsTFxSEzMxNLly7F22+/DQD44IMPMG/ePBQWFkKj0WDlypXYu3cvjh07hurqapSWluL++++HWq1GUVER5s2bh3Xr1gXzbVGYEUUBJWWN2LqnBtv21KHT6cHIFPY8EmDQq7GrrAFb99Tgu0oTRIUCGYl61DfZ4PZw6CoNH+ZHCgX+LTle2VSG9TuqWEBSQHyMFu9uP4pXNpVhy+4abtcSxYJaPN50003YsGEDJk+eHDhmNpvR1NSE3NzcwLGsrCxUVFQA8F1ZzcnJCdyn0+mQkZGBiooKHD16FBkZGdDr9YH7s7OzUV5eHoR3Q+HMvyVHXZOvRymTPY/0bx1WZ7ftWtIS9HB5vCg/0SZvYBTRmB8pVPi35DCZHXKHQiGG27UQEOTiMS0t7YxjNptvJUOtVhs4ptPp4HA4AvfrdN036dZqtbDb7bBard0ed/pjifri/wXIOY/Um+R4LUSFgP2VzXKHQhGM+ZGIiMKBUu4A/Imvs7MzcMxutweulvaU7BwOBwwGAyRJ6va40x/bX0lJ4dfrlJISI3cIIeFc20GnU8MtCeiwtyIlQYfRmQndjmu1KngkwOBFn7cHcu5w3HbZXCEf40Bvn8vjrHYXDAbNkMcSFwuMSDHg+6Mm/McNRghCeIzV4e8In3Buh1DIj0D45chw/j8fSufaDkqlCJVKCaVSRGKi8czjogJKMTRu9+dcACEfYzBuAxieeE/7nIQD/o7wOeffEUMcx4DFxcUhJSUFVVVVgSuv1dXVgWE6ubm5qK6uDpxvt9tx8uRJ5OTkQJIk1NfXw+FwBK6wVlVVdRvi0x8tLRZ4vdIQvaPhl5ISg6YmLuBxru0gigLsdiesVida2h0YnWpEU1NHt+MOgwoOhwtWa2eft0UB/T53OG4DvtcP5RgHevtc4gUwbHFlJOrxzaFGfF/egIyk0O+l5u8In9PbQaEQwqoQCoX8CIRXjuRn32cw+dHt9sDlcsPt9sBkssDjkbof93jh9oTG7f6cCyDkYwzGbQDDE2+Xz0k44O8In8Hkx5DY53Hx4sX4y1/+ApPJhNraWrz66qtYvHgxAKC4uBjbtm1DSUkJnE4nnnvuOeTn5yMrKwvZ2dmYMGECXnjhBTidTuzatQvbtm1DcXGxzO+IwoFXktBucSIz1cjVw+isMv+9oNJ3lS19nEk0tJgfSS5cXZOIeiJ7zyMAPPDAA3j66adRXFwMr9eLG2+8ETfffDMAIC8vD08++SRWrVqFhoYGTJs2DatXrw48ds2aNXjsscdQVFSEhIQEPPHEExg/frxcb4XCiNXugleS0OnyYOueGqQlhs+QRAoug06F0alGfFfZjMtnjZY7HIoizI8kF//qmqZ2B7JGxHJ1TSICIFPxOGvWLOzZsyfwvUajwapVq7Bq1aoez1+0aBEWLVrU430ZGRn429/+NixxUmTrsPmGfGrUItotThj1XD2MejdtXDI+/Oo4rA4XDFqV3OFQhGJ+pFDiX10zIVbb98lEFBVCYtgqkRzMNicEAHEGtdyhUBiYPi4ZXknC91UcukpERETRicUjRa0OmwuxRnVgFTKis8keEYsYvQoHOO+RiIiIohT/aqaoZbY6kcShONRPCkHA1JwkfF/VAo/XK3c4REREREHH4pGiktvjhdXhRmKsRu5QKIxMy0mG1eFGZW273KEQERERBR2LR4pKZqtvcZxE9jzSAEzKSoSoEHDgKIeuEhERUfRh8UhRqc3C4pEGTqdRYvyoeBzgojlEREQUhVg8UlRq7eiEQuBKqzRwU3OSUNdkRUu7Q+5QiIiIiIKKxSNFpTZLJ4x6FRQK7npMAzM1JwkAcOBos8yREBEREQUXi0eKSm0WJ2L07HWkgUtP1CM1XofvjrZAFIXAFxEREf2AOTIyKeUOgCjYbA43bA43xqQZ5Q6FwpAgCJiSk4QvvqvHzu9Pwd7pQoxeg1kTU+HxSHKHR0REJDtRFLB+RxVMZgcSY7W4bk42c2SEYM8jRZ26ZgsAsOeRztm0nCQ43V5U1rai3eJEh61T7pCIiIhCisnsQHObHSYz1wiIJCweKerUNloBALF6lcyRULjKGx0PtUqB2iar3KEQERERBQ2LR4o6dU0WKEUBOg1HbVP/CAAUih/mbqiUIiaNTURdsxWSxGE4REQUnQShe36kyMe/ninq1DZZEW/UQBD4S476x6BXY1dZA8yWzsD8xmm5ydh3pBkWuwvxMRq5QyQiIgq6+Bgt3t1+FKZ2zm2MFux5pKgiSRJqmyyIN3K+Iw1Mh9XZbX7jtFzflh0NJrucYREREcmKcxujC4tHiipmmwsdNvYU0eAlxmqRYFSjodUmdyhEREREQcHikaJKXZNvpdV4I4tHOjf++Y8KhYCRKUaYzJ1wujxyh0VERCSrrvMfFQpODYpUnPNIUaXu36tjxhvVcLq8MkdD4cg//1GnViEz1YDSahPqW9j7SERE0a3r/MesEbG+q60UcVg8UlSpabQgRq+CVi2yeKRz1mF1wuuVkByvg0qpCFyUICIiimb++Y8JsVq5Q6FhwmGrFFVONHZgTHoMV1qlIaEQBKTG61DXbIWXW3YQERFRhGPxSFHD7fGirsmKMWkxcodCESQtUYdOpwfV9Wa5QyEiIiIaViweKWrUN1vh8UoYnWaUOxSKIKnxOggA9h9pljsUIiIiomHF4pGixokG30qro9nzSENIrRKRmqDDtxVNcodCRERENKxYPFLUONHYAbVKgfREvdyhUIQZlWZEXZMVDSauukpERESRi8UjRY2aBgtGpRi59xANuVGpvqHQe9n7SERERBGMxSNFBUmScKLRglEcskrDwKhTYUx6DItHIiIiimgsHikqNLc7YO90c7EcGjbn5aXgaL0ZrR2dcodCRERENCxYPFJUONHQAQAYncqeRxoe5+WlAAD2H2HvIxEREUUmFo8UFU40WCAIQGaKQe5QKEKNTDYgLUHHoatEREQUsVg8UlSoabQgI8kAtUqUOxSKUIIgYMb4FBw+0QaH0w1RFCCKXJyJiIioK39+ZI4MT0q5AyAKhuMNHcgbFS93GBThZoxPwf+WnMD6HVUYkaxHjF6DWRNT4fFIcodGREQkO1EUsH5HFUxmBxJjtbhuTjZzZJhh8UgRTRQFdNicaO3oxGiutErDLGtELOKNalTUtMGg5a9XIgptXXt++Ac8BYvJ7EBzm13uMOgc8a8biliiKKCkrBEVNSYAwNgMFo80vBSCgPPyUrB9Xz2mZCfKHQ4RUa966gEiIuoL5zxSROuwdaK+2QbAVzyKogCFgmPsafjMmpgGj1fCqRab3KEQEZ2VvweotcMBhYL5kYj6xp5HinjtVicMOiUOHmuF2dKJtEQjBIEJkoaOAAT+6MobkwC9VonaZismsfeRiMJAfIwW724/ijiD2vcLjWgICcIPOZIXKMIfi0eKeGaLE8lxOnRYnWi3OGHUO+UOiSKMQa/GrrKGwMWJrIxYlFWb4HC65Q6NiKhfTGYHJE57pGHgvzhhancga0QsL1CEOQ5bpYjmcnvRYXchOU4rdygU4fwXJ6wOJ7IyYiABOH7KIndYREREsvMPkW6z8AJ+uGPxSBHN1OEAAKQm6GSOhKJJQowGMToVqurNcodCRERENGRYPFJEa2n3F496mSOhaCIIAkalGdHc7kBtE3sfiYiIKDKweKSI1tzeCZ1GhJ577lGQjUoxQiEAn++vlzsUIiIioiHB4pEiWku7AwlGjdxhUBTSqEVkphqx8/tTcLm9codDRERENGgsHilima1OWOwuxMeweCR5jMuMg8Xuwr4jTXKHQkRERDRoLB4pYlWf9C1WEs+eR5JJepIeKfE6bN1TK3coRERERIPG4pEiVlW9GQKAeKNa7lAoSikEAQtnjkJlXTuO1rfLHQ4RERHRoLB4pIh1tM6MOKMaSpEfc5LPxVMzoNOI+GR3jdyhEBEREQ1KyPxVvW7dOkyaNAkFBQWBr40bN8LpdOKxxx7DzJkzMXv2bKxdu7bb4zZv3owFCxZg+vTpuPvuu9HS0iLTO6BQ4pUkHK1rR0o893ckeek0SsyZNhJ7Djehqc0udzgUppgjiYgoFIRM8VhWVoY77rgD+/btC3z96Ec/wpo1a1BdXY1PPvkE69atw8aNG/Hee+8BACorK/Hoo4/iqaeeQklJCcaMGYMVK1bI+0YoJNQ3W2HrdCMlXit3KERYcP4oKBQCNu08JncoFKaYI4mIKBSETPF48OBB5Ofnn3F848aNuPfeexEXF4fMzEwsXboUb7/9NgDggw8+wLx581BYWAiNRoOVK1di7969OHbsWJCjp1BTWeubX8aeRwoFCTEazJsxEjtLT+KUySZ3OBSGmCOJiCgUhETx6PF4UF5ejvfffx8XXXQRFixYgL/+9a9ob29HU1MTcnNzA+dmZWWhoqICgO+qak5OTuA+nU6HjIyMwP0UvY7UtiPWoEKMXiV3KEQAgCtnj4FKqcB7X1QBAERRCHwRnQ1zJBFFG+bI0KWUOwAAMJlMmDx5Mq699lr8+c9/xtGjR7F8+XI4nU4AgFb7w9BDnU4Hh8MBALDZbNDpuvcsabVa2O0Dm1eUlGQc5DsIvpSUGLlDCAm9tUP1KTMmZSdDr9fAAwW0WhU8EmDwYshvD+dz9+e2y+YK+RiD0aZWuwsGgyakYtTpVEhMNP77swpcOycX72ytwHWXOdHQYoPF7oRRp8alhaOC9rMRbSKhHZgjByYS/s+HQn/aQakUoVIpoRQVUIpi4N+ux0L5dn/OBRDyMQbjNoDQi1cpBnLk6V7bdBAmswOJsVrccfWkofvBAH9H+J1rO4RE8ZiSkoI33ngj8H1+fj5++tOf4vPPPwcAdHZ2Bu6z2+3Q6/UAuidJP4fDAYPBMKDXb2mxwOuVzjX8oEtJiUFTU4fcYciut3Zot3TiVIsNc6ePgN3uhNXqhMOggsPhgtXaOeS3RQHD9tz9uQ34Xj+UYwxGmwIIuRiVggSTyQKPx/f75dKpGdj6zXH8+Z39uGRaBjpsLsQZ1d3OGc6fjWhzejsoFELYFUIAc+RA8LPv0592EEUBbrcHLpcbbo8Xbo8n8G/XY6F8uz/nAgj5GINxG0Doxev29Jj/RFFAo8mK5jZ7r+cM589GNBhMfgyJYatHjhzBiy++2O2Yy+WCRqNBSkoKqqqqAserq6sDQ3Ryc3NRXV0duM9ut+PkyZPdhulQ9Dny7/mO40bFyxsI0Wk0ahE3XzYONY0WHD7RJnc4FCaYI4mIKFSERPEYGxuL1157De+88w68Xi9KS0vxj3/8A0uWLMHixYvxl7/8BSaTCbW1tXj11VexePFiAEBxcTG2bduGkpISOJ1OPPfcc8jPz0dWVpbM74jkVFHTBrVSgbHpHJZA8hLgu5rXdd7GjPEpmJabhP1HmtFhc8obIIUF5kgiikSCcGaOpNAXEsNW09LS8NJLL+GZZ57BU089hYSEBCxfvhyXX345Lr30Ujz99NMoLi6G1+vFjTfeiJtvvhkAkJeXhyeffBKrVq1CQ0MDpk2bhtWrV8v8bkhuh0+0IjczLjDGn0guBr0au8oaYLZ0IkavwayJqfB4gDuvzMevX/4K+440Y0TywIYQUvRhjiSiSBQfo8W724/C1O6b23jdnOwhncJBwyMkikcAKCoqwoYNG844rtFosGrVKqxatarHxy1atAiLFi0a7vAoTJhtTtQ2WbEkP03uUIgAAB1WJ9ot3XsY42M0mD0xDZ9/dxL7K5uxcOZomaKjcMEcSUSRyGR2oLltYIt4kbzYNUMRpeLf88gmZSVCoeAQCApdY9JjMCbNiIPVrdh/pFnucIgoSviHCDJHEtG5CJmeR6KhUF7TCqWoQFV9O+xOLwSByZFC1+TsRJhtLvz1g4NYdcf5SI7TdZv3weE7RDSURFHA+h1VMJkdyBoR65uYTRRG/DmS+VE+7HmkiFJ2rBWpCVp02FywOrgYCYWOrovn+K/4iwoFLpmWAa8k4eX3DkKChJKyRmzdU4OSskYuIEBEQ84/TLDNwhxJoaPr4jm99Yr7L36s31HF/Cgj9jxSxGizdOJkiw0zxifLHQrRGbounpOWaAz0isca1LireCLWrP8eb289gvQk/RlzJImIiCJZ18VzztYrbjI7er6DgoY9jxQxDlabAADpSXqZIyHqmX/xnNN7xQsnpGJB4Sh8sqcWx09x82IiIoo+7BUPDyweKWJ8X9WCOIMaiTEauUMhGrAfX5qDnJGx+Lq0ARa7S+5wiIiIiM7A4pEigiAAB4+ZMCUniYvkUFhSigrct2QKFApgz+FGuD1euUMiogjBFVaJaKiweKSwJ4oCNu08BqvdjXijhsUjhZWuC+mkxOtw4ZQMmG0u7D7UKHdoRBQB/IuMvLKpDFt213CFVQor/VlIh4KLC+ZQRDhS2wYASEnQcvlmCiunL6STmWrEuMw4HKltx5cHTqJoUrrcIRJRmPPPJUuI1codCtGA9LiQDv/MkxV7Hiki1DdbkRCjgVbN6yEUfk5fSGfC6HikJerwP/97GLWNFpmjIyIikg8X0gktLB4p7JnMDrSYO5GWoJM7FKIhIQgCLp6aAZ1GiZfeK4W90y13SEREREQsHin8fVveBAAYkWSQORKioaPTKLHsR5PR0GrD/3x0GJLEcTpEREQkLxaPFPb2lDcizqCGUa+SOxSiISMAmJSViOvm5OCbQ43Yvr8usGIiERFRtOq6iM7pXzT8OEGMwprZ5kT5iTZMzkqUOxSiIeVfSEerViB7RCze3FKBky02jB+VgFkTU7kwFBERRaXTF9FptzphancgMVaL6+ZkMz8OM/Y8UljbV9EESQJGp8XIHQrRkOuwOmG2ujB3xkjEGtTYvq8OtY1mucMiIiKSVddFdPy3TWaH3GFFBRaPFNZ2H25EaoIOCTFquUMhGjYalYh552VCoRDw6d56mK1ccY6IiIiCj8Ujha2mVjsOHWvFBZPTIQgc506RzahTYVZ+GuydbqxedwAut0fukIiIiCjKsHiksLV9bw0kABdOyZA7FKKgSIjR4MIp6aisbcdLG0vh9ni73c8FA4iIiM7E/Dh0uGAOhSVJkrBtdw3GZ8Yhlfs7UhQZkx6D2zJi8fePyvHXTWW4Z/FEiAoFRFFASVkjOmydSEk0Ylp2AhcNICKiqCeKAtbvqILJ7EBqogHXXDiG+XEQWDxSWKo6aUZdkwU/u2KC3KEQBd1l52Wi0+nBPz+thFqpwJ1X5UOEgA5bJ9otTuh0nBNJRETk519UR6kU5Q4l7LF4pLC0fV8dtGoR509IlTsUoqAS4Nvf6sqiMXC5vdjweRXUKhE/uyJP7tCIiIhk49//0Y+9i8ODxSOFHbPNiZKyRiycNRo6DT/CFF38+z+aLZ1IS9TjqqIx+PDr49CqRSTHaeQOj4iISBZd93/kno/Dh395U9j5fH893B4vii/KhigK3a4yEUWDDqsT7Rbf0NQfX5oDh9ODj0pOYEp2IrIyYmWOjohCgX9xEOZIiib+4ak0fFg8Ulhxe7z4bF8dJmUlorKmDU0mC9ISjdyqg6KWIAi4ef44uNxefP6d78JKeopR7rCISGb+BUKyRsT6xrsTEQ0BbtVBYWXXwQa0dnRi4fmjYLH7el+sDi4OQtHJP/9RpVRgaXE+xmbE4NDxNpQebZE7NCKSmb8Hps3CHEnRxz//kSPUhh57HilseLxe/OvrYxiTHoOC8cn4srRB7pCIZNV9/qMRF03JgKPTg6++P4m8UXG4eOoIuUMkoiDiPnZEPl3nP7L3fWixeKSw8c2hRjS22jG3YAQOHG2VOxyikOCf/2jUO6FQCDgvLwUHjrbg9c2HoRQVKJqULneIRBQEXfeyyx2VwD+WKer5e98TYrVyhxJROGyVwoLb48X7X1YjIUaDGJ2KQ1WJeiEqBCyYNRp5Y+Lx6r8O4dvyRrlDIqIg8f+x3G7plDsUIopQLB4p5ImigG17a9HYaseM8clcHIeoD0pRgRU3TEPWiBj89/sHse9IE4ezEUUoUeS8LqJz5f/5YY7sPw5bpZAmigI+21uHDdurkD0iFiNTjGjr4BVVor7otSr8+uYCPPv2fvx5w/dYeP5o/GTBOO55RRRBug5V5bwuor75F9IBfP+++9lRmMzcF3IgWDxSyPv8u3q4vV7MmpgmdyhEYcGgUwUW0rly9li8tbUcW3afQG5mLM4bnyp3eEQ0hDivi6j/4o2abgvpmDq4L+RAcdgqhbS9FU2oqjdj3Mg4xMdo5A6HKGz4F9JxeTyYXzgKyXFavLzxID7/rl7u0IiIiGTDbWwGh8UjhSyL3YXXNx9GglGN8aPi5Q6HKGyplApcdl4mJmUl4PX/PYwNnx+FJHFoDhEREQ0Mi0cKSV6vhLUfHITF7sIFU9K5EADRIKmUCvzyhmm4ZFoG/vXVcbz64SFIkLhIABER0Wm4kE7vOOeRQtL6z4/iYLUJd1w5AW6PF+0cWkA0KAIAtUrEnVflIzVBj3Xbj+JIbRuumD0Wl503kosEEBFR1OJCOv3H4pFCzqd7a/G/u05gbsEIzDsvE1u+OSF3SERhz6BXBxbRGZsRh4unZeCr70/hn58eQXqiDhNGJ8gdIhH1k783hKNyiIZGfIyWC+n0E4tHCilflZ7EG1sqMDY9BiOTDfiu0sR9HYmGiH8RHaPeiayMWCgVAvZWNOOZ/7cPiy/KQvEFYyAqOJuBKBR1LRj9vSLcnoNo6HDl4v5h8UghQZIk/G/JCazbfhRj0mJw4ZR0dNhciHVwuCrRcInRq3HF7NE40WjB+19WY39lM+68Mh+jUo1yh0ZEXZy+n6O/V4R/5BJRsPESM8nO5nBh7QcHsW77UYzNiMGiWaMgivxoEgWDWqnAvddMwn1LJsNkduD3r+/Gxs+r4HJ75Q6NiLrg9gJEweefC8nFc37AnkeSjSRJ2FvRjLe2VaCtw4nr5+ZAq1b8u3D0yB0eUVQw6NUoOdQIs9WJ6+fmoqKmFZu+OoaSQw24bk4OCvNSOHSciIiiUte5kEmxWlx/aQ68Xt/iOdG6iA6LRwo6ryShtKoFm78+joradoxMMeA/lkzBuFHxXByHSAb+uZAjU42YPj4FWrWIfUea8fJ7pcgZGYubLhuHnBFxcodJFHW4MA6R/LrOhWQhyeKRgkSSJNQ1W1FyqAHflDWiqc2OhBgNbrs8D3qNCtUnzbB1etnDQSSzDqsTBq0KP56Xi4oTbdh18BSe+Pu3yB+TgCtnj8HEsQn8OSUaYl2Hw/n/CD19niMXxiGSX0+FZLRt58HiMcr1lLDOdnwgmtvtqKxrR0VNG8qOtaKx1Q5BADJTjLj32kk4f0Iq1CoRW745EVgBkohCg0IQMCrVCJ16JBpbfT/Lz/1zP7IyYnD5rNEonJAKpaiImmRJ0Wk4c2RPq6d27c1QKASu/kgUwvw/n133iDxdJOZIFo9RxJ+oOp0e3we+3YGSQ41oarPB6fJCrVKgw+aC1e6Cw+mBJEkQBAEatQidWgmdRgmdRvT9q1ZCr1NCr/Edd3u8sHd60NxuR1OrHY1tdnTYXAAAlVKBzFQjZo+IRZxBjeyRcVAoBGzfV4e0RCN7MYhCmFJU4PyJaSjMT8X+iiYcPtGOl987CJ36MCZlJeGWheMQZ9DIHSbRoPTV83d6UddTsdf1sWd7TuDM7Ta6rp7ada859jYShb7T94hstzojukcyIorHw4cPY9WqVSgvL8eoUaPwxBNPYOrUqXKHNSz8iUcUhV4/jF5JgsvlhcPpRlObAydbrGhotaHsWCtaOzrRYXPBK3VJcAoBsQY1tBoRSoUCo1Jj0OnywOnywKhXweuVYHW44PF64XQLaLPYYO90w2J3dVuRUQCQFKeFVi0iPVGP8/NjoNeIEABkpsXAanehraMTQPf95ogo9NkcbqQm6DF9fAoqatpwsMqEPeWN2FvRhILxyZgzfQQmjE6AkislhxTmx/49rrcisachar0Ve30VmAB6LRi7Ym8jUfjp+nPb2nFmj2QkFZBhXzw6nU4sX74ct912G9544w1s2bIFS5cuxWeffQajMXz3KnN7vGizdsJsdaLV7ITZ5kSHzYkjte2wd7rh9kjQa0Q4nB50ujxwub3odHnQ6fTA2cMS+0pRgFGnQlKcFrmZcVCrFBiTFge3x4NOp7tbcTcy1djrbYVCgNnSibREIxpbrWg1O5CSoIcoCrA5XMhIikFjq/WMxxJRZBAEAZkpRhi1KsQY1Cg7ZkJpVQu+LW+CXqPEtNwkzBifinGj4hCrV8sdblSL1PwI/FAont5LaLa5EKtXdbva39vy+l3/mOutSOza89dbUdffAjPOoOb+jERRxN8jCQkR1QMZ9sXjN998A5fLhZ/97GcAgKuuugpvvPEGNm/ejBtuuEHe4Lrw995Z7C7YOt0wW51otzoD/7ZbnN2Odbp63qpCo1JArVJCrRSg0+gQa1BDoxZhtjohSRJiDRoIAuDxeGHQqTEzPxVpiTqkJeixdU8NjHp1oKBLiPXddvbyWj05vcfQP6zVanehw+pCrIE9iUTRItagxpTsJIxI0sPucMPllbC3vAlfH2wAACTHaZGVEYuMJD0SY7VIiNEg3qiBTiNCq1ZCqxbZUzmMwiU/nq6vvdT8vXoCcEYvYbvVBY/HE7jaf/rwUP9wstN7CbsaTM/f2QpMKTL+biSiATCZHRAQWXMiw754rKysRE5OTrdj2dnZqKio6PdzDGYJbKfbi8/21cJsccLlkeBy+3oBnW4vXC4vnG4PrA437A4Xevt4aFQiDDrfVfykeC3ijb4CUACQEq+H0aACJAnpyYbA6xi1KohKATa7y9dFbnagw+pEcrwenS534LaoFNDY5oDLC6QlGaBRKWHQqaDTKJEYqwvJ2/2NUQIgSFLYxDtct/2fh1COMRhtGuzPQyi3aYJRC1FAUOP1/745Pz8NDqcbtU1WnGyxwWR24ERDR6+//5RKBTQqEVq1CI1KhObf/6pEBVRKBZSiAiqVAkmxWlw4OX3Ac6S7/n6Ptu0OhiI/AoNvt4FsrK1QCNj5/Sl02JxISzTA1ulCh/XM20aDCjE6Nb4uawgcG50RA4vNjYwkfbfjRoMKbo8Xeq0KHkmC2+1FerLhjMfGGtQYkWxAjEGFWP3Q347RqYftuU+/nZFsgE4rhnSMwWrTc22LcPx/P9tte6cHeo0YNvEOV4xyfB5O/13l/10Wo1fjwinpgTnTg3EuRei55sewLx5tNhu02u5X+HQ6Hex2e7+fIyHBMKgYfnL5xEE9noiIhkdSUngPzxyMociPwOBz5EBdcWF2UF+PiCganWt+DPvxQnq9Hp2d3efU2e126PV6mSIiIiKSH/MjERENtbAvHnNyclBdXd3tWFVVFXJzc2WKiIiISH7Mj0RENNTCvnicNWsWJEnC66+/DpfLhQ8//BDl5eVYsGCB3KERERHJhvmRiIiGmiBJ4b/+V0VFBVatWoXDhw8jMzMT//mf/4mioiK5wyIiIpIV8yMREQ2liCgeiYiIiIiIaHiF/bBVIiIiIiIiGn4sHomIiIiIiKhPLB6JiIiIiIioTyweiYiIiIiIqE8sHsPE4cOHceONN2L69Om4+uqrceDAAblDCroDBw50WyXQ6XTisccew8yZMzF79mysXbtWxuiG386dO7FkyRLMmDEDCxYswNtvvw0g+toBAD777DNcffXVKCgowPz586O6LcxmM+bOnYsNGzYAiM42WLduHSZNmoSCgoLA18aNG6OyLaIVcyRzJHOkD/Njd8yRw5AjJQp5nZ2d0qWXXiq99tprktPplP71r39JhYWFUkdHh9yhBYXX65Xeeecd6bzzzpPOO++8wPFnn31WuuWWW6S2tjappqZGWrRokbRx40b5Ah1G9fX1UkFBgbRlyxbJ4/FI3333nXT++edLn3/+eVS1gyRJUkNDgzR58mRp+/btkiRJUmlpqTRlyhSptLQ06tpCkiTpl7/8pTRhwgRp/fr1kiRF18+F3+9+9zvpmWeeOeN4NLZFNGKOZI5kjvRhfjwTc+TQ50j2PIaBb775Bi6XCz/72c+gUqlw1VVXITc3F5s3b5Y7tKB48cUX8dZbb2HZsmXdjm/cuBH33nsv4uLikJmZiaVLlwausEWauro6FBcXY8GCBVAoFJg6dSpmzpyJvXv3RlU7AEBqaiq+/vprzJkzB16vF21tbRBFEQaDIeraYuPGjbBYLBg/fny3Y9HUBgBw8OBB5Ofnn3E8GtsiGjFHMkcyR/owP3bHHOkz1DmSxWMYqKysRE5OTrdj2dnZqKiokCmi4LrpppuwYcMGTJ48OXDMbDajqakJubm5gWNZWVkR2yaFhYX4/e9/H/i+ra0Ne/bswcSJE6OqHfyMRiPsdjumTJmCO++8E7fccgsSExOjqi1qamrw5z//GU8++WTgWLT9XACAx+NBeXk53n//fVx00UVYsGAB/vrXv6K9vT3q2iJaMUcyRzJH/oD50Yc50mc4cqRyOAOmoWGz2aDVarsd0+l0sNvtMkUUXGlpaWccs9lsANCtXXQ6HRwOR9DikktHRweWLVuGadOmYdKkSQCisx00Gg327duH8vJy/PznPw+0QTS0hcfjwa9//Ws89NBDSElJCRyPxp8Lk8mEyZMn49prr8Wf//xnHD16FMuXL4fT6QQQXW0RrZgjmSO7Yo6M7vwIMEd2NRw5ksVjGNDr9ejs7Ox2zG63Q6/XyxSR/HQ6HQB0a5doaJPq6mosX74cubm5ePbZZwM/5NHWDgCgUCigVqsxZcoU3HDDDSgtLQUQHW3x0ksvISsrCwsXLux2PBp/LlJSUvDGG28Evs/Pz8dPf/pTfP755wCiqy2iFXPkmaLxdwHAHOkXzfkRYI7sajhyJIethoGcnBxUV1d3O1ZVVdWtqznaxMXFISUlBVVVVYFj1dXVEd0mu3fvxg033ID58+fjxRdfhEajicp2+Oabb7BkyZJux5xOJ2JjY6OmLT788EN8/PHHKCwsRGFhISoqKvC73/0OL7zwQtS0gd+RI0fw4osvdjvmcrmg0Wiiri2iFXPkmaIxNzBHMj/6MUf+YDhyJHsew8CsWbMgSRJef/113HLLLdiyZQvKy8uxYMECuUOT1eLFi/GXv/wFeXl5sNlsePXVV3HbbbfJHdawOHHiBO655x6sWLECt956a7f7oqkdAN9Vs4aGBrz22mu47bbb8N1332H9+vX485//jOTk5Khoi48++qjb99dccw1uv/12LFmyBHq9PirawC82NhavvfYa0tPTcf3116OsrAz/+Mc/8NhjjyE3Nzeq2iJaMUf2LJpyA3OkD/OjD3PkD4YlRw75erA0LMrLy6WbbrpJmj59ulRcXCx99dVXcocUdLt27eq2DLnD4ZB++9vfSkVFRdKsWbOk559/XvJ6vTJGOHyefPJJafz48dL06dO7ff3pT3+KqnbwKy0tlW666SZpxowZ0lVXXSV9/PHHkiRF12eiq8WLFweWIY/GNvjqq6+kH/3oR9L06dOlSy+9VHrjjTckSYrOtohWzJHMkcyRPsyPZ2KOHNocKUiSJA1/3UtEREREREThjHMeiYiIiIiIqE8sHomIiIiIiKhPLB6JiIiIiIioTyweiYiIiIiIqE8sHomIiIiIiKhPLB6JiIiIiIioTyweiWQ0b9485OXl4eOPPz7jvra2NkyaNAmzZs0a1Gts2LABeXl5vX6dvqEyERFRKGCOJAo9SrkDIIp2KpUKW7duxaJFi7od//TTT+HxeAb9/FdeeSUuvvjiwPfFxcVYvnw5rrzyysDrExERhSLmSKLQwuKRSGYzZ87Ejh074Ha7oVT+8CO5ZcsWTJ8+HdXV1YN6fq1WC61W2+1YTEwMUlJSBvW8REREw405kii0cNgqkcwuvPBCuFwu7N69O3DMarWipKQE8+fPDxw7cOAAbr/9dhQUFGDKlCm4/vrrsXfvXgDAe++9h/z8fHz//fcAgI6ODsyZMwd//OMf+xXDq6++iksvvRQFBQW4+eabsX///sB9Dz/8MB5//HE88sgjmD59Oi655BJs3rwZW7Zswfz581FQUIAHH3wQTqcTALBmzRrce++9ePzxx1FQUIBLLrkEr7/++iBbiYiIohFzJFFoYfFIJDONRoOLL74Y27ZtCxzbsWMHJk+ejMTERAC+RHn33XcjPz8f77//Pt555x0YDAasWrUKAHDttdfikksuwapVq+D1evHkk0/CaDRixYoVfb7+22+/jb///e9YtWoVNm7ciDlz5uD2229HTU1Nt3NGjx6NTZs24aKLLsKjjz6KV199FatXr8YLL7yAbdu24YMPPgic/+WXX+LkyZN455138OCDD+L555/Hxo0bh6rJiIgoSjBHEoUWFo9EIWDhwoX49NNPA99v2bKl2/wOh8OBu+66C7/61a8wevRo5Ofn4yc/+QkqKysD5/zud7/D8ePH8X/+z//Bpk2b8Kc//QlqtbrP1167di1WrlyJuXPnYuzYsbj33ntx3nnn4f/9v/8XOGfMmDFYtmwZRo0ahZtuugk2mw0PPPAAJk2ahLlz52Lq1KndYtFqtfjjH/+IcePG4dprr8WNN97Y7fmIiIj6izmSKHSweCQKAXPnzkVjYyMOHTqEzs5OfPHFF1iwYEHg/qSkJPz4xz/Gm2++iUceeQQ333wzHnroIXi93sA56enpePDBB7Fp0ybccccdmDRpUp+va7VaUV9fj8ceewwFBQWBr5KSkm7zSEaPHh247Z8bMmrUqMAxtVodGJIDAPn5+TAajYHvp06diiNHjgywVYiIiJgjiUIJF8whCgFGoxFFRUXYunUrJk2ahHHjxiEtLS1wf2NjI5YsWYKcnBxccskluPrqq9HS0oJf/epX3Z7n8OHDEEURJSUl8Hq9UCjOfn3In1iffvppTJw4sdt9XRcQ6Gm1OUEQen1eURTPeJ2+YiEiIuoJcyRR6OAnlShELFy4EFu3bsUnn3yChQsXdrvvk08+gVqtxuuvv46lS5figgsuwKlTpwAAkiQBAL7++mts2LABa9euxfHjx/s1Ad+/olxDQwPGjBkT+HrjjTfwxRdfnPN7OXLkSLerrN9//z0mTJhwzs9HRETRjTmSKDSweCQKEZdddhmOHDmCjz/+uNtwHACIj49Hc3Mztm/fjtraWmzYsAEvv/wyAMDpdMJut+M3v/kNbr75Zlx88cVYuXIlVq9ejePHj/f5unfddRdeeuklbN68GTU1NXjppZfw5ptvIisr65zfS3NzM373u9+hqqoKGzduxDvvvIPbb7/9nJ+PiIiiG3MkUWjgsFWiEJGYmIgZM2bAZrN1mysBAFdccQX27duHhx9+GC6XC+PGjcPjjz+OBx98EKWlpfjoo4/gdDrxy1/+EgDw4x//GBs2bMCjjz6Kf/zjH2cdPnPbbbfB4XDgmWeeQXNzM8aOHYsXX3wR55133jm/l7y8PKjVaixZsgTJyclYtWrVGRs8ExER9RdzJFFoECR/fz4R0RBYs2YNPvvsM2zYsEHuUIiIiEIKcySFOw5bJSIiIiIioj6xeCQiIiIiIqI+cdgqERERERER9Yk9j0RERERERNQnFo9ERERERETUJxaPRERERERE1CcWj0RERERERNQnFo9ERERERETUJxaPRERERERE1Kf/H8eoScgCenZtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_and_stats(df, 'MaxTemp');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "503ec6f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3298755978938974\n",
      "0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_and_stats(df, 'Rainfall');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "889579eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.178685839459762\n",
      "4.8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_and_stats(df, 'Evaporation');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "926b69eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[16,8])\n",
    "plt.subplot(1,2,1)\n",
    "sns.lineplot(x=\"Sunshine\",y=\"Evaporation\",hue=\"RainToday\",data=df, palette=\"tab10\", linewidth=2.5)\n",
    "plt.subplot(1,2,2)\n",
    "sns.lineplot(x=\"WindSpeed9am\",y=\"Humidity9am\",hue=\"RainToday\",data=df, palette=\"tab10\", linewidth=2.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6525c15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[16,8])\n",
    "plt.subplot(1,2,1)\n",
    "sns.lineplot(data = df[\"WindSpeed9am\"], palette=\"tab10\", linewidth=2.5)\n",
    "plt.subplot(1,2,2)\n",
    "sns.lineplot(data = df[\"WindSpeed3pm\"], palette=\"tab10\", linewidth=2.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5e9c5065",
   "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>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</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>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MinTemp</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.734426</td>\n",
       "      <td>0.103195</td>\n",
       "      <td>0.371953</td>\n",
       "      <td>0.034238</td>\n",
       "      <td>0.174264</td>\n",
       "      <td>0.177211</td>\n",
       "      <td>0.172693</td>\n",
       "      <td>-0.235352</td>\n",
       "      <td>0.004478</td>\n",
       "      <td>-0.426344</td>\n",
       "      <td>-0.436021</td>\n",
       "      <td>0.038812</td>\n",
       "      <td>-0.002549</td>\n",
       "      <td>0.898419</td>\n",
       "      <td>0.700083</td>\n",
       "      <td>0.084283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxTemp</th>\n",
       "      <td>0.734426</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.073272</td>\n",
       "      <td>0.456160</td>\n",
       "      <td>0.317898</td>\n",
       "      <td>0.070428</td>\n",
       "      <td>0.018160</td>\n",
       "      <td>0.050366</td>\n",
       "      <td>-0.501884</td>\n",
       "      <td>-0.500240</td>\n",
       "      <td>-0.312112</td>\n",
       "      <td>-0.400023</td>\n",
       "      <td>-0.235095</td>\n",
       "      <td>-0.219365</td>\n",
       "      <td>0.880475</td>\n",
       "      <td>0.969255</td>\n",
       "      <td>-0.159418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rainfall</th>\n",
       "      <td>0.103195</td>\n",
       "      <td>-0.073272</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042732</td>\n",
       "      <td>-0.169965</td>\n",
       "      <td>0.127128</td>\n",
       "      <td>0.086886</td>\n",
       "      <td>0.059935</td>\n",
       "      <td>0.219381</td>\n",
       "      <td>0.248145</td>\n",
       "      <td>-0.158121</td>\n",
       "      <td>-0.118961</td>\n",
       "      <td>0.163492</td>\n",
       "      <td>0.137317</td>\n",
       "      <td>0.011615</td>\n",
       "      <td>-0.077631</td>\n",
       "      <td>0.234603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evaporation</th>\n",
       "      <td>0.371953</td>\n",
       "      <td>0.456160</td>\n",
       "      <td>-0.042732</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.283673</td>\n",
       "      <td>0.153174</td>\n",
       "      <td>0.151973</td>\n",
       "      <td>0.099995</td>\n",
       "      <td>-0.390936</td>\n",
       "      <td>-0.299077</td>\n",
       "      <td>-0.219422</td>\n",
       "      <td>-0.239795</td>\n",
       "      <td>-0.174877</td>\n",
       "      <td>-0.161345</td>\n",
       "      <td>0.433244</td>\n",
       "      <td>0.442518</td>\n",
       "      <td>-0.087342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunshine</th>\n",
       "      <td>0.034238</td>\n",
       "      <td>0.317898</td>\n",
       "      <td>-0.169965</td>\n",
       "      <td>0.283673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026508</td>\n",
       "      <td>-0.007986</td>\n",
       "      <td>0.025284</td>\n",
       "      <td>-0.332381</td>\n",
       "      <td>-0.433698</td>\n",
       "      <td>0.032959</td>\n",
       "      <td>-0.012212</td>\n",
       "      <td>-0.509821</td>\n",
       "      <td>-0.530869</td>\n",
       "      <td>0.192625</td>\n",
       "      <td>0.333755</td>\n",
       "      <td>-0.324472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <td>0.174264</td>\n",
       "      <td>0.070428</td>\n",
       "      <td>0.127128</td>\n",
       "      <td>0.153174</td>\n",
       "      <td>-0.026508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.580049</td>\n",
       "      <td>0.659395</td>\n",
       "      <td>-0.213636</td>\n",
       "      <td>-0.028974</td>\n",
       "      <td>-0.428559</td>\n",
       "      <td>-0.387102</td>\n",
       "      <td>0.044060</td>\n",
       "      <td>0.071561</td>\n",
       "      <td>0.149518</td>\n",
       "      <td>0.035615</td>\n",
       "      <td>0.224989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <td>0.177211</td>\n",
       "      <td>0.018160</td>\n",
       "      <td>0.086886</td>\n",
       "      <td>0.151973</td>\n",
       "      <td>-0.007986</td>\n",
       "      <td>0.580049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511056</td>\n",
       "      <td>-0.272426</td>\n",
       "      <td>-0.031433</td>\n",
       "      <td>-0.216574</td>\n",
       "      <td>-0.166739</td>\n",
       "      <td>0.004797</td>\n",
       "      <td>0.027688</td>\n",
       "      <td>0.131669</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.090667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <td>0.172693</td>\n",
       "      <td>0.050366</td>\n",
       "      <td>0.059935</td>\n",
       "      <td>0.099995</td>\n",
       "      <td>0.025284</td>\n",
       "      <td>0.659395</td>\n",
       "      <td>0.511056</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.144415</td>\n",
       "      <td>0.015382</td>\n",
       "      <td>-0.278000</td>\n",
       "      <td>-0.239963</td>\n",
       "      <td>0.032955</td>\n",
       "      <td>0.011937</td>\n",
       "      <td>0.161370</td>\n",
       "      <td>0.028053</td>\n",
       "      <td>0.087988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity9am</th>\n",
       "      <td>-0.235352</td>\n",
       "      <td>-0.501884</td>\n",
       "      <td>0.219381</td>\n",
       "      <td>-0.390936</td>\n",
       "      <td>-0.332381</td>\n",
       "      <td>-0.213636</td>\n",
       "      <td>-0.272426</td>\n",
       "      <td>-0.144415</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.658766</td>\n",
       "      <td>0.134725</td>\n",
       "      <td>0.179104</td>\n",
       "      <td>0.354885</td>\n",
       "      <td>0.276159</td>\n",
       "      <td>-0.473445</td>\n",
       "      <td>-0.494365</td>\n",
       "      <td>0.254116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity3pm</th>\n",
       "      <td>0.004478</td>\n",
       "      <td>-0.500240</td>\n",
       "      <td>0.248145</td>\n",
       "      <td>-0.299077</td>\n",
       "      <td>-0.433698</td>\n",
       "      <td>-0.028974</td>\n",
       "      <td>-0.031433</td>\n",
       "      <td>0.015382</td>\n",
       "      <td>0.658766</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022841</td>\n",
       "      <td>0.050728</td>\n",
       "      <td>0.395755</td>\n",
       "      <td>0.402547</td>\n",
       "      <td>-0.218447</td>\n",
       "      <td>-0.556343</td>\n",
       "      <td>0.438711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure9am</th>\n",
       "      <td>-0.426344</td>\n",
       "      <td>-0.312112</td>\n",
       "      <td>-0.158121</td>\n",
       "      <td>-0.219422</td>\n",
       "      <td>0.032959</td>\n",
       "      <td>-0.428559</td>\n",
       "      <td>-0.216574</td>\n",
       "      <td>-0.278000</td>\n",
       "      <td>0.134725</td>\n",
       "      <td>-0.022841</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.960091</td>\n",
       "      <td>-0.093613</td>\n",
       "      <td>-0.104736</td>\n",
       "      <td>-0.400294</td>\n",
       "      <td>-0.269214</td>\n",
       "      <td>-0.235220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure3pm</th>\n",
       "      <td>-0.436021</td>\n",
       "      <td>-0.400023</td>\n",
       "      <td>-0.118961</td>\n",
       "      <td>-0.239795</td>\n",
       "      <td>-0.012212</td>\n",
       "      <td>-0.387102</td>\n",
       "      <td>-0.166739</td>\n",
       "      <td>-0.239963</td>\n",
       "      <td>0.179104</td>\n",
       "      <td>0.050728</td>\n",
       "      <td>0.960091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.040244</td>\n",
       "      <td>-0.057315</td>\n",
       "      <td>-0.444519</td>\n",
       "      <td>-0.363721</td>\n",
       "      <td>-0.215812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud9am</th>\n",
       "      <td>0.038812</td>\n",
       "      <td>-0.235095</td>\n",
       "      <td>0.163492</td>\n",
       "      <td>-0.174877</td>\n",
       "      <td>-0.509821</td>\n",
       "      <td>0.044060</td>\n",
       "      <td>0.004797</td>\n",
       "      <td>0.032955</td>\n",
       "      <td>0.354885</td>\n",
       "      <td>0.395755</td>\n",
       "      <td>-0.093613</td>\n",
       "      <td>-0.040244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.563598</td>\n",
       "      <td>-0.125222</td>\n",
       "      <td>-0.240579</td>\n",
       "      <td>0.248103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud3pm</th>\n",
       "      <td>-0.002549</td>\n",
       "      <td>-0.219365</td>\n",
       "      <td>0.137317</td>\n",
       "      <td>-0.161345</td>\n",
       "      <td>-0.530869</td>\n",
       "      <td>0.071561</td>\n",
       "      <td>0.027688</td>\n",
       "      <td>0.011937</td>\n",
       "      <td>0.276159</td>\n",
       "      <td>0.402547</td>\n",
       "      <td>-0.104736</td>\n",
       "      <td>-0.057315</td>\n",
       "      <td>0.563598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.112000</td>\n",
       "      <td>-0.254231</td>\n",
       "      <td>0.291362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp9am</th>\n",
       "      <td>0.898419</td>\n",
       "      <td>0.880475</td>\n",
       "      <td>0.011615</td>\n",
       "      <td>0.433244</td>\n",
       "      <td>0.192625</td>\n",
       "      <td>0.149518</td>\n",
       "      <td>0.131669</td>\n",
       "      <td>0.161370</td>\n",
       "      <td>-0.473445</td>\n",
       "      <td>-0.218447</td>\n",
       "      <td>-0.400294</td>\n",
       "      <td>-0.444519</td>\n",
       "      <td>-0.125222</td>\n",
       "      <td>-0.112000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846549</td>\n",
       "      <td>-0.024960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp3pm</th>\n",
       "      <td>0.700083</td>\n",
       "      <td>0.969255</td>\n",
       "      <td>-0.077631</td>\n",
       "      <td>0.442518</td>\n",
       "      <td>0.333755</td>\n",
       "      <td>0.035615</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.028053</td>\n",
       "      <td>-0.494365</td>\n",
       "      <td>-0.556343</td>\n",
       "      <td>-0.269214</td>\n",
       "      <td>-0.363721</td>\n",
       "      <td>-0.240579</td>\n",
       "      <td>-0.254231</td>\n",
       "      <td>0.846549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.190279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RainTomorrow</th>\n",
       "      <td>0.084283</td>\n",
       "      <td>-0.159418</td>\n",
       "      <td>0.234603</td>\n",
       "      <td>-0.087342</td>\n",
       "      <td>-0.324472</td>\n",
       "      <td>0.224989</td>\n",
       "      <td>0.090667</td>\n",
       "      <td>0.087988</td>\n",
       "      <td>0.254116</td>\n",
       "      <td>0.438711</td>\n",
       "      <td>-0.235220</td>\n",
       "      <td>-0.215812</td>\n",
       "      <td>0.248103</td>\n",
       "      <td>0.291362</td>\n",
       "      <td>-0.024960</td>\n",
       "      <td>-0.190279</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                MinTemp   MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "MinTemp        1.000000  0.734426  0.103195     0.371953  0.034238   \n",
       "MaxTemp        0.734426  1.000000 -0.073272     0.456160  0.317898   \n",
       "Rainfall       0.103195 -0.073272  1.000000    -0.042732 -0.169965   \n",
       "Evaporation    0.371953  0.456160 -0.042732     1.000000  0.283673   \n",
       "Sunshine       0.034238  0.317898 -0.169965     0.283673  1.000000   \n",
       "WindGustSpeed  0.174264  0.070428  0.127128     0.153174 -0.026508   \n",
       "WindSpeed9am   0.177211  0.018160  0.086886     0.151973 -0.007986   \n",
       "WindSpeed3pm   0.172693  0.050366  0.059935     0.099995  0.025284   \n",
       "Humidity9am   -0.235352 -0.501884  0.219381    -0.390936 -0.332381   \n",
       "Humidity3pm    0.004478 -0.500240  0.248145    -0.299077 -0.433698   \n",
       "Pressure9am   -0.426344 -0.312112 -0.158121    -0.219422  0.032959   \n",
       "Pressure3pm   -0.436021 -0.400023 -0.118961    -0.239795 -0.012212   \n",
       "Cloud9am       0.038812 -0.235095  0.163492    -0.174877 -0.509821   \n",
       "Cloud3pm      -0.002549 -0.219365  0.137317    -0.161345 -0.530869   \n",
       "Temp9am        0.898419  0.880475  0.011615     0.433244  0.192625   \n",
       "Temp3pm        0.700083  0.969255 -0.077631     0.442518  0.333755   \n",
       "RainTomorrow   0.084283 -0.159418  0.234603    -0.087342 -0.324472   \n",
       "\n",
       "               WindGustSpeed  WindSpeed9am  WindSpeed3pm  Humidity9am  \\\n",
       "MinTemp             0.174264      0.177211      0.172693    -0.235352   \n",
       "MaxTemp             0.070428      0.018160      0.050366    -0.501884   \n",
       "Rainfall            0.127128      0.086886      0.059935     0.219381   \n",
       "Evaporation         0.153174      0.151973      0.099995    -0.390936   \n",
       "Sunshine           -0.026508     -0.007986      0.025284    -0.332381   \n",
       "WindGustSpeed       1.000000      0.580049      0.659395    -0.213636   \n",
       "WindSpeed9am        0.580049      1.000000      0.511056    -0.272426   \n",
       "WindSpeed3pm        0.659395      0.511056      1.000000    -0.144415   \n",
       "Humidity9am        -0.213636     -0.272426     -0.144415     1.000000   \n",
       "Humidity3pm        -0.028974     -0.031433      0.015382     0.658766   \n",
       "Pressure9am        -0.428559     -0.216574     -0.278000     0.134725   \n",
       "Pressure3pm        -0.387102     -0.166739     -0.239963     0.179104   \n",
       "Cloud9am            0.044060      0.004797      0.032955     0.354885   \n",
       "Cloud3pm            0.071561      0.027688      0.011937     0.276159   \n",
       "Temp9am             0.149518      0.131669      0.161370    -0.473445   \n",
       "Temp3pm             0.035615      0.008057      0.028053    -0.494365   \n",
       "RainTomorrow        0.224989      0.090667      0.087988     0.254116   \n",
       "\n",
       "               Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  \\\n",
       "MinTemp           0.004478    -0.426344    -0.436021  0.038812 -0.002549   \n",
       "MaxTemp          -0.500240    -0.312112    -0.400023 -0.235095 -0.219365   \n",
       "Rainfall          0.248145    -0.158121    -0.118961  0.163492  0.137317   \n",
       "Evaporation      -0.299077    -0.219422    -0.239795 -0.174877 -0.161345   \n",
       "Sunshine         -0.433698     0.032959    -0.012212 -0.509821 -0.530869   \n",
       "WindGustSpeed    -0.028974    -0.428559    -0.387102  0.044060  0.071561   \n",
       "WindSpeed9am     -0.031433    -0.216574    -0.166739  0.004797  0.027688   \n",
       "WindSpeed3pm      0.015382    -0.278000    -0.239963  0.032955  0.011937   \n",
       "Humidity9am       0.658766     0.134725     0.179104  0.354885  0.276159   \n",
       "Humidity3pm       1.000000    -0.022841     0.050728  0.395755  0.402547   \n",
       "Pressure9am      -0.022841     1.000000     0.960091 -0.093613 -0.104736   \n",
       "Pressure3pm       0.050728     0.960091     1.000000 -0.040244 -0.057315   \n",
       "Cloud9am          0.395755    -0.093613    -0.040244  1.000000  0.563598   \n",
       "Cloud3pm          0.402547    -0.104736    -0.057315  0.563598  1.000000   \n",
       "Temp9am          -0.218447    -0.400294    -0.444519 -0.125222 -0.112000   \n",
       "Temp3pm          -0.556343    -0.269214    -0.363721 -0.240579 -0.254231   \n",
       "RainTomorrow      0.438711    -0.235220    -0.215812  0.248103  0.291362   \n",
       "\n",
       "                Temp9am   Temp3pm  RainTomorrow  \n",
       "MinTemp        0.898419  0.700083      0.084283  \n",
       "MaxTemp        0.880475  0.969255     -0.159418  \n",
       "Rainfall       0.011615 -0.077631      0.234603  \n",
       "Evaporation    0.433244  0.442518     -0.087342  \n",
       "Sunshine       0.192625  0.333755     -0.324472  \n",
       "WindGustSpeed  0.149518  0.035615      0.224989  \n",
       "WindSpeed9am   0.131669  0.008057      0.090667  \n",
       "WindSpeed3pm   0.161370  0.028053      0.087988  \n",
       "Humidity9am   -0.473445 -0.494365      0.254116  \n",
       "Humidity3pm   -0.218447 -0.556343      0.438711  \n",
       "Pressure9am   -0.400294 -0.269214     -0.235220  \n",
       "Pressure3pm   -0.444519 -0.363721     -0.215812  \n",
       "Cloud9am      -0.125222 -0.240579      0.248103  \n",
       "Cloud3pm      -0.112000 -0.254231      0.291362  \n",
       "Temp9am        1.000000  0.846549     -0.024960  \n",
       "Temp3pm        0.846549  1.000000     -0.190279  \n",
       "RainTomorrow  -0.024960 -0.190279      1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f48eec7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's visualize it more\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize = (15, 8))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                 annot = True,\n",
    "                 linewidth = 0.5,\n",
    "                 fmt = \".2f\",\n",
    "                 cmap = sns.cubehelix_palette(rot=-.4));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3690959b",
   "metadata": {},
   "source": [
    "Since Pressure9am and Temp3pm is correlated more than 90% with other independent variable, we need tp drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e1f68214",
   "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>row ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Row0</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Row1</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Row2</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Row3</td>\n",
       "      <td>Albury</td>\n",
       "      <td>14.6</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>WNW</td>\n",
       "      <td>56.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Row4</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>W</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  row ID Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0   Row0   Albury     13.4     22.9       0.6          4.8       8.4   \n",
       "1   Row1   Albury      7.4     25.1       0.0          4.8       8.4   \n",
       "2   Row2   Albury     17.5     32.3       1.0          4.8       8.4   \n",
       "3   Row3   Albury     14.6     29.7       0.2          4.8       8.4   \n",
       "4   Row4   Albury      7.7     26.7       0.0          4.8       8.4   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... WindSpeed9am  WindSpeed3pm  \\\n",
       "0           W           44.0          W  ...         20.0          24.0   \n",
       "1         WNW           44.0        NNW  ...          4.0          22.0   \n",
       "2           W           41.0        ENE  ...          7.0          20.0   \n",
       "3         WNW           56.0          W  ...         19.0          24.0   \n",
       "4           W           35.0        SSE  ...          6.0          17.0   \n",
       "\n",
       "   Humidity9am  Humidity3pm  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  \\\n",
       "0         71.0         22.0       1007.1       8.0       5.0     16.9   \n",
       "1         44.0         25.0       1007.8       5.0       5.0     17.2   \n",
       "2         82.0         33.0       1006.0       7.0       8.0     17.8   \n",
       "3         55.0         23.0       1005.4       5.0       5.0     20.6   \n",
       "4         48.0         19.0       1010.1       5.0       5.0     16.3   \n",
       "\n",
       "   RainToday RainTomorrow  \n",
       "0         No            0  \n",
       "1         No            0  \n",
       "2         No            0  \n",
       "3         No            0  \n",
       "4         No            0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_drop = [\"Pressure9am\", \"Temp3pm\"]\n",
    "df.drop(cols_drop, axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "762969c8",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row ID</th>\n",
       "      <td>Row0</td>\n",
       "      <td>Row1</td>\n",
       "      <td>Row2</td>\n",
       "      <td>Row3</td>\n",
       "      <td>Row4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "      <td>Albury</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MinTemp</th>\n",
       "      <td>13.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxTemp</th>\n",
       "      <td>22.9</td>\n",
       "      <td>25.1</td>\n",
       "      <td>32.3</td>\n",
       "      <td>29.7</td>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rainfall</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evaporation</th>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunshine</th>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustDir</th>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDir9am</th>\n",
       "      <td>W</td>\n",
       "      <td>NNW</td>\n",
       "      <td>ENE</td>\n",
       "      <td>W</td>\n",
       "      <td>SSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDir3pm</th>\n",
       "      <td>WNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>NW</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity9am</th>\n",
       "      <td>71.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity3pm</th>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure3pm</th>\n",
       "      <td>1007.1</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>1010.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud9am</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud3pm</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp9am</th>\n",
       "      <td>16.9</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.8</td>\n",
       "      <td>20.6</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RainToday</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RainTomorrow</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0       1       2       3       4\n",
       "row ID           Row0    Row1    Row2    Row3    Row4\n",
       "Location       Albury  Albury  Albury  Albury  Albury\n",
       "MinTemp          13.4     7.4    17.5    14.6     7.7\n",
       "MaxTemp          22.9    25.1    32.3    29.7    26.7\n",
       "Rainfall          0.6     0.0     1.0     0.2     0.0\n",
       "Evaporation       4.8     4.8     4.8     4.8     4.8\n",
       "Sunshine          8.4     8.4     8.4     8.4     8.4\n",
       "WindGustDir         W     WNW       W     WNW       W\n",
       "WindGustSpeed    44.0    44.0    41.0    56.0    35.0\n",
       "WindDir9am          W     NNW     ENE       W     SSE\n",
       "WindDir3pm        WNW     WSW      NW       W       W\n",
       "WindSpeed9am     20.0     4.0     7.0    19.0     6.0\n",
       "WindSpeed3pm     24.0    22.0    20.0    24.0    17.0\n",
       "Humidity9am      71.0    44.0    82.0    55.0    48.0\n",
       "Humidity3pm      22.0    25.0    33.0    23.0    19.0\n",
       "Pressure3pm    1007.1  1007.8  1006.0  1005.4  1010.1\n",
       "Cloud9am          8.0     5.0     7.0     5.0     5.0\n",
       "Cloud3pm          5.0     5.0     8.0     5.0     5.0\n",
       "Temp9am          16.9    17.2    17.8    20.6    16.3\n",
       "RainToday          No      No      No      No      No\n",
       "RainTomorrow        0       0       0       0       0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ba628b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "# Let's make a function\n",
    "def label_encoding_df_cols (col_name, encoder, data_frame):\n",
    "    print(\"Unique values:\", data_frame[col_name].unique())\n",
    "    print(\"Unique value counts:\", data_frame[col_name].value_counts())\n",
    "    encoder.fit(data_frame[col_name]);\n",
    "    transform_encode = encoder.transform(data_frame[col_name])\n",
    "    data_frame[col_name] = transform_encode\n",
    "    \n",
    "    return label_encoding_df_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5eb13cdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: ['W', 'WNW', 'N', 'NNE', 'SW', ..., 'ESE', 'NW', 'E', 'SSW', 'SE']\n",
      "Length: 16\n",
      "Categories (16, object): ['E' < 'ENE' < 'ESE' < 'N' ... 'SW' < 'W' < 'WNW' < 'WSW']\n",
      "Unique value counts: W      7249\n",
      "SE     6890\n",
      "E      6754\n",
      "N      6752\n",
      "SSE    6726\n",
      "SW     6641\n",
      "S      6614\n",
      "WSW    6564\n",
      "SSW    6448\n",
      "WNW    6060\n",
      "NW     6018\n",
      "ENE    5983\n",
      "ESE    5465\n",
      "NE     5372\n",
      "NNW    4993\n",
      "NNE    4987\n",
      "Name: WindGustDir, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.label_encoding_df_cols(col_name, encoder, data_frame)>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoding_df_cols('WindGustDir', le ,df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "76a35e81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: ['W', 'NNW', 'ENE', 'SSE', 'S', ..., 'E', 'NW', 'NNE', 'SW', 'WNW']\n",
      "Length: 16\n",
      "Categories (16, object): ['E' < 'ENE' < 'ESE' < 'N' ... 'SW' < 'W' < 'WNW' < 'WSW']\n",
      "Unique value counts: N      8482\n",
      "E      6798\n",
      "SE     6743\n",
      "SSE    6631\n",
      "NW     6436\n",
      "S      6424\n",
      "W      6232\n",
      "SW     6218\n",
      "NNE    6082\n",
      "NNW    5869\n",
      "NE     5784\n",
      "ENE    5778\n",
      "ESE    5762\n",
      "SSW    5575\n",
      "WNW    5479\n",
      "WSW    5223\n",
      "Name: WindDir9am, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.label_encoding_df_cols(col_name, encoder, data_frame)>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoding_df_cols('WindDir9am', le ,df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "99cea1bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: ['WNW', 'WSW', 'NW', 'W', 'SSE', ..., 'N', 'S', 'SE', 'NNE', 'NE']\n",
      "Length: 16\n",
      "Categories (16, object): ['E' < 'ENE' < 'ESE' < 'N' ... 'SW' < 'W' < 'WNW' < 'WSW']\n",
      "Unique value counts: SE     7586\n",
      "W      7060\n",
      "S      6827\n",
      "WSW    6802\n",
      "SW     6614\n",
      "SSE    6552\n",
      "N      6220\n",
      "WNW    6217\n",
      "NW     6081\n",
      "ESE    6031\n",
      "E      6005\n",
      "NE     5872\n",
      "SSW    5732\n",
      "NNW    5613\n",
      "ENE    5585\n",
      "NNE    4719\n",
      "Name: WindDir3pm, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.label_encoding_df_cols(col_name, encoder, data_frame)>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoding_df_cols('WindDir3pm', le ,df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cbc1093d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(18,18))\n",
    "\n",
    "ax = sns.histplot(ax = axs[0,0],\n",
    "                 data = df[\"WindGustDir\"],\n",
    "                 legend = ('WNW','WSW','NW','W','SSE','ESE','ENE','SSW','E','SW','NNW','N','S','SE','NNE','NE'),\n",
    "                 bins= len(df['WindGustDir'].unique()),\n",
    "                 color = [\"Red\"]);\n",
    "                 #kind = \"hist\",\n",
    "                 #hue =\"RainTomorrow\");   \n",
    "\n",
    "ax = sns.histplot(ax = axs [0,1],\n",
    "                 data = df[\"WindDir9am\"], \n",
    "                 bins= len(df['WindDir9am'].unique()),\n",
    "                 color = [\"DarkBlue\"]);\n",
    "                 #kind = \"hist\",\n",
    "                 #hue = \"RainTomorrow\");\n",
    "                 \n",
    "ax = sns.histplot(ax = axs [1,0],\n",
    "                 data = df[\"WindDir3pm\"], \n",
    "                 bins= len(df['WindDir3pm'].unique()),\n",
    "                 color = [\"Purple\"]);\n",
    "                 #kind = \"hist\",\n",
    "                 #hue = \"RainTomorrow\");\n",
    "\n",
    "                     \n",
    "axs[0,0].set_title('Distribution of Wind Gust Direction with Rain Today Possibility');\n",
    "axs[0,1].set_title('Distribution of Wind Gust 9 am Direction with Rain Today Possibility');\n",
    "axs[1,0].set_title('Distribution of Wind Gust 3 pm Direction with Rain Today Possibility');\n",
    "# axs[1,1].set_title('Distribution of Wind Gust Speed with Rain Today Possibility')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4bd157cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: ['Albury', 'BadgerysCreek', 'Cobar', 'CoffsHarbour', 'Moree', ..., 'Launceston', 'AliceSprings', 'Darwin', 'Katherine', 'Uluru']\n",
      "Length: 49\n",
      "Categories (49, object): ['Adelaide' < 'Albany' < 'Albury' < 'AliceSprings' ... 'Williamtown' < 'Witchcliffe' < 'Wollongong' < 'Woomera']\n",
      "Unique value counts: Canberra            2393\n",
      "Sydney              2361\n",
      "Perth               2262\n",
      "Hobart              2239\n",
      "Darwin              2217\n",
      "Brisbane            2202\n",
      "Adelaide            2178\n",
      "PerthAirport        2167\n",
      "Albury              2142\n",
      "MountGambier        2140\n",
      "MelbourneAirport    2139\n",
      "Mildura             2124\n",
      "Ballarat            2122\n",
      "AliceSprings        2119\n",
      "Portland            2113\n",
      "Watsonia            2113\n",
      "Nuriootpa           2110\n",
      "Bendigo             2110\n",
      "Townsville          2106\n",
      "Cairns              2101\n",
      "SydneyAirport       2100\n",
      "Tuggeranong         2099\n",
      "Newcastle           2096\n",
      "Sale                2093\n",
      "Wollongong          2090\n",
      "Cobar               2090\n",
      "Launceston          2072\n",
      "WaggaWagga          2068\n",
      "Dartmoor            2067\n",
      "CoffsHarbour        2066\n",
      "Witchcliffe         2060\n",
      "Richmond            2060\n",
      "Penrith             2059\n",
      "GoldCoast           2057\n",
      "Woomera             2053\n",
      "Albany              2051\n",
      "BadgerysCreek       2041\n",
      "NorfolkIsland       2038\n",
      "SalmonGums          2031\n",
      "NorahHead           2028\n",
      "MountGinini         2025\n",
      "Moree               2020\n",
      "Walpole             1989\n",
      "PearceRAAF          1953\n",
      "Williamtown         1816\n",
      "Melbourne           1695\n",
      "Nhil                1136\n",
      "Katherine           1065\n",
      "Uluru               1040\n",
      "Name: Location, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.label_encoding_df_cols(col_name, encoder, data_frame)>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoding_df_cols('Location', le ,df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b75772f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"new_column\"] = pd.get_dummies(df[\"RainToday\"], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "da22dc65",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename({\"new_column\" : \"Rain_Today\"}, axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "50d245f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"RainToday\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bae44129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=uint8)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Rain_Today\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "37fbe705",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping RowID\n",
    "df.drop(\"row ID\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0fb31c87",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MinTemp</th>\n",
       "      <td>13.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.8</td>\n",
       "      <td>14.1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>9.8</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxTemp</th>\n",
       "      <td>22.9</td>\n",
       "      <td>25.1</td>\n",
       "      <td>32.3</td>\n",
       "      <td>29.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>30.1</td>\n",
       "      <td>30.4</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.7</td>\n",
       "      <td>20.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>22.5</td>\n",
       "      <td>25.6</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rainfall</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evaporation</th>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunshine</th>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustDir</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDir9am</th>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDir3pm</th>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity9am</th>\n",
       "      <td>71.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity3pm</th>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure3pm</th>\n",
       "      <td>1007.1</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>1005.7</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>1004.2</td>\n",
       "      <td>1001.8</td>\n",
       "      <td>1010.3</td>\n",
       "      <td>1010.4</td>\n",
       "      <td>1002.2</td>\n",
       "      <td>1009.7</td>\n",
       "      <td>1017.1</td>\n",
       "      <td>1008.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud9am</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud3pm</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp9am</th>\n",
       "      <td>16.9</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.8</td>\n",
       "      <td>20.6</td>\n",
       "      <td>16.3</td>\n",
       "      <td>20.1</td>\n",
       "      <td>20.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>15.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.8</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RainTomorrow</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain_Today</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0       1       2       3       4       5       6       7   \\\n",
       "Location          2.0     2.0     2.0     2.0     2.0     2.0     2.0     2.0   \n",
       "MinTemp          13.4     7.4    17.5    14.6     7.7    13.1    13.4    15.9   \n",
       "MaxTemp          22.9    25.1    32.3    29.7    26.7    30.1    30.4    21.7   \n",
       "Rainfall          0.6     0.0     1.0     0.2     0.0     1.4     0.0     2.2   \n",
       "Evaporation       4.8     4.8     4.8     4.8     4.8     4.8     4.8     4.8   \n",
       "Sunshine          8.4     8.4     8.4     8.4     8.4     8.4     8.4     8.4   \n",
       "WindGustDir      13.0    14.0    13.0    14.0    13.0    13.0     3.0     5.0   \n",
       "WindGustSpeed    44.0    44.0    41.0    56.0    35.0    28.0    30.0    31.0   \n",
       "WindDir9am       13.0     6.0     1.0    13.0    10.0     8.0    10.0     4.0   \n",
       "WindDir3pm       14.0    15.0     7.0    13.0    13.0    10.0     2.0     1.0   \n",
       "WindSpeed9am     20.0     4.0     7.0    19.0     6.0    15.0    17.0    15.0   \n",
       "WindSpeed3pm     24.0    22.0    20.0    24.0    17.0    11.0     6.0    13.0   \n",
       "Humidity9am      71.0    44.0    82.0    55.0    48.0    58.0    48.0    89.0   \n",
       "Humidity3pm      22.0    25.0    33.0    23.0    19.0    27.0    22.0    91.0   \n",
       "Pressure3pm    1007.1  1007.8  1006.0  1005.4  1010.1  1005.7  1008.7  1004.2   \n",
       "Cloud9am          8.0     5.0     7.0     5.0     5.0     5.0     5.0     8.0   \n",
       "Cloud3pm          5.0     5.0     8.0     5.0     5.0     5.0     5.0     8.0   \n",
       "Temp9am          16.9    17.2    17.8    20.6    16.3    20.1    20.4    15.9   \n",
       "RainTomorrow      0.0     0.0     0.0     0.0     0.0     0.0     1.0     1.0   \n",
       "Rain_Today        0.0     0.0     0.0     0.0     0.0     1.0     0.0     1.0   \n",
       "\n",
       "                   8       9       10      11      12      13      14  \n",
       "Location          2.0     2.0     2.0     2.0     2.0     2.0     2.0  \n",
       "MinTemp          12.6     9.8    14.1    13.5    11.2     9.8    17.1  \n",
       "MaxTemp          21.0    27.7    20.9    22.9    22.5    25.6    33.0  \n",
       "Rainfall          3.6     0.0     0.0    16.8    10.6     0.0     0.0  \n",
       "Evaporation       4.8     4.8     4.8     4.8     4.8     4.8     4.8  \n",
       "Sunshine          8.4     8.4     8.4     8.4     8.4     8.4     8.4  \n",
       "WindGustDir      12.0    14.0     1.0    13.0    10.0    10.0     4.0  \n",
       "WindGustSpeed    44.0    50.0    22.0    63.0    43.0    26.0    43.0  \n",
       "WindDir9am       13.0     6.0    11.0     3.0    15.0     9.0     4.0  \n",
       "WindDir3pm       11.0    14.0     0.0    14.0    12.0     6.0     3.0  \n",
       "WindSpeed9am     24.0    13.0    11.0     6.0    24.0    17.0    17.0  \n",
       "WindSpeed3pm     20.0    22.0     9.0    20.0    17.0     6.0    22.0  \n",
       "Humidity9am      65.0    50.0    69.0    80.0    47.0    45.0    38.0  \n",
       "Humidity3pm      43.0    28.0    82.0    65.0    32.0    26.0    28.0  \n",
       "Pressure3pm    1001.8  1010.3  1010.4  1002.2  1009.7  1017.1  1008.1  \n",
       "Cloud9am          5.0     0.0     8.0     8.0     5.0     5.0     5.0  \n",
       "Cloud3pm          7.0     5.0     1.0     1.0     2.0     5.0     1.0  \n",
       "Temp9am          15.8    17.3    17.2    18.0    15.5    15.8    24.5  \n",
       "RainTomorrow      0.0     0.0     1.0     1.0     0.0     0.0     0.0  \n",
       "Rain_Today        1.0     1.0     0.0     1.0     1.0     0.0     0.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af6363e",
   "metadata": {},
   "source": [
    "Our data is ready for modelling."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa656528",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9eb8d8e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99516 entries, 0 to 99515\n",
      "Data columns (total 20 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Location       99516 non-null  int32  \n",
      " 1   MinTemp        99516 non-null  float64\n",
      " 2   MaxTemp        99516 non-null  float64\n",
      " 3   Rainfall       99516 non-null  float64\n",
      " 4   Evaporation    99516 non-null  float64\n",
      " 5   Sunshine       99516 non-null  float64\n",
      " 6   WindGustDir    99516 non-null  int32  \n",
      " 7   WindGustSpeed  99516 non-null  float64\n",
      " 8   WindDir9am     99516 non-null  int32  \n",
      " 9   WindDir3pm     99516 non-null  int32  \n",
      " 10  WindSpeed9am   99516 non-null  float64\n",
      " 11  WindSpeed3pm   99516 non-null  float64\n",
      " 12  Humidity9am    99516 non-null  float64\n",
      " 13  Humidity3pm    99516 non-null  float64\n",
      " 14  Pressure3pm    99516 non-null  float64\n",
      " 15  Cloud9am       99516 non-null  float64\n",
      " 16  Cloud3pm       99516 non-null  float64\n",
      " 17  Temp9am        99516 non-null  float64\n",
      " 18  RainTomorrow   99516 non-null  int64  \n",
      " 19  Rain_Today     99516 non-null  uint8  \n",
      "dtypes: float64(14), int32(4), int64(1), uint8(1)\n",
      "memory usage: 13.0 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e8a32696",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into x and y\n",
    "x = df.drop(\"Rain_Today\", axis = 1)\n",
    "y = df[\"Rain_Today\"]\n",
    "\n",
    "# Set random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split data into training and validation sets\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "05e8e910",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((69661, 19), (29855, 19), (69661,), (29855,))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_valid.shape, y_train.shape, y_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7578b54e",
   "metadata": {},
   "source": [
    "Now we've got our data into training and validation sets. Let's build a model. \n",
    "\n",
    "We are going to train our data in these models:\n",
    "\n",
    "1. Random Forest Classifier\n",
    "2. Logistic Regression\n",
    "3. KNeighbors Classifier\n",
    "4. GaussianNB\n",
    "5. DecisionTree Classifier\n",
    "6. XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "95cc15a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(x_train)\n",
    "x_train = scaler.transform(x_train)\n",
    "\n",
    "#--------tranform value for valid data-------#\n",
    "x_valid = scaler.transform(x_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d6e35df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\"Random Forest Classifier\" : RandomForestClassifier(),\n",
    "          \"Logistic Regression\" : LogisticRegression(max_iter = 200),\n",
    "          \"KNeighbors Classifier\" : KNeighborsClassifier(),\n",
    "          \"GaussianNB\" : GaussianNB(),\n",
    "          \"DesicisonTree Classifier\" : DecisionTreeClassifier(),\n",
    "          \"XGB Classifier\" : XGBClassifier()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "182d022f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "def fit_and_score (models, x_train, x_valid, y_train, y_valid):\n",
    "    \"\"\"\n",
    "    Fits and evaluates the given models.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Set random seed\n",
    "    np.random.seed(42)\n",
    "    \n",
    "    # Set empty dictionary\n",
    "    model_scores = {}\n",
    "    \n",
    "    # Loop through models\n",
    "    for names, model in models.items():\n",
    "        \n",
    "        # Fit the model\n",
    "        model.fit(x_train, y_train)\n",
    "    \n",
    "        \n",
    "        # Evaluate the model and append the score to model_scores\n",
    "        model_scores[names] = model.score(x_valid, y_valid)\n",
    "        \n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c0d3dfed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Random Forest Classifier': 0.9959135823145202,\n",
       " 'Logistig Regression': 0.8881594372801875,\n",
       " 'KNeighbors Classifier': 0.8110534248869536,\n",
       " 'GaussianNB': 0.9464746273656004,\n",
       " 'DesicisonTree Classifier': 0.9915592028135991,\n",
       " 'XGB Classifier': 0.9956121252721487}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score = fit_and_score(models = models,\n",
    "                             x_train = x_train,\n",
    "                             x_valid = x_valid,\n",
    "                             y_train = y_train,\n",
    "                             y_valid = y_valid)\n",
    "model_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e21ce579",
   "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>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest Classifier</th>\n",
       "      <td>0.995914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistig Regression</th>\n",
       "      <td>0.888159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighbors Classifier</th>\n",
       "      <td>0.811053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.946475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DesicisonTree Classifier</th>\n",
       "      <td>0.991559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB Classifier</th>\n",
       "      <td>0.995612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Scores\n",
       "Random Forest Classifier  0.995914\n",
       "Logistig Regression       0.888159\n",
       "KNeighbors Classifier     0.811053\n",
       "GaussianNB                0.946475\n",
       "DesicisonTree Classifier  0.991559\n",
       "XGB Classifier            0.995612"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_table = pd.DataFrame(model_score, index = [\"Scores\"])\n",
    "model_table.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f1f0a4",
   "metadata": {},
   "source": [
    "After scoring our model, we can see that `RandomForestClassifier()`, `DesicisonTreeClassifier()` and `XGB Classifier()` seems to overfitting. Whereas, `LogictisRegression()` and `KneighborsClassifier()` is underfitting. `GaussianNB()` has got an ideal scoring for us to test it in our test data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a077140a",
   "metadata": {},
   "source": [
    "## Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ea25c07a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_score, index = [\"Accuracy\"])\n",
    "model_compare.T.plot.bar(figsize = (10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "3b10ddbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "6386a806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=GaussianNB(), n_iter=20,\n",
       "                   param_distributions={&#x27;var_smoothing&#x27;: array([1.00000000e+00, 8.11130831e-01, 6.57933225e-01, 5.33669923e-01,\n",
       "       4.32876128e-01, 3.51119173e-01, 2.84803587e-01, 2.31012970e-01,\n",
       "       1.87381742e-01, 1.51991108e-01, 1.23284674e-01, 1.00000000e-01,\n",
       "       8.11130831e-02, 6.57933225e-02, 5.33669923e-02, 4.32876128e-02,\n",
       "       3.5111917...\n",
       "       1.23284674e-07, 1.00000000e-07, 8.11130831e-08, 6.57933225e-08,\n",
       "       5.33669923e-08, 4.32876128e-08, 3.51119173e-08, 2.84803587e-08,\n",
       "       2.31012970e-08, 1.87381742e-08, 1.51991108e-08, 1.23284674e-08,\n",
       "       1.00000000e-08, 8.11130831e-09, 6.57933225e-09, 5.33669923e-09,\n",
       "       4.32876128e-09, 3.51119173e-09, 2.84803587e-09, 2.31012970e-09,\n",
       "       1.87381742e-09, 1.51991108e-09, 1.23284674e-09, 1.00000000e-09])},\n",
       "                   random_state=42, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=GaussianNB(), n_iter=20,\n",
       "                   param_distributions={&#x27;var_smoothing&#x27;: array([1.00000000e+00, 8.11130831e-01, 6.57933225e-01, 5.33669923e-01,\n",
       "       4.32876128e-01, 3.51119173e-01, 2.84803587e-01, 2.31012970e-01,\n",
       "       1.87381742e-01, 1.51991108e-01, 1.23284674e-01, 1.00000000e-01,\n",
       "       8.11130831e-02, 6.57933225e-02, 5.33669923e-02, 4.32876128e-02,\n",
       "       3.5111917...\n",
       "       1.23284674e-07, 1.00000000e-07, 8.11130831e-08, 6.57933225e-08,\n",
       "       5.33669923e-08, 4.32876128e-08, 3.51119173e-08, 2.84803587e-08,\n",
       "       2.31012970e-08, 1.87381742e-08, 1.51991108e-08, 1.23284674e-08,\n",
       "       1.00000000e-08, 8.11130831e-09, 6.57933225e-09, 5.33669923e-09,\n",
       "       4.32876128e-09, 3.51119173e-09, 2.84803587e-09, 2.31012970e-09,\n",
       "       1.87381742e-09, 1.51991108e-09, 1.23284674e-09, 1.00000000e-09])},\n",
       "                   random_state=42, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=GaussianNB(), n_iter=20,\n",
       "                   param_distributions={'var_smoothing': array([1.00000000e+00, 8.11130831e-01, 6.57933225e-01, 5.33669923e-01,\n",
       "       4.32876128e-01, 3.51119173e-01, 2.84803587e-01, 2.31012970e-01,\n",
       "       1.87381742e-01, 1.51991108e-01, 1.23284674e-01, 1.00000000e-01,\n",
       "       8.11130831e-02, 6.57933225e-02, 5.33669923e-02, 4.32876128e-02,\n",
       "       3.5111917...\n",
       "       1.23284674e-07, 1.00000000e-07, 8.11130831e-08, 6.57933225e-08,\n",
       "       5.33669923e-08, 4.32876128e-08, 3.51119173e-08, 2.84803587e-08,\n",
       "       2.31012970e-08, 1.87381742e-08, 1.51991108e-08, 1.23284674e-08,\n",
       "       1.00000000e-08, 8.11130831e-09, 6.57933225e-09, 5.33669923e-09,\n",
       "       4.32876128e-09, 3.51119173e-09, 2.84803587e-09, 2.31012970e-09,\n",
       "       1.87381742e-09, 1.51991108e-09, 1.23284674e-09, 1.00000000e-09])},\n",
       "                   random_state=42, verbose=1)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a hyperparameter grid for GaussianNB()\n",
    "gs_NB_grid = {\"var_smoothing\" : np.logspace(0,-9, num=100)}\n",
    "\n",
    "# Let's try RndomizedSearchCV on GaussianNB\n",
    "rs_NB_gs = RandomizedSearchCV(GaussianNB(), \n",
    "                 param_distributions = gs_NB_grid, \n",
    "                 cv = 5,\n",
    "                 verbose=1,\n",
    "                 n_iter = 20,\n",
    "                 random_state = 42)\n",
    "rs_NB_gs.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "92b6691a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'var_smoothing': 4.3287612810830526e-07}"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_NB_gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "775dca56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9492547312008038"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_NB_gs.score(x_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "dae415b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'var_smoothing': 4.3287612810830526e-07}"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_NB_gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "aac6dcac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9492547312008038"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_NB_gs.score(x_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "42f7ea57",
   "metadata": {},
   "outputs": [],
   "source": [
    "ideal_model = rs_NB_gs.predict(x_valid)\n",
    "def evaluate_preds (y_valid, y_preds):\n",
    "    \"\"\"\n",
    "    Performs evaluation comparison on y_test labels and y_preds labels on our classification model.\n",
    "    \"\"\"\n",
    "    accuracy = accuracy_score(y_valid, y_preds)\n",
    "    precision = precision_score(y_valid, y_preds)\n",
    "    recall = recall_score(y_valid, y_preds)\n",
    "    f1 = f1_score(y_valid, y_preds)\n",
    "    \n",
    "    metric_dict = {\"accuracy\" : round(accuracy, 2),\n",
    "                   \"precision\" : round(precision, 2),\n",
    "                   \"recall\" : round(recall, 2),\n",
    "                   \"f1\" : round(f1, 2)} \n",
    "    print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
    "    print(f\"Precision: {precision:.2f}\")\n",
    "    print(f\"Recall: {recall:.2f}\")\n",
    "    print(f\"F1: {f1:.2f}\")\n",
    "    \n",
    "    return metric_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "cd1dcb65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.93%\n",
      "Precision: 0.83\n",
      "Recall: 0.98\n",
      "F1: 0.90\n"
     ]
    }
   ],
   "source": [
    "y_preds = rs_NB_gs.predict(x_valid)\n",
    "gs_metrics = evaluate_preds(y_valid, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "4d0c3b6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.94      0.97     22995\n",
      "           1       0.83      0.98      0.90      6860\n",
      "\n",
      "    accuracy                           0.95     29855\n",
      "   macro avg       0.91      0.96      0.93     29855\n",
      "weighted avg       0.96      0.95      0.95     29855\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37ab4c9c",
   "metadata": {},
   "source": [
    "### Evaluating our tuned machine learning classifier, beyond accuracy\n",
    "\n",
    "* ROC curve and AUC score\n",
    "* Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "649b6aa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Win10\\Desktop\\data-science-projects\\australia-rainfall-prediction-project\\env\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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UrlxZl/EJIYQoBBQNcIeEhNCuXTsOHjxI6dKlMTc3Jzg4GFdXV/766y9dxyiEEKKAKUoWc+fOpVOnTuzatYtZs2bx1VdfsWvXLrp3757tvFEvCw0NpXfv3tja2tKlSxdCQkIyrRcXF8fkyZNp0qQJTZs2ZcaMGXLXlRBCFCBFyeLixYu4ubllmHW2X79+XLx4UdGOkpKSGDFiBB06dODs2bMMGzYMd3f3TJ8Anzp1Kk+fPuXw4cPs2bOHixcvsm7dOkX7EUIIkfcUJQtLS0uio6MzlEdFRWFqaqpoR2fOnCE5ORk3NzcMDAzo1KkTNjY27NmzR6ve3bt3OXz4MF999RUlSpTAwsKClStX0qVLF0X7EUIIkfcUJYuuXbsyY8YMDhw4wN27d7l79y779+/H29sbV1dXRTsKDw/H2tpaq8zKyoorV65olV2+fJny5csTEBCAs7Mzjo6ObNq0ibJlyypskhBCiLym6G6oYcOGcffuXT777DPS0tIA0NfXp3///owbN07RjuLj4ylevLhWmbGxMQkJCVpljx8/Jjo6mqtXrxIQEMDDhw8ZNmwYpqamDBs2TNG+ACwsSiiu+4KBoT4AlpYlc/zeN520uWiQNhcNumizomRhaGjIV199haenJ5GRkRgZGVGlSpUMB/9XMTExITExUassISEhw1oZhoaGpKamMnnyZExNTTE1NcXNzY3NmzfnKFk8eBBHWppacX2A5KRUDAz1uXcvNkfve9NZWpaUNhcB0uaiIbdt1tNTvfIk+5XJ4o8//mD//v0YGhri4uJC/fr1qVevXo6DALC2tsbPz0+rLCIigm7dummVWVlZAenzUZUsmZ4dU1NTc7VPIYQQeSPLMYs9e/bQt29fgoODOXLkCH369OHHH3/M9Y6aNm2KWq3Gz8+P5ORkAgMDCQsLo02bNlr1atasSZ06dZgzZw7x8fFER0fj5+dH586dc71vIYQQryfLZLFx40bGjh3L3r172b17N5MmTWLlypW53pGhoSFr165l3759NGnShNWrV7NixQrMzc0JCAjAzs5OU3ft2rUYGRnh4uLCRx99hLOzMwMHDsz1voUQQrwelVqtzrRj387OjsDAQCpUqADA8+fPsbW15bfffsPCwiJfg8yN3IxZzNt0DgNDfcb3qq+jqAon6dctGqTNRYOuxiyyvLJISEjQmiSwePHiGBsbEx8fn+MghBBCvNkUPWchhBCiaMvybiiVSsXz5881z0Go1eoMZS8YGxvrNkohhBAFKstkoVarcXFxyVCW2RPbly9fzvvIhBBCFBpZJouNGzfmZxxCCCEKsSyTRcOGDdHX18/RxlJTU3P8HiGEEIVflgPcPXv2ZM+ePWRxZ62WlJQU/P39+eijj/I0OCGEEIVDllcWK1euZNasWXz99de0bt2aVq1aYWNjQ+nSpVGr1Tx8+JDQ0FDOnj3Lvn37qFevHitWrMjP2IUQQuSTLJNF+fLlWblyJZcuXeKHH35g5syZPHjwQGsBJAsLC1q1asWaNWuoW7duvgQshBAi/2U76+wHH3zAnDlzALh16xb3799HT08PS0tLWWNCCCGKCEVTlL9QoUIFzfQfQgghig55glsIIUS2JFkIIYTIliQLIYQQ2cpRsnj27Bl///03SUlJxMXF6SomIYQQhYyiZJGUlIS3tzeNGzemZ8+e3Llzh6lTp+Lh4UFsbNGaK14IIYoiRcli6dKlnD9/nk2bNmnWuBgyZAjR0dGa22qFEEK8vRQli6CgILy8vLSWPq1Xrx6zZs3iyJEjOgtOCCFE4aAoWTx8+BBLS8sM5SVKlOD58+d5HpQQQojCRVGyaNSoUYYpy5OSkli5ciUNGjTQSWBCCCEKD0VPcE+bNo0hQ4bw22+/kZSUxJQpU7h+/Tr6+vqsX79e1zEKIYQoYIqSRbVq1QgKCmLXrl2Eh4eTmppKly5dcHV1lSVVhRCiCFCULKZMmcK0adMyrFfx5MkTJk2ahI+Pj06CE0IIUThkmSxCQ0O5c+cOADt37qRFixaULFlSq86VK1c4fvy4biMUQghR4LJMFrGxsXh4eGh+//zzzzPUMTExwd3dXTeRCSGEKDSyTBaNGzcmNDQUgNatW7Nt2zbMzc3zLTAhhBCFh6JbZw8fPpxlonjRVSWEEOLtpWiAOzIykvnz53Pt2jVSU1MBUKvVJCUl8fDhQ/7++2+dBimEEKJgKbqy8Pb25vbt2/Tp04e7d+/y6aef0qpVKx4/fsysWbN0HaMQQogCpujK4sKFC2zcuJF69eqxZ88e6tevj7u7O1WrVmX37t0ZbqkVQgjxdlF0ZaFWqylTpgwAVlZWXL58GQBnZ+ccdUGFhobSu3dvbG1t6dKlCyEhIa+sn5ycTI8ePeQ5DiGEKGCKkkWtWrXYv38/ADY2Npw9exZIH9xWq9WKdpSUlMSIESPo0KEDZ8+eZdiwYbi7u79yEaUlS5ZoEpMQQoiCoyhZjBo1ikWLFrFp0yZcXV05fvw4/fv3Z+zYsbRs2VLRjs6cOUNycjJubm4YGBjQqVMnbGxs2LNnT6b1T58+zYkTJ3BwcFDeGiGEEDqhaMyiZcuW7Nu3j9TUVMqWLcuWLVvYuXMnLVu2ZODAgYp2FB4ejrW1tVaZlZUVV65cyVD3yZMneHl5sXz5cpYsWaJo+/9lYVEix+8xMNQHwNKyZDY13z7S5qJB2lw06KLNipIFQIUKFTT/rlGjBpMmTUKtVrNlyxb69OmT7fvj4+MpXry4VpmxsTEJCQkZ6s6cOZNPP/2UGjVqKA0vgwcP4khLU9ZF9kJyUioGhvrcu1e0loq1tCwpbS4CpM1FQ27brKeneuVJdpbdUGq1mrVr19KjRw969erFd999p/X6X3/9Ra9evfjyyy8VBWJiYkJiYqJWWUJCAiYmJlpl27dv59GjR4qvWIQQQuhelsli4cKFLFu2jPfff5+6devi4+ODr68varWa+fPn88knnxAbG8u3336raEfW1tZERkZqlUVERGBjY6NVFhgYSEhICI0bN6ZRo0YEBwfj6+urNU+VEEKI/JVlN1RQUBDe3t6aZyhatGjB7NmzuX79Ort27WL48OEMHToUQ0NDRTtq2rQparUaPz8/+vbty/79+wkLC6NNmzZa9datW6f1+4gRI6hduzajR4/OaduEEELkkSyvLO7evUuzZs00v7du3Zo7d+5w6tQptm7dyqhRoxQnCgBDQ0PWrl3Lvn37aNKkCatXr2bFihWYm5sTEBCAnZ3d67VECCGEzmR5ZZGSkqI1IK1SqTA0NGTmzJnUqlUrVzurUaMGmzdvzlDu6uqKq6trpu9ZuXJlrvYlhBAi7yh6zuJlVlZWuohDCCFEIZZlslCpVKhUqgxlQgghip4su6HUajUdO3bUShDx8fH07NkTPT3tHHPy5EndRSiEEKLAZZks5syZk59xCCGEKMSyTBbdu3fPzziEEEIUYjke4BZCCFH0SLIQQgiRLUkWQgghsiXJQgghRLYUJ4sLFy4wZswYunbtyu3bt1m7di1BQUG6jE0IIUQhoShZBAcHM3DgQMzMzIiMjCQlJYXU1FQmTpzItm3bdB2jEEKIAqYoWSxdupSpU6fy5Zdfoq+fvprcsGHDmDFjhuIpyoUQQry5FCWLiIgI7O3tM5Tb29tz69atPA9KCCFE4aIoWZQtW5aLFy9mKP/tt9+0llsVQgjxdlK0Bvfw4cOZOXMmN27cIC0tjSNHjhAdHc2WLVuYPn26rmMUQghRwBQli27dumFhYYGvry/Gxsb4+PhgbW3NokWLcHFx0XWMQgghCpiiZHHnzh1atmxJy5YtdR2PEEKIQkjRmMWHH37IwIED2bZtG3FxcbqOSQghRCGjKFls376devXqsXr1apo3b86oUaPYt28fSUlJuo5PCCFEIaAoWdSuXZsJEyZw8OBBvv/+eypVqsT8+fNp3rw5U6ZM0XWMQgghCpiiMYuX1a1bl2fPnpGcnMyOHTv47bffdBGXEEKIQkRRslCr1Zw6dYqgoCAOHjxISkoKLi4urFixgmbNmuk6RiGEEAVMUbJo0aIFz549o1WrVsycORMnJycMDQ11HZsQQohCQlGyGD9+PO3ataNkyZK6jkcIIUQhlGWyCA4Opnnz5hgYGGBpacm5c+ey3Iijo6NOghNCCFE4ZJksPDw8+O2337CwsMDDwyPLDahUKi5fvqyT4IQQQhQOWSaL0NDQTP8thBCi6FH0nMWAAQN4+vRphvKHDx/So0ePPA9KCCFE4ZLllcXp06cJDw8H4OzZs2zZsgVTU1OtOhEREdy4cUO3EQohhChwWSYLMzMz1q9fj1qtRq1W8+OPP6Kn9++FiEqlwsTEBE9PT8U7Cw0NZebMmYSFhVG5cmVmz55NvXr1MtS7ePEic+bMISwsjBIlStCzZ09GjhyJSqXKYfOEEELkhSyTRc2aNTl06BAA/fv3Z/ny5ZQqVSrXO0pKSmLEiBEMGDCAH374gf379+Pu7s6RI0coUaKEpl5CQgIeHh4MHz6cjRs3EhUVhbu7O5aWlvTu3TvX+xdCCJF7WY5ZJCQkaP7t6+uLoaEhCQkJmf4ocebMGZKTk3Fzc8PAwIBOnTphY2PDnj17tOrdvn0bW1tb+vXrh76+PlWrVsXFxYU//vgjl00UQgjxurK8smjQoAHHjx/HwsICOzu7TLuA1Gq14ltnw8PDsba21iqzsrLiypUrGcpWrFih+T0pKYmjR4/KVYUQQhSgLJPFhg0bNN1OGzdufO0dxcfHU7x4ca0yY2PjV16ZJCUlMWHCBIyNjfnkk09ytD8LixLZV/oPA0N9ACwti96T6tLmokHaXDToos1ZJosmTZpk+m9IP4iHhYVRrVo1rfGGVzExMSExMVGrLCEhARMTk0zr37t3j9GjR6Onp8d3332XIdFk58GDONLS1Dl6T3JSKgaG+ty7F5uj973pLC1LSpuLAGlz0ZDbNuvpqV55kq3oOYt//vmHfv368eeff5KQkECvXr3o1asXTk5O/Pnnn4oCsba2JjIyUqssIiICGxubDHXDw8Pp2bMnVapUwc/P77UG1oUQQrw+Rcli1qxZmJqaUrFiRfz9/YmJiWHv3r306dOHuXPnKtpR06ZNUavV+Pn5kZycTGBgIGFhYbRp00ar3pMnTxg8eDAdO3Zk3rx5MrutEEIUAoqSxR9//MGUKVOwtLTk4MGDfPjhh1StWpVevXopnhfK0NCQtWvXsm/fPpo0acLq1atZsWIF5ubmBAQEYGdnB4C/vz937txh8+bN2NnZaX7Gjx+f+1YKIYR4LYqmKDc0NCQlJYX4+HjOnj3L119/DcD9+/dzNG15jRo12Lx5c4ZyV1dXXF1dgfSpRQYMGKB4m0IIIXRPUbJwcHBg+vTpmJqaUrx4cZycnDh58iSzZ8+mdevWuo5RCCFEAVPUDfXll19Sp04djIyM8PX1xcTEhL/++osmTZowdepUXccohBCigCm6sjA1NWX69OkAxMXF8fTpU4YOHarTwIQQQhQeiq4sAH788UccHR1p3LgxTZs2xcHBAV9fX13GJoQQopBQdGWxbt06Vq5cybBhw2jYsCFqtZpz586xdu1ajIyMGDhwoK7jFEIIUYAUJYtNmzbxxRdf0LlzZ01Zw4YNqVixIkuWLJFkIYQQbzlF3VCPHj2ibt26Gco/+OADYmJi8jwoIYQQhYuiZFG9enX27t2boXzPnj1YWVnleVBCCCEKF0XdUGPGjMHDw4Pz589rnrQ+f/48x48fx8fHR6cBCiGEKHiKriwcHBzw8/PD0NAQf39/9u3bR4kSJdi2bRtOTk66jlEIIUQBU3RlAdC4cWMaN26sy1iEEEIUUq9cVtXLy4smTZrg4OCAt7c3cXFx+RmbEEKIQiLLK4tly5Zx5MgRhgwZgp6eHps2beLx48csWbIkH8MTQghRGGSZLPbt28fChQtp1qwZkL5aXt++fUlOTsbAwCDfAhRCCFHwskwWd+7c0bottm7duqjVah48eEC5cuXyJTjxehISnhEX95jU1JRX1rt7V4+0tLR8iqpwkDYXDdLml6kwNCxO6dKWqFSqHG83y2SRmpqKvr7+v7tRqTAwMCA5OTnHOxH5LyHhGbGxjzAzs8TAwPCVfxzFiumRklK0/kNJm4sGafO/1Oo0Hj++T1zcE0qWNMvxdhVPJCjeLHFxjzEzs8TQ0ChXZxFCiLeLSqVHyZKlSUjI3Y1Kr7x1dvv27ZiYmGh+T01Nxd/fn9KlS2vV69u3b652LnQnNTUFAwNZv1wI8S99/WKkpaXm6r1ZJosKFSpkWAK1TJkybN++XatMpVJJsiik5IpCCPGy1zkmZJksDh8+nOuNCiGEeLvImIUoFBwcGuHs3II2bVrSpk1LnJ1b8NFHndmwYZ1O9rd27WqmTJmQp9u8cOE83bt3zNNtQvpnc/VqGJD+sOzixfPo1q0DLi4OfPRRZ5YtW0RCQkKe7/dVzp37nfbtP3xlnRs3/sHbexquru1o29aRgQM/ISBgB5DejnbtHDl0aH+G98XFxeHs3IKQkD8z3W5YWChTpnyuVXbv3l0cHZvi5TU5Q/1Ro4by888/ZihfunQRs2d7a35PS0vjl19+YtCgT2nXzhFX13Z4e0/j9u1br2ynUrGxsUyfPol27Rzp1q0DgYEBWdZ99OgR3t7T6NjRma5d2+Pru5LU1H+7j7Zt20KvXl1p186RCRPGEB19U/PaZ5+N4saNf/Ik5pdJshCFxurV6zlw4Jjmx9NzOhs2rOPUqRMFHZoi9evbsWPHHp3u45tv5hMTE8O6dd9z8OBxli/3JTT0MvPnz9bpfnPq2rVwhg4dSI0aNdm8+Rf27j3C+PGerFu3mk2bNmBsbEzbth3Zs2d3hvfu3x9EpUqVqVfPNsNraWlpzJ37JcOGjdIq37VrJ23atOfkyePcvXsnVzF//fUXBAUFMnnyDIKCjvDDD1spWfIdRowYwtOnT3O1zZctXDgHPT19/P33sWDBElavXs75839kWnf27Jk8evSITZu28v33P3H58iW+/XY1AIcOHcDXdxVTp84gMPAQDg6tGDduJImJiQB4eIxg7txZrx3vfymeG0qI/KSnp0eTJs14772qRERco1mz5sTGxrJkyQJCQv7k4cMHVKxYiTFjJtCoURPOnfudxYvn07x5C3bvDsDIyIjOnbvi7u4BwO3bt5g7dxZ//32RKlWqUbVqVc2+UlJS2LBhHYGBATx//py6devx2WcTKV++AufO/c7SpYto3tyBnTt/wdDQkLFjPyciIpwdO7ZSrJgBo0ePw8WlHefO/c7UqZ+zd++vLFjwNfv3B2n2kZSUhJmZGf7++0hMTMTXdwVHjhwiJSUFB4dWjB49HmNjYwB+/vlHfvzxexITE+nTp5/W5/L33xfp2fMTLCzKAFC+fAXGjBnPgQP/LiEQGRnBkiULCQu7jLm5OW5u/6Nt2/YA3LkTo3nt8ePHWFvbMGnSNKpXr8GePbvYtSv9zP+ff/5h2bLVmJmVZsmS+Zw9e5rixYvj6tpD85mmpalZu3YVe/bsIjExkY8/7oOb2xAAfHwW06FDFz79dIAmrvr17Zg4cRq//34agO7dP2LQoL7cv3+PMmUsNfV27/anW7eemf5dBAcf5p13SlGlyr/fX2pqKrt3+zNjxiySk5PYsWMbHh4js/sT0xIS8ieHDx/k55/9KVMm/bN95513GD9+EvHxcfzzTyR169bXes/+/UEsWPB1hm2VLVueH374Wavs+fPn/PrrIb7//meKFy9O9eo16dKlGwEBO7Cza5ih7unTJ/Hz20zp0uYADBkyHE/PcQwdOoLg4MN06dJN877u3XuybdsW/vjjLM2bO1C79vs8f/6cs2dP0bhxsxx9Dq8iyaII+e2v2xwPuZ2hXKUCtTpv9+VQrzwt6pbP9ftTUlIIDj7C9esRNGiQ/p9i5cplPH+ewPff/0yxYsVYs2YFS5Ys1PzHvH49gpYtHQkI2Me5c78zYcJonJ3bUrVqNby8JlO9ek0WLFhKREQ448ePon799On2161bw/Hjwfj4rKFMGUtWrfJh0qTP+O679K6La9eu0ry5A4GBB9my5Qe++GIaffsOxN9/Hzt3bmPx4vm4uLTTin/ixKlMnDgVSD9ADx06kFGjxgGwatUyrl69wrffbsTQ0IjZs71ZsmQBU6bM4MSJ46xfv5YlS1ZSrZoVS5Ys0Nquk5MLK1YsISwslEaNGlOvni21ar1PrVrvAxAfH8+4cSP5+ONPWbRoGVevhjFp0jjKli1Lw4YNmTt3FlWqVGXWrLmkpqYwZ84sfH1XsGDBUgD++iuEhQuXUbduPUxMTBk+3J2KFSuyY0cQT58+ZdSo/1G5chXKlClDfPwzkpKS2Lo1gL/+usCYMcNwdGxNpUqVOXfudwYNGprx78KhFQ4OrQCwsrLh/fc/YO/eQPr1cwPg6tUwoqJu0K5dh0z/Lnbt8sfJyUWr7OTJ3zAyMsLWtgFpaWnMmDEZN7chGBkZZfdnpnHq1Anq1q2vSRQvqFQqvLwyP0tv27YDbdtmHud/RUX9g0qlonLl9zRlVapU5bffjmao++KBuuLFi2vK9PX1ePz4EbGxsaSlpWlOLF7Q09MjKuofwAEAJydn/P135GmyyFE3VHh4OAcOHCA+Pp6oqCjUeX2EEUXaiBH/o337D2ndugXOzi0IDAxg7tzFmgPh//43jMmTZ2BgYMDdu3coUaIE9+/f07xfpVLh5jaEYsWK0aRJMywsynDzZhTR0TcJDf2bYcNGYmhoSK1a79OhQyfN+/bt24Ob2xAqVqyEkZERI0eO5c6dO/z990UA9PX1GTTof+jp6dGgQWNSU1Pp06c/xYoVw97egadPn/DsWeb3rickJDB58ng6dnSlTZv2qNVqdu3ayYgRYzE3t6BEiRIMHz6KoKDdJCUlcejQftq160CtWrUxMjJixIixWttzd/fAy2sWsbFPWLx4Ht27d8TdvT8XLvwJwMmTxzE1NeXTT9Pjq137Azp1cmXnzl8AmDzZi2HDRgPpSeydd97R+gzNzMxo1qw5pqYluH37FhcvhjBq1HhMTEwoV64cixf70KBBI83nMnToCIoVK4adXUPMzS24ffsWT548IS0tDXNz82y/865dP2Lv3kDN77t27aRduw6YmJhmqJuWlsaFC+d4//0PtMoDArbTrdtHADRo0Agzs9IcPLgv232/7PHjR4riza34+IQMzzwVL16c58+fZ6hrYmJC48bNWLVqGU+fPuHJk8d8991aAJKSEvnww9YEBOwgNPRvUlJSCAjYwY0b/2i6oQA++KAuFy6cz9M2KLqyiIuLY9y4cRw7dgw9PT327dvHnDlziIqKYu3atTL9xxuiRd3Mz/YLy1OuK1eupXr1mty/f58vvpiGkZEhjRo10bx+//49li1bTGTkNSpVeg9zcwvg3xMWU1NTrbPJYsWKkZaWxsOHDzA0NKJUKTPNaxUqVOTWrfSBy0ePHlK27L+fi4GBAWXKlOHu3TuYm1tQvHhxDA3Tn1nR108/vypZsiSQfkYH6V0y/6VWq/nySy8sLcvyv/8NB9IPSomJiYwfP1LrwFGsWDFiYm7z8OEDqlX7d5qdkiVLUqJESa3ttmr1Ia1afYharebatXB++mkTn38+ml9+2U1MzG1u3ozSGnxOTU2jZs1aAERF3WDKlM+5e/cOVatWw9DQUOuq0sLi3+6ghw8fYmhopPVc1XvvVQXgxo3rGBsba80TZ2BgQEpKCmZmZhQrVoyHDx9onUlD+hVjfHw877zzDgCtW7fBx2cxly5dxMamOgcO7MPHZ02GzxLgyZMnJCYmanVZxcTEcOrUCS5e/ItNmzYC8OxZHNu2baFTJ1cADA2NSEnJ+GxBamqK5nu1sCiT5YD648ePKVWqVIbbTvfv38vixXMz1C9bthwbNmzRKjM2Lk5SUiJqtVqznefPn2NsbJLh/QBeXl+ydOlC+vbthZmZGb179+W3345RokRJXFza8fDhQ2bOnEpCQgKtW7ehUaOmlCz5jub9FhZlePToYZ7O5acoWcydO5ekpCSCg4Np3z6973P69OlMnDiRr7/+mmXLluVJMEJA+vM8s2fPx83tU5YtW8T48Z4AzJw5lU6dXFm2bDV6enoEBx/h/Pnfs92epeW7JCUl8ujRQ00f8N27dzWvly1bjpiYW9Spk77OfHJyMvfu3fv/ZJT7e9NXr15OVNQNfH2/0ySVd94phYGBAWvXbtAceJOSkrh1K5qKFStRpoyl1rr28fHxmquWyMgIhgzpz/btgZQqZYZKpcLGpjpTpszgwIG9REffpEwZS2rVep81a77TbCP9ykFFSkoyU6Z8jqfnNE232U8/bSIo6N8z+5fbamlpSVJSIk+ePNYk2uPHg0lNTdU6MP1XsWLFaNy4KcHBhzVdfS8cOXKQhQvnsHPnXoyNjTE0NKR9+84EBe2mXj1bqlatio1N9Uy3q6eXHtvLiXn37p00adKMKVNmaMqePXvGwIGfcOHCeRo2bMi7777LnTsZu19v3YrWjEM0b+7Ajz9u5MGD+5rxoPR9pTFq1P9wcWmnGY95oW3b9pqxoOxUqvQearVa8z0D/PPPdapWrZZp/QcP7jNp0lTNFdbp0yepWrUaxYsX5/79+7Ro0ZKPP+4DpI/Z9Orlirv7v91+qamp6Onp5emzVoq6oX799VcmTZpE2bJlNWUVKlTAy8uLU6dO5VkwQrzwzjulmDzZix07tnHmTPrf2LNnzzAyKo6enh43b0axYcO3iuYqK1euPHZ2DVm+fAnPnz8nPPyq1m2L7dt3YsOGdURH3yQxMZEVK5ZiZmaWYUAzJ/buDSQw0J958xZrdano6+vTtm0HVq3y4cmTxyQlJbFixRI8PdPHMzp06MyBA0FcvBhCcnIya9Ys13T3Vq1ajapVrfjiCy/++ec6kH7Wu3Hjeiwty2JtXR17+xZER99kz55dpKSkcOtWNKNGeeDv/wtJSckkJSVq+sLDwkLZunULKSmZf4Zly5ajfn07Vq3yITHxOTExMSxbtpikpKRs2z906Eh27w5g8+YfiI9/RkpKCidPHmfJkgX06+em1eferdtHBAcfZu/e3VkObAOUKmX2/wfL9G6zlJQUdu/2p0OHzlhYlNH8vPdeFZo3d2Dr1vSze2fntuzdG8jp0ydRq9UkJj5n795ALlw4T+vWbQCoXfsDWrVyYvLk8Vy5Eopareb+/fvMnu1NfHw8Xbv2yLbNr2JiYkKrVk6sWbOC+Ph4rl698v9dbpnfau3j8w1r164mJSWF6OibrFrlQ48eHwPwxx9nmDBhNA8e3Of58+f4+q7EwqIMtWv/2z334MF9zM0tKFYs74alFSWL+Ph4rcGWF1JTU4vcjI4i/zRp0oyOHbswb95XxMc/Y/JkL3755SfatGnFxIljadu2g+Y/U3a++OJrYmOf0qVLG7y9p9Gq1b/LAffr50arVk6MGTOMLl3aEhV1g2++WfFal+/r1vny/PlzhgwZoHl2pE2blsTExDB27AQsLd/Fze1TXF3T97dw4TL09fVp0KARo0ePZ+bMqXTu7EKxYgaUKlUKSD/rX7zYh0qVKjF+/ChcXBz49NOPiIy8xrJlqzA0NOSdd0qxaJEPe/bsonPnNgwfPphWrT7EzW0IJiYmTJw4lUWL5tGunSNff/0FXbv2ICbmNvHx8a/83Hr06MSwYYPo3LkrbdpkfzZdvXoNli1bzfnzf/Dxx13p2NGZ1atXMHz4aPr3H6RVt3Ll97CysiEs7HKGwev/atCgMZcuhQBw4sRxnj17phkwf1nHjq4cO/Yrd+7E0LhxUyZMmMKaNSto3/5DXF3bsXu3PwsXLtPqJps2zZsWLVrh7T2Ntm0dGTy4L2q1mhUr1mquSF/HxIlTUalU9OzZhYkTxzJgwGDs7VsA6d1pbdq01IwzeHpOIyLiGh07OjN6tAdt27anR49eQPrAeqtWTri5fUq3bh2Ijo5i3rzFWlcRly79RePGTV875pep1ApGqceOHYtKpWLBggU0adKEgIAATE1N+eyzzzAzMyuU3VAPHsRl2o/8KvM2ncPAUJ/xvXJ/RllYxMT8Q7lyVRTVLSxjFvlJ2vxmOnToADt3bstyXOO/3oY251SxYnoMGNCHkSM/o2HDjEthZ3Vs0NNTYWFRIsvtKrqy8PLyIiYmhqZNm/L8+XMGDRrEhx9+SFxcHNOmTVPciNDQUHr37o2trS1dunQhJCQk03q3bt1i0KBB2NnZ4eLiQnBwsOJ9CCHeXk5Ozjx9+pSIiPCCDqXQCgm5gLGxSaaJ4nUo6tAqU6YMW7Zs4dSpU1y7do2UlBSsra1p0aKF4gGUpKQkRowYwYABA/jhhx/Yv38/7u7uHDlyhBIltLPZ+PHjsbW1Zc2aNfzxxx+MHDkSf39/KleunPMWCiHeGnp6ekyZ4sWaNSuYN++bgg6nUFq7djWTJik/iVdKUTdUXjh+/DhTpkzh2LFjmrI+ffrQvXt3Pv74Y01ZZGQkXbp04cyZM5rp0T///HMqVqzIuHHjFO9PuqGkG+pVpM1Fg7Q5o9x2Qym6smjWrNkrryBOnjyZ7TbCw8OxtrbWKrOysuLKlStaZdeuXaN8+fJa62hYWVll2WWVlVc1OisdHdJvY7O0LJlNzcLv7l09ihVT/sxlTuq+LaTNRYO0WZuenl6ujnGKkoWnp6fW7ykpKURFRbF9+3YmTFA2c2dmd1QZGxtnmC3z2bNnmdbL7EnHV8nNlUXdKqWxtCzJvXuxOXpfYZSWlkZycqqibkI5+yoapM1Fw6varFarSUtLy/QYlydXFt27d8+0vG7dumzcuDHL119mYmKi9Tg6pE+F8PIVRE7qiVfT1y9GcnIShobK58cRQrzdUlNT0NPTz9V7X+v6rFatWly4cEFRXWtrayIjI7XKIiIisLGxyVDv1q1bWlcSmdUTr1aihBmPH9/TTDEghCja1Oo0YmMfYWyc8y56UHhlER6e8Ta12NhYVq1aRZUqygZRmzZtilqtxs/Pj759+7J//37CwsJo06aNVj0rKytq1arFN998w4QJEzh37hyHDh3ip59+UrQfkc7YOP2p4SdP7pOamvLKunp6ekXu4Uppc9EgbX6ZCkPD4pQoUSpX21V0N1StWrVQqVQZzlDLly/PnDlzaNZM2TS4V65cYebMmYSGhlKpUiWmTp2Kvb09AQEBzJw5k/Pn059evH37Nl5eXpw/f57SpUszfvx4OnbM2QpkuRmzAN6aMYuckDYXDdLmoiG3bc5uzEJRsrh8+bJmlkhIn3bgxcyceTlRVV6SZKGctLlokDYXDbpKForGLEaMGMHjx4+pWLEiFStWpEKFClhaWhbaRCGEECJvKR7glkFSIYQouhQNcHfs2BF3d3c6dOjAe++9l2G5wr59++okuNfxYu77/H7vm0raXDRIm4uG3LQ5u/coGrNo3bp11htQqTh06FCOAxNCCPHmyDJZLF++HHd39wwLgwshhCh6shyzWLFiRZYLogghhChaskwWMqAthBDihVcOcMfExGSYpykzFSpUyLOAhBBCFD5Zjlm8eGr7VdRqNSqVisuXL+skOCGEEIXDK68sNm7ciJmZWT6FIoQQorDKMlmoVCqsrKywsLDIz3iEEEIUQjLALYQQIltZJovu3btneFJbCCFE0aToCW4hhBBFW9FbyRwIDQ2ld+/e2Nra0qVLF0JCQjKtd+vWLQYNGoSdnR0uLi4EBwfnc6R5R2mbL168SN++fWnUqBEffvghy5cvfyO7JJW294Xk5GR69OiBj49PPkWY95S2OS4ujsmTJ9OkSROaNm3KjBkzSE5Ozudo84bSNkdERDBgwAAaNWqEo6Mjq1atyudI815ISAj29vZZvp7nxy91EZOYmKh2cnJSf/fdd+qkpCT17t271Y0aNVLHxsZmqNu7d2/1nDlz1ImJieoTJ06o7ezs1Ddu3CiAqF+P0jbHx8ermzdvrv7+++/VKSkp6sjISHXr1q3VW7ZsKaDIcycn3/EL8+fPV9eqVUu9bNmyfIw07+SkzaNHj1YPHz5cHRsbq75//766e/fu6lWrVhVA1K8nJ21+0cbU1FR1ZGSk2t7eXn3w4MECiPr1paWlqX/++Wd1w4YN1Q0bNsyyXl4fv4rclcWZM2dITk7Gzc0NAwMDOnXqhI2NDXv27NGqFxkZycWLFxkzZgyGhobY29vTunVrtm3bVkCR557SNt++fRtbW1v69euHvr4+VatWxcXFhT/++KOAIs8dpe194fTp05w4cQIHB4d8jjTvKG3z3bt3OXz4MF999RUlSpTAwsKClStX0qVLlwKKPPdy8j1HRkYC/z4bplKp3tgx2WXLlrF582aGDx+eZR1dHL+KXLIIDw/H2tpaq8zKyoorV65olV27do3y5ctjYmKiVS8sLCxf4sxLSttsZWXFihUrNL8nJSVx9OhR3n///XyJM68obS/AkydP8PLyYt68eRgYGORXiHlOaZsvX75M+fLlCQgIwNnZGUdHRzZt2kTZsmXzM9w8kZPvecSIEfj4+FC3bl3atm1L586d39iTg08++YTt27dTp06dLOvo4vhV5JJFfHw8xYsX1yozNjYmISFBq+zZs2eZ1nv+/LnOY8xrStv8sqSkJCZMmICxsTGffPKJrkPMUzlp78yZM/n000+pUaNGfoWnE0rb/PjxY6Kjo7l69SoBAQH88MMPHD58mG+//TY/w80TOfmeVSoVnp6enD9/Hn9/fw4cOMDWrVvzK9Q8pSSx6+L4VeSShYmJSYb5rhISErQycE7qvQly2pZ79+4xYMAAHjx4wHfffZfhj66wU9re7du38+jRIwYOHJif4emE0jYbGhqSmprK5MmTMTU1pXLlyri5ubF///78DDdPKG3zX3/9hZ+fHwMGDMDIyIhatWrh7u7O5s2b8zPcfKWL41eRSxbW1taa/ssXIiIisLGxyVDv1q1bWpk4s3pvAqVthvRL+549e1KlShX8/PwoVapUfoWZZ5S2NzAwkJCQEBo3bkyjRo0IDg7G19cXDw+P/Aw3Tyhts5WVFQBPnz7VlKWmpuo+QB1Q2uaYmBiSk5O17uorVqwYxYopWij0jaST41euh8bfUImJiepWrVpp3UFhZ2enfvDgQYa6H330kfrrr79WJyYmqk+ePKm2tbVVh4WFFUDUr0dpmx8/fqxu2bKleu7cuQUUad7IyXf8suHDh7/Rd0MpbXOPHj3UI0eOVD979kx98+ZNdbt27dTr1q0rgKhfj9I2379/X92oUSP1kiVL1MnJyerIyEh127Zt1X5+fgUUed44derUK++GyuvjV5FLFmq1Wh0WFqb+5JNP1La2turOnTurT5w4oVar1Wp/f3+1ra2tpt6tW7fU7u7u6gYNGqidnZ3VgYGBBRXya1PS5g0bNqhr1Kihrl+/vtrW1lbzM27cuIIMPVeUfscve5OThVqtvM0PHjxQjx8/Xm1vb69u2rSpev78+eqUlJSCCvu1KG3z+fPn1X369FE3bNhQ7eTkpF69erU6NTW1oMLOE/9NFro+fskT3EIIIbJV5MYshBBC5JwkCyGEENmSZCGEECJbkiyEEEJkS5KFEEKIbEmyEEIIkS1JFoVU69atqVmzZqY/KSkp2b7fx8eHHj165Ets77//Pk5OTsybN4/4+PjX3v7p06epWbMmz549A9LXLDh16pTm9Zo1a3LkyJHX3s9/bd++PcNnXadOHZycnJg/f36OnnQ+c+YMly9ffq14bt++Tbdu3UhOTtZ8Ji//1K9fn969e2c5m25OTZ48mTFjxgDps7P+/PPPmikjtm/fTtOmTfNkP/+Vk+/z5RhzI7fvf/LkCQ4ODlqTFMbExNCtWzeSkpJyHc+b5O193v0tMH78+EwP+IVhmoKXY0tLSyMiIoLPP/+cp0+fMnv27Nfatp2dHcePH9fMYzNixAgGDx5Ms2bNADh+/LjOpiExMzNj9+7dmt+fP3/OqVOn+OKLLzAzM2Po0KGKttO/f39Wr15N7dq1cx3Ll19+yZAhQ7Rmwz148KBmrq7ExEQCAgIYN24c7777Lo0aNcr1vgCmTZummRLj7NmzeHl50alTJ4yMjOjYsSOOjo6vtf031ZMnTxg2bBj37t3TKi9Xrhz29vb4+voyatSoAoou/8iVRSFmamqKpaVlhp/C4OXYypYti729PQMGDGDfvn2vvW1DQ0MsLS1RqVSZvm5paYmhoeFr7ycrL3/WlStXplevXnTt2pWgoCCd7fO/Ll26xIULF+jQoYNWubm5uSa2SpUqMWLECKpWrZonn3vJkiV55513ADKsjli8eHEsLCxeex9vmhMnTtC9e/csZ2ju378/3333HXFxcfkcWf6TZPEG8/Pzo127dtSpU4cmTZowceJETdfNf61YsQJHR0fq1q1Lt27dtJZYvHfvHmPHjsXOzg4HBwemTZtGbGxsjuPR19fXOogfP36cXr16YWtri5OTE99++63mIBQXF8eECRNo2rQptra2uLu7c/36dUC7G6p///5ER0cza9Ys+vfvD/zbbbF161bs7e21uocuX75MrVq1iImJAWDnzp20a9eO+vXr0717d3799dcctwvSE5ie3r//XY4dO8bHH39MvXr1qF+/PgMGDODatWtAejcdwLBhw5g8eTKQvhiNu7s79evX13Rrvar74vvvv8fZ2Rl9ff1sYytWrJjmc09KSmLZsmW0bt2aunXr0qdPHy5cuKCp+/vvv9OzZ0/q1auHg4ODVvfaiy6amzdvMmDAAAAaNGjA9u3btbqhPv30U+bMmaMVw+zZsxk8eDCQ/t16eXlplm0dM2YMd+7cybYdkD7tuLe3Nw4ODnzwwQc4OjqycuVKrToJCQlMmDCBevXq0bp16wzdcDn5zmvWrPnKpXSPHTtGv379WLZsWaavV6hQgSpVqvDLL78oat+bTJLFG2r37t34+PgwefJk9u3bx5w5czh06BA//fRThroHDhxg48aNLFiwgKCgIBwdHfnss880Z0OjR49GrVbz008/sWrVKm7cuMG4ceMUx5KWlkZISAg//PADLi4uQHo3xtChQ3FycmLHjh2MGzeOlStX8uOPPwKwZMkSbt68ycaNG9m+fTt6enpMnTo1w7Z9fHwoV64c48ePz/Cful27dsTFxXH69GlNWWBgII0bN6ZcuXIcO3aMr776ijFjxrBr1y569+7NmDFjOH/+fI7adujQIXbu3Kk5y4+Ojmb48OG0b9+ewMBANmzYwJMnT1iwYAGAZjWy+fPnM23aNBITE3F3d6dy5crs2LGD+fPna2LLytGjR2nZsuUrY0tISMDPz49r167h7OwMpHdd/fLLL8ycOZOdO3dSvXp1Bg0axN27d0lNTWXkyJF8+OGH7Nmzh/nz57N161a2b9+utd3y5ctrPuuDBw/SsWNHrdc7d+7M3r17NYk/LS2NvXv3albbmzFjBpGRkXz77bd8//33qFQqhgwZomisbe7cufz555+sXLmSvXv30r9/f5YuXcrFixe1PpvSpUuzY8cO3NzcmDBhAr///jtAjr/z48ePa5JcZjw9PRk8eLDWicJ/tWzZkqNHj2bbtjddwXd+iyzNnTuXRYsWaZWtXr2apk2b8u677zJ37lycnJwAqFixIk2aNCE8PDzDdqKjozEwMKBChQpUqlSJkSNH0rhxY4oVK8apU6cICwtj48aNmrPThQsX0qpVK65cuZLlokAvx5aUlIRKpaJ169Z8/vnnQPqZccuWLRkxYgQA1apVIyYmhtWrV9O3b1+io6MxNTWlUqVKmJqa8tVXXxEdHZ1hP2ZmZujr62NqaoqZmZnWa++88w6Ojo4EBQXRvHlzAIKCgjRTjK9ZswZ3d3c6deoEwHvvvcelS5f47rvvsLOzy7Rdjx8/1notMTGRd999l6FDh2oOKqmpqXh6emqudCpXrkzXrl01idDc3FwTX8mSJfnll18wMDBg5syZqFQqrKys+OKLL+jbty+TJk2iRIkSGb6vBw8eZDqd9Muru8XHx2NhYcH06dNp0KABT58+Zfv27SxevFgzvuDt7c0ff/zBpk2bGDRoEI8fP+bdd9+lUqVKVKpUiXXr1mXoXtLX19eMCZmbm2dYz6RDhw58/fXXnD9/ngYNGvD777/z5MkT2rRpQ1RUFIGBgRw9elSzSM+CBQto2rQpx44d0/y9ZqVBgwb07t2bDz74AIAhQ4awYsUKwsPDNSvDVatWjWnTpqFSqbC2tub06dNs3ryZRo0a5fg7z4tuXRsbG7Zs2fLa2ynsJFkUYh4eHri6umqVvfgP2KRJE/766y+++eYbIiMjuXr1KpGRkXTr1i3DdlxdXdmxYwcuLi588MEHtG7dmo8++ojixYsTHh5OQkJCpne6REZGZpksXo7NwMCAMmXKaHVBXb16NUPsDRs2ZNGiRTx9+hQPDw88PDywt7encePGuLi40LVr1xx9PpB+luvt7c3MmTO5dOkSd+7coV27dpoYLly4gK+vr6Z+cnIy1apVy3J7pUqV0qygFhERwRdffIG9vT1Dhw7VnF2+9957tG3bFl9fX8LDw4mIiCA0NJR33303022Gh4cTFRVFgwYNNGVqtZq0tDSuX7+eYXnMBw8eAFC6dOkM29q8eTPGxsaoVCqMjY21DnaRkZGkpqZqHRT19PSws7Pj6tWrmJmZ4eHhgZeXFz4+Pjg6OtKpUyfq1auX5eeRmdKlS9OiRQuCgoJo0KABgYGBODk5UaJECc6ePQtA+/bttd6TkJBAZGRktsnC1dWVI0eO4O/vz/Xr17l8+TLx8fGkpaVp6tSvX19rPKtu3bqa8aTcfOevy8zMjCdPnpCamqqo2/BNJcmiECtdujRVqlTJ9LXt27fj7e1Njx49NGfwWfWrmpubs337dk6ePMmvv/7Kjh072LBhAz/88AMpKSlUqFCB7777LsP7XjWg+arYgExX13u528LW1pZDhw5x5MgRjh49yjfffMOPP/6Y4wXlnZycmD59OqdOneLYsWO0atVKc1acmprKhAkTMhygXnU3mUql0rSrSpUqlCtXjl69emFmZoanpycAV65coXfv3rRo0YLGjRvTq1cvLly4oLmy+K+UlBRsbW0z9PND5ktkvjgQZjYhdOXKlTE1Nc10P0ZGRpmWv0hMkH4XW/fu3Tl06BDBwcEMHjyYsWPHMmzYsEzfm5UuXbowf/58Jk2axP79+5k1axaQ/pkbGBiwY8eODDcoKLmDberUqZw4cYJu3brRrVs3vL29M5xE/LdLKC0tTXPHWG6+89eVlpaGnp5eljdkvC1kzOINtWnTJtzd3fH29qZXr17UrFmTf/75J9MDzP79+9myZQsODg5Mnz6dvXv3YmpqSnBwMNbW1ty9exdTU1OqVKlClSpVMDAwYO7cuTx8+DDX8VlZWfHnn39qlZ0/fx4LCwtKlSrFmjVrCAkJoUuXLixYsICffvqJK1eu5HhBeSMjI9q0acPhw4c5cOCApt8c0lcLi46O1rSrSpUqBAUFERgYqHj7tWvXxsPDAz8/P0JCQgDYsWMH77//PsuXL2fgwIE0btyYmzdvZvrZv4jjn3/+oVy5cpo4njx5wqJFi0hOTs5Q/8XVwqNHj3LyUWi+u5f759VqNRcuXMDKyopbt24xc+ZMKlasyJAhQ/j+++/53//+R0BAQIZtZXfgc3Z2JjY2lo0bN5KSkkKrVq2A9O89OTmZhIQETVstLS2ZP3++5gaGrMTFxeHv78+8efMYP348HTt2xMDAgNjYWK3P9r9/IxcuXNB02eXFd55Tjx49wtzc/JXjGm+Dt7t1bzEzMzNOnz5NeHg4V69eZfr06YSHh2d6h01qaioLFixg3759REdHs3//fu7du0edOnVo0aIF1atXZ9y4cVy8eJHQ0FAmTpxIVFQUFStWzHV8Q4YM4dixY6xcuZLr16+zZ88efH196d+/PyqVipiYGL766ivOnTtHVFQUO3bsoESJElStWjXDtkxNTbl27Zqme+a/unTpwo4dO3j8+LHWGeWQIUPYsmULmzdv5saNG/z888/4+PjkuF0eHh5UrlwZb29v0tLSKF26NBEREfz+++9ERUXh5+fH1q1btT57ExMTrl69yuPHj3F1dUVPTw9PT0+uXLnC+fPnmTJlCvHx8ZQsWTLD/sqWLYulpSWhoaE5itPY2Jh+/foxZ84cgoODuXbtGrNmzSIqKoqPP/4Yc3Nz9u3bx+zZs/nnn3+4dOkSv/32G3Xr1s2wrRfPuFy6dCnTO+yMjY1xdnZm+fLltGvXTtMFaWVlRevWrZk0aRK///47165dw9PTU5OwXsXIyAhjY2MOHDhAVFQUf/zxh+bmi5c/28uXL7Nw4UKuXbvGunXrtAapc/qd37t3L8s7CJUKDQ3N0JX4NpJk8YZ6McD30UcfMWjQIJKSkvDw8ODvv//OULdDhw6MHj2a+fPn065dO5YsWcKMGTOwt7dHT0+PVatWYWZmxoABA+jXrx9mZmb4+vq+Vv9r7dq18fHxYe/evXTu3JnFixczYsQITXfHxIkTadCgAaNGjaJjx46cOnUKX19fzX3+LxswYAD+/v64u7tnuq9mzZphampKmzZttLq/2rRpg5eXF35+fnTs2JF169Yxc+ZMzeCnUoaGhsyYMYNLly6xefNm+vfvT/PmzRk2bBg9evTgyJEjeHt78+DBA80tu+7u7qxYsYJp06ZhYmLC+vXrefr0Kb169WLYsGHUrVs3w80LL6hUKlq2bMmZM2dyFCegOSOfMmUKPXr04MqVK2zcuJGqVatSvHhx1qxZw5UrV+jWrRuDBg2iRo0aTJ8+PcN2atSogZOTE4MHD+bnn3/OdF9dunQhPj6ezp07a5XPmzePOnXqMHLkSHr27ElsbCzr16/P9Lt9mYGBAYsWLeLEiRN06tQJT09PWrRogaOjI5cuXdLUc3V11YzP/fzzzyxdulQztpbT79zBwYH169e/Mq7snD17NtuxmLeBrJQnRCEUEhLC0KFDOXr0qE4fQBSvJzIyko8//pgjR45kuKvtbSNXFkIUQvXq1aNevXp5Nu+T0I1NmzbRv3//tz5RgCQLIQqtGTNmsH79+kwHwUXBi4mJ4fTp05rnet520g0lhBAiW3JlIYQQIluSLIQQQmRLkoUQQohsSbIQQgiRLUkWQgghsvV/xJphPjVQVakAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate AUC metric\n",
    "plot_roc_curve(rs_NB_gs, x_valid, y_valid);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "f624996e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[21596  1399]\n",
      " [  116  6744]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "print(confusion_matrix(y_valid, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "58a04b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Improve visualization for confusion matrix\n",
    "sns.set(font_scale = 1.2)\n",
    "def plot_conf_mat(y_valid, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a confusion matrix using seaborn's heatmap.\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize = (3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_valid, y_preds),\n",
    "                     annot = True,\n",
    "                     cbar = False)\n",
    "            \n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    \n",
    "plot_conf_mat(y_valid, y_preds);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb2472f",
   "metadata": {},
   "source": [
    "## Predictions on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "b8f44e95",
   "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>row ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Row0</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Row1</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Row2</td>\n",
       "      <td>Albury</td>\n",
       "      <td>14.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>50.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1009.6</td>\n",
       "      <td>1008.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Row3</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.7</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NNW</td>\n",
       "      <td>80.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1008.9</td>\n",
       "      <td>1003.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.3</td>\n",
       "      <td>30.2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Row4</td>\n",
       "      <td>Albury</td>\n",
       "      <td>15.9</td>\n",
       "      <td>18.6</td>\n",
       "      <td>15.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>994.3</td>\n",
       "      <td>993.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>15.8</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  row ID Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0   Row0   Albury     12.9     25.7       0.0          NaN       NaN   \n",
       "1   Row1   Albury      9.2     28.0       0.0          NaN       NaN   \n",
       "2   Row2   Albury     14.3     25.0       0.0          NaN       NaN   \n",
       "3   Row3   Albury      9.7     31.9       0.0          NaN       NaN   \n",
       "4   Row4   Albury     15.9     18.6      15.6          NaN       NaN   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... WindSpeed3pm  Humidity9am  \\\n",
       "0         WSW           46.0          W  ...         26.0         38.0   \n",
       "1          NE           24.0         SE  ...          9.0         45.0   \n",
       "2           W           50.0         SW  ...         24.0         49.0   \n",
       "3         NNW           80.0         SE  ...         28.0         42.0   \n",
       "4           W           61.0        NNW  ...         28.0         76.0   \n",
       "\n",
       "   Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  \\\n",
       "0         30.0       1007.6       1008.7       NaN       2.0     21.0   \n",
       "1         16.0       1017.6       1012.8       NaN       NaN     18.1   \n",
       "2         19.0       1009.6       1008.2       1.0       NaN     18.1   \n",
       "3          9.0       1008.9       1003.6       NaN       NaN     18.3   \n",
       "4         93.0        994.3        993.0       8.0       8.0     17.4   \n",
       "\n",
       "   Temp3pm  RainToday  \n",
       "0     23.2         No  \n",
       "1     26.5         No  \n",
       "2     24.6         No  \n",
       "3     30.2         No  \n",
       "4     15.8        Yes  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import test data\n",
    "df_test = pd.read_csv(\"data/weather-test-data.csv\")\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "942f7593",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42677 entries, 0 to 42676\n",
      "Data columns (total 22 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   row ID         42677 non-null  object \n",
      " 1   Location       42677 non-null  object \n",
      " 2   MinTemp        42483 non-null  float64\n",
      " 3   MaxTemp        42585 non-null  float64\n",
      " 4   Rainfall       42250 non-null  float64\n",
      " 5   Evaporation    24365 non-null  float64\n",
      " 6   Sunshine       22178 non-null  float64\n",
      " 7   WindGustDir    39868 non-null  object \n",
      " 8   WindGustSpeed  39887 non-null  float64\n",
      " 9   WindDir9am     39670 non-null  object \n",
      " 10  WindDir3pm     41547 non-null  object \n",
      " 11  WindSpeed9am   42264 non-null  float64\n",
      " 12  WindSpeed3pm   41882 non-null  float64\n",
      " 13  Humidity9am    42136 non-null  float64\n",
      " 14  Humidity3pm    41573 non-null  float64\n",
      " 15  Pressure9am    38411 non-null  float64\n",
      " 16  Pressure3pm    38432 non-null  float64\n",
      " 17  Cloud9am       26592 non-null  float64\n",
      " 18  Cloud3pm       25585 non-null  float64\n",
      " 19  Temp9am        42387 non-null  float64\n",
      " 20  Temp3pm        41855 non-null  float64\n",
      " 21  RainToday      42250 non-null  object \n",
      "dtypes: float64(16), object(6)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "a5c2562e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(df):\n",
    "    \"\"\"\n",
    "    Performs transformations on df and returns transformed df.\n",
    "    \"\"\"\n",
    "    for label, content in df_test.items():\n",
    "        if pd.api.types.is_numeric_dtype(content):\n",
    "            if pd.isnull(content).sum():\n",
    "                # Fill missing data\n",
    "                df_test[label] = content.fillna(content.median())\n",
    "                \n",
    "\n",
    "    num_na=df['WindGustDir'].isna().sum()\n",
    "    df.loc[df.WindGustDir.isna(),\"WindGustDir\"]=random.choices(['W','WNW','N','NNE','SW','ENE','SSE','NE','WSW','NNW','S','ESE','NW','E','SSW','SE'], k=num_na)\n",
    "    \n",
    "    wind_dir_na=df['WindDir9am'].isna().sum()\n",
    "    df.loc[df.WindDir9am.isna(),\"WindDir9am\"]=random.choices(['W','NNW','ENE','SSE','S','NE','SSW','N','WSW','SE','ESE','E','NW','NNE','SW','WNW'], k=wind_dir_na)\n",
    "    \n",
    "    wind_dir_three_na=df['WindDir3pm'].isna().sum()\n",
    "    df.loc[df.WindDir3pm.isna(),\"WindDir3pm\"]=random.choices(['WNW','WSW','NW','W','SSE','ESE','ENE','SSW','E','SW','NNW','N','S','SE','NNE','NE'], k=wind_dir_three_na)\n",
    "    \n",
    "    rain_today_na=df_test['RainToday'].isna().sum()\n",
    "    df_test.loc[df_test.RainToday.isna(),\"RainToday\"]=random.choices(['No','Yes'], k=rain_today_na)\n",
    "        \n",
    "    \n",
    "    df_test[\"new_column\"] = pd.get_dummies(df_test[\"RainToday\"], drop_first = True)\n",
    "    df_test.rename({\"new_column\" : \"Rain_Today\"}, axis = 1, inplace = True)\n",
    "    \n",
    "    le = LabelEncoder()\n",
    "\n",
    "    # Let's make a function\n",
    "    def label_encoding_df_cols (col_name, encoder, data_frame):\n",
    "        print(\"Unique values:\", data_frame[col_name].unique())\n",
    "        print(\"Unique value counts:\", data_frame[col_name].value_counts())\n",
    "        encoder.fit(data_frame[col_name]);\n",
    "        transform_encode = encoder.transform(data_frame[col_name])\n",
    "        data_frame[col_name] = transform_encode\n",
    "\n",
    "        return label_encoding_df_cols\n",
    "    \n",
    "    label_encoding_df_cols('WindGustDir', le ,df)\n",
    "    label_encoding_df_cols('WindDir9am', le ,df)\n",
    "    label_encoding_df_cols('WindDir3pm', le ,df)\n",
    "    label_encoding_df_cols('Location', le ,df)\n",
    "    \n",
    "               \n",
    "    df_test.drop([\"RainToday\", \"row ID\", \"Pressure9am\", \"Temp3pm\"], axis = 1, inplace = True)\n",
    "               \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "52650d1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: ['WSW' 'NE' 'W' 'NNW' 'S' 'WNW' 'NW' 'SE' 'NNE' 'N' 'SSE' 'ESE' 'SW' 'E'\n",
      " 'ENE' 'SSW']\n",
      "Unique value counts: W      3097\n",
      "SE     3010\n",
      "WSW    2935\n",
      "S      2922\n",
      "N      2915\n",
      "E      2906\n",
      "SSE    2825\n",
      "SSW    2764\n",
      "SW     2741\n",
      "ENE    2598\n",
      "NW     2570\n",
      "WNW    2557\n",
      "ESE    2402\n",
      "NE     2276\n",
      "NNW    2134\n",
      "NNE    2025\n",
      "Name: WindGustDir, dtype: int64\n",
      "Unique values: ['W' 'SE' 'SW' 'NNW' 'WNW' 'ENE' 'SSE' 'NE' 'N' 'E' 'ESE' 'WSW' 'NW' 'NNE'\n",
      " 'SSW' 'S']\n",
      "Unique value counts: N      3539\n",
      "SE     3057\n",
      "SSE    2938\n",
      "E      2892\n",
      "NW     2755\n",
      "S      2710\n",
      "W      2646\n",
      "SW     2600\n",
      "NNW    2582\n",
      "ENE    2555\n",
      "NNE    2540\n",
      "SSW    2482\n",
      "ESE    2424\n",
      "NE     2385\n",
      "WNW    2338\n",
      "WSW    2234\n",
      "Name: WindDir9am, dtype: int64\n",
      "Unique values: ['WSW' 'E' 'W' 'NW' 'NNW' 'SE' 'WNW' 'N' 'SSW' 'SW' 'NNE' 'S' 'NE' 'ENE'\n",
      " 'SSE' 'ESE']\n",
      "Unique value counts: SE     3324\n",
      "W      3082\n",
      "S      3022\n",
      "SSE    2821\n",
      "SW     2798\n",
      "WSW    2754\n",
      "WNW    2667\n",
      "N      2661\n",
      "NW     2619\n",
      "ESE    2602\n",
      "E      2572\n",
      "NE     2520\n",
      "SSW    2509\n",
      "ENE    2395\n",
      "NNW    2375\n",
      "NNE    1956\n",
      "Name: WindDir3pm, dtype: int64\n",
      "Unique values: ['Albury' 'BadgerysCreek' 'Cobar' 'CoffsHarbour' 'Moree' 'Newcastle'\n",
      " 'NorahHead' 'NorfolkIsland' 'Penrith' 'Richmond' 'Sydney' 'SydneyAirport'\n",
      " 'WaggaWagga' 'Williamtown' 'Wollongong' 'Canberra' 'Tuggeranong'\n",
      " 'MountGinini' 'Ballarat' 'Bendigo' 'Sale' 'MelbourneAirport' 'Melbourne'\n",
      " 'Mildura' 'Nhil' 'Portland' 'Watsonia' 'Dartmoor' 'Brisbane' 'Cairns'\n",
      " 'GoldCoast' 'Townsville' 'Adelaide' 'MountGambier' 'Nuriootpa' 'Woomera'\n",
      " 'Albany' 'Witchcliffe' 'PearceRAAF' 'PerthAirport' 'Perth' 'SalmonGums'\n",
      " 'Walpole' 'Hobart' 'Launceston' 'AliceSprings' 'Darwin' 'Katherine'\n",
      " 'Uluru']\n",
      "Unique value counts: Canberra            1025\n",
      "Sydney               976\n",
      "Darwin               975\n",
      "Albany               965\n",
      "Brisbane             959\n",
      "Launceston           956\n",
      "Hobart               949\n",
      "Woomera              937\n",
      "Perth                931\n",
      "Townsville           927\n",
      "NorfolkIsland        926\n",
      "Bendigo              924\n",
      "SalmonGums           924\n",
      "GoldCoast            923\n",
      "Adelaide             912\n",
      "AliceSprings         912\n",
      "WaggaWagga           908\n",
      "Sale                 907\n",
      "Ballarat             906\n",
      "SydneyAirport        905\n",
      "Penrith              905\n",
      "NorahHead            901\n",
      "Tuggeranong          899\n",
      "Cobar                898\n",
      "Wollongong           893\n",
      "Nuriootpa            892\n",
      "Witchcliffe          892\n",
      "Richmond             891\n",
      "MountGambier         890\n",
      "Cairns               887\n",
      "CoffsHarbour         887\n",
      "BadgerysCreek        887\n",
      "Watsonia             886\n",
      "Mildura              883\n",
      "Portland             883\n",
      "MountGinini          882\n",
      "Dartmoor             876\n",
      "MelbourneAirport     870\n",
      "Albury               869\n",
      "Newcastle            859\n",
      "PerthAirport         842\n",
      "Moree                834\n",
      "Walpole              830\n",
      "PearceRAAF           809\n",
      "Melbourne            740\n",
      "Williamtown          737\n",
      "Katherine            494\n",
      "Uluru                481\n",
      "Nhil                 433\n",
      "Name: Location, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Rain_Today</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>15</td>\n",
       "      <td>46.0</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>19.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>14.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>13</td>\n",
       "      <td>50.0</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1008.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>9.7</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1003.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>18.6</td>\n",
       "      <td>15.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>13</td>\n",
       "      <td>61.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>993.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustDir  \\\n",
       "0         2     12.9     25.7       0.0          4.8       8.5           15   \n",
       "1         2      9.2     28.0       0.0          4.8       8.5            4   \n",
       "2         2     14.3     25.0       0.0          4.8       8.5           13   \n",
       "3         2      9.7     31.9       0.0          4.8       8.5            6   \n",
       "4         2     15.9     18.6      15.6          4.8       8.5           13   \n",
       "\n",
       "   WindGustSpeed  WindDir9am  WindDir3pm  WindSpeed9am  WindSpeed3pm  \\\n",
       "0           46.0          13          15          19.0          26.0   \n",
       "1           24.0           9           0          11.0           9.0   \n",
       "2           50.0          12          13          20.0          24.0   \n",
       "3           80.0           9           7           7.0          28.0   \n",
       "4           61.0           6           6          28.0          28.0   \n",
       "\n",
       "   Humidity9am  Humidity3pm  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  \\\n",
       "0         38.0         30.0       1008.7       5.0       2.0     21.0   \n",
       "1         45.0         16.0       1012.8       5.0       5.0     18.1   \n",
       "2         49.0         19.0       1008.2       1.0       5.0     18.1   \n",
       "3         42.0          9.0       1003.6       5.0       5.0     18.3   \n",
       "4         76.0         93.0        993.0       8.0       8.0     17.4   \n",
       "\n",
       "   Rain_Today  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           1  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Process test data\n",
    "df_test = preprocess_data(df_test)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "6faee3ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42677 entries, 0 to 42676\n",
      "Data columns (total 19 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Location       42677 non-null  int32  \n",
      " 1   MinTemp        42677 non-null  float64\n",
      " 2   MaxTemp        42677 non-null  float64\n",
      " 3   Rainfall       42677 non-null  float64\n",
      " 4   Evaporation    42677 non-null  float64\n",
      " 5   Sunshine       42677 non-null  float64\n",
      " 6   WindGustDir    42677 non-null  int32  \n",
      " 7   WindGustSpeed  42677 non-null  float64\n",
      " 8   WindDir9am     42677 non-null  int32  \n",
      " 9   WindDir3pm     42677 non-null  int32  \n",
      " 10  WindSpeed9am   42677 non-null  float64\n",
      " 11  WindSpeed3pm   42677 non-null  float64\n",
      " 12  Humidity9am    42677 non-null  float64\n",
      " 13  Humidity3pm    42677 non-null  float64\n",
      " 14  Pressure3pm    42677 non-null  float64\n",
      " 15  Cloud9am       42677 non-null  float64\n",
      " 16  Cloud3pm       42677 non-null  float64\n",
      " 17  Temp9am        42677 non-null  float64\n",
      " 18  Rain_Today     42677 non-null  uint8  \n",
      "dtypes: float64(14), int32(4), uint8(1)\n",
      "memory usage: 5.3 MB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "481f34d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Win10\\Desktop\\data-science-projects\\australia-rainfall-prediction-project\\env\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but GaussianNB was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=uint8)"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions on updated test data\n",
    "test_preds = gs_NB_gs.predict(df_test)\n",
    "test_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "955b28e4",
   "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>Location</th>\n",
       "      <th>Rain Tomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location  Rain Tomorrow\n",
       "0         2              1\n",
       "1         2              1\n",
       "2         2              1\n",
       "3         2              1\n",
       "4         2              1"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format of predictions\n",
    "df_preds = pd.DataFrame()\n",
    "df_preds [\"Location\"] = df_test[\"Location\"]\n",
    "df_preds[\"Rain Tomorrow\"] = test_preds\n",
    "df_preds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f6ce0e2",
   "metadata": {},
   "source": [
    "We need to convert the numbers back to country names and 1 0 to True and False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "48a24389",
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_y_preds = rs_NB_gs.predict(x_valid)\n",
    "def evaluate_preds (y_valid, test_preds):\n",
    "    \"\"\"\n",
    "    Performs evaluation comparison on y_test labels and y_preds labels on our classification model.\n",
    "    \"\"\"\n",
    "    accuracy = accuracy_score(y_valid, test_preds)\n",
    "    precision = precision_score(y_valid, test_preds)\n",
    "    recall = recall_score(y_valid, test_preds)\n",
    "    f1 = f1_score(y_valid, test_preds)\n",
    "    \n",
    "    metric_dict = {\"accuracy\" : round(accuracy, 2),\n",
    "                   \"precision\" : round(precision, 2),\n",
    "                   \"recall\" : round(recall, 2),\n",
    "                   \"f1\" : round(f1, 2)} \n",
    "    print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
    "    print(f\"Precision: {precision:.2f}\")\n",
    "    print(f\"Recall: {recall:.2f}\")\n",
    "    print(f\"F1: {f1:.2f}\")\n",
    "    \n",
    "    return metric_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "71769211",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 95.27%\n",
      "Precision: 0.84\n",
      "Recall: 0.98\n",
      "F1: 0.90\n"
     ]
    }
   ],
   "source": [
    "test_preds = gs_NB_gs.predict(x_valid)\n",
    "gs_metrics = evaluate_preds(y_valid, test_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89acc1f4",
   "metadata": {},
   "source": [
    "## Permutation Importance\n",
    "\n",
    "Let's find the permutation importance for our `GaussianNB()` model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "ad24e4ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=GaussianNB(), n_iter=20,\n",
       "                   param_distributions={&#x27;var_smoothing&#x27;: array([1.00000000e+00, 8.11130831e-01, 6.57933225e-01, 5.33669923e-01,\n",
       "       4.32876128e-01, 3.51119173e-01, 2.84803587e-01, 2.31012970e-01,\n",
       "       1.87381742e-01, 1.51991108e-01, 1.23284674e-01, 1.00000000e-01,\n",
       "       8.11130831e-02, 6.57933225e-02, 5.33669923e-02, 4.32876128e-02,\n",
       "       3.5111917...\n",
       "       1.23284674e-07, 1.00000000e-07, 8.11130831e-08, 6.57933225e-08,\n",
       "       5.33669923e-08, 4.32876128e-08, 3.51119173e-08, 2.84803587e-08,\n",
       "       2.31012970e-08, 1.87381742e-08, 1.51991108e-08, 1.23284674e-08,\n",
       "       1.00000000e-08, 8.11130831e-09, 6.57933225e-09, 5.33669923e-09,\n",
       "       4.32876128e-09, 3.51119173e-09, 2.84803587e-09, 2.31012970e-09,\n",
       "       1.87381742e-09, 1.51991108e-09, 1.23284674e-09, 1.00000000e-09])},\n",
       "                   random_state=42, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=GaussianNB(), n_iter=20,\n",
       "                   param_distributions={&#x27;var_smoothing&#x27;: array([1.00000000e+00, 8.11130831e-01, 6.57933225e-01, 5.33669923e-01,\n",
       "       4.32876128e-01, 3.51119173e-01, 2.84803587e-01, 2.31012970e-01,\n",
       "       1.87381742e-01, 1.51991108e-01, 1.23284674e-01, 1.00000000e-01,\n",
       "       8.11130831e-02, 6.57933225e-02, 5.33669923e-02, 4.32876128e-02,\n",
       "       3.5111917...\n",
       "       1.23284674e-07, 1.00000000e-07, 8.11130831e-08, 6.57933225e-08,\n",
       "       5.33669923e-08, 4.32876128e-08, 3.51119173e-08, 2.84803587e-08,\n",
       "       2.31012970e-08, 1.87381742e-08, 1.51991108e-08, 1.23284674e-08,\n",
       "       1.00000000e-08, 8.11130831e-09, 6.57933225e-09, 5.33669923e-09,\n",
       "       4.32876128e-09, 3.51119173e-09, 2.84803587e-09, 2.31012970e-09,\n",
       "       1.87381742e-09, 1.51991108e-09, 1.23284674e-09, 1.00000000e-09])},\n",
       "                   random_state=42, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=GaussianNB(), n_iter=20,\n",
       "                   param_distributions={'var_smoothing': array([1.00000000e+00, 8.11130831e-01, 6.57933225e-01, 5.33669923e-01,\n",
       "       4.32876128e-01, 3.51119173e-01, 2.84803587e-01, 2.31012970e-01,\n",
       "       1.87381742e-01, 1.51991108e-01, 1.23284674e-01, 1.00000000e-01,\n",
       "       8.11130831e-02, 6.57933225e-02, 5.33669923e-02, 4.32876128e-02,\n",
       "       3.5111917...\n",
       "       1.23284674e-07, 1.00000000e-07, 8.11130831e-08, 6.57933225e-08,\n",
       "       5.33669923e-08, 4.32876128e-08, 3.51119173e-08, 2.84803587e-08,\n",
       "       2.31012970e-08, 1.87381742e-08, 1.51991108e-08, 1.23284674e-08,\n",
       "       1.00000000e-08, 8.11130831e-09, 6.57933225e-09, 5.33669923e-09,\n",
       "       4.32876128e-09, 3.51119173e-09, 2.84803587e-09, 2.31012970e-09,\n",
       "       1.87381742e-09, 1.51991108e-09, 1.23284674e-09, 1.00000000e-09])},\n",
       "                   random_state=42, verbose=1)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit an instance of GaussianNB\n",
    "rs_NB_gs.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "4f227458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Location': 6.699045386016067e-06,\n",
       " 'MinTemp': 0.0004957293585663658,\n",
       " 'MaxTemp': -0.0039926310500754125,\n",
       " 'Rainfall': 0.28294757996985426,\n",
       " 'Evaporation': -0.0016613632557360924,\n",
       " 'Sunshine': -0.0073019594707754544,\n",
       " 'WindGustDir': -0.0009512644448166574,\n",
       " 'WindGustSpeed': -0.0010785463071512735,\n",
       " 'WindDir9am': -0.00020097136158103712,\n",
       " 'WindDir3pm': -0.000756992128621703,\n",
       " 'WindSpeed9am': -0.00028805895159944583,\n",
       " 'WindSpeed3pm': -0.0003416513146876854,\n",
       " 'Humidity9am': -0.005647295260425423,\n",
       " 'Humidity3pm': -0.009800703399765553,\n",
       " 'Pressure3pm': -0.00030815608775753845,\n",
       " 'Cloud9am': -0.005205158264947296,\n",
       " 'Cloud3pm': -0.004796516496399316,\n",
       " 'Temp9am': -0.0013398090772065662,\n",
       " 'RainTomorrow': -0.007134483336124653}"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.inspection import permutation_importance\n",
    "imps = permutation_importance(rs_NB_gs, x_valid, y_valid)\n",
    "perm_dict = dict(zip(df.columns, list(imps.importances_mean)))\n",
    "perm_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "2bfce6ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "perm_df = pd.DataFrame(perm_dict, index = [0])\n",
    "perm_df.T.plot.bar(title = \"Permuntation Importance\", legend = False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "3d92c606",
   "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>Location</th>\n",
       "      <th>Rain Tomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location  Rain Tomorrow\n",
       "0         2              1\n",
       "1         2              1\n",
       "2         2              1\n",
       "3         2              1\n",
       "4         2              1"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format of predictions\n",
    "df_preds = pd.DataFrame()\n",
    "df_preds [\"Location\"] = df_test[\"Location\"]\n",
    "df_preds[\"Rain Tomorrow\"] = test_preds\n",
    "df_preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "4828bc5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_preds.to_csv(\"data/weather-test-predictions.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b0bdc9d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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