Data Cleaning and RF Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Indyscout\\Documents\\Masters_Program\\MSBX_5415_R2\\project\n"
     ]
    }
   ],
   "source": [
    "# Libraries\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import os\n",
    "os.chdir('C:\\\\Users\\\\Indyscout\\\\Documents\\\\Masters_Program\\\\MSBX_5415_R2\\\\project')\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(145460, 24)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>State</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>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</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>...</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",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</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>...</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",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Location            State  MinTemp  MaxTemp  Rainfall  \\\n",
       "0 2008-12-01   Albury  New_South_Wales     13.4     22.9       0.6   \n",
       "1 2008-12-02   Albury  New_South_Wales      7.4     25.1       0.0   \n",
       "2 2008-12-03   Albury  New_South_Wales     12.9     25.7       0.0   \n",
       "3 2008-12-04   Albury  New_South_Wales      9.2     28.0       0.0   \n",
       "4 2008-12-05   Albury  New_South_Wales     17.5     32.3       1.0   \n",
       "\n",
       "   Evaporation  Sunshine WindGustDir  WindGustSpeed  ... Humidity9am  \\\n",
       "0          NaN       NaN           W           44.0  ...        71.0   \n",
       "1          NaN       NaN         WNW           44.0  ...        44.0   \n",
       "2          NaN       NaN         WSW           46.0  ...        38.0   \n",
       "3          NaN       NaN          NE           24.0  ...        45.0   \n",
       "4          NaN       NaN           W           41.0  ...        82.0   \n",
       "\n",
       "  Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  \\\n",
       "0        22.0       1007.7       1007.1       8.0       NaN     16.9     21.8   \n",
       "1        25.0       1010.6       1007.8       NaN       NaN     17.2     24.3   \n",
       "2        30.0       1007.6       1008.7       NaN       2.0     21.0     23.2   \n",
       "3        16.0       1017.6       1012.8       NaN       NaN     18.1     26.5   \n",
       "4        33.0       1010.8       1006.0       7.0       8.0     17.8     29.7   \n",
       "\n",
       "   RainToday  RainTomorrow  \n",
       "0         No            No  \n",
       "1         No            No  \n",
       "2         No            No  \n",
       "3         No            No  \n",
       "4         No            No  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"weatherAUS_states.csv\", parse_dates = ['Date'])\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Cloud9am</th>\n",
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       "      <th>Temp3pm</th>\n",
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       "      <th>RainTomorrow</th>\n",
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       "      <th>count</th>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
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       "      <td>8</td>\n",
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       "      <th>top</th>\n",
       "      <td>2013-12-22 00:00:00</td>\n",
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       "      <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>No</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>49</td>\n",
       "      <td>3436</td>\n",
       "      <td>46075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>9915</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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",
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       "      <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>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.194034</td>\n",
       "      <td>23.221348</td>\n",
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       "      <td>7.611178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.035230</td>\n",
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       "      <td>4.509930</td>\n",
       "      <td>16.990631</td>\n",
       "      <td>21.68339</td>\n",
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       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.398495</td>\n",
       "      <td>7.119049</td>\n",
       "      <td>8.478060</td>\n",
       "      <td>4.193704</td>\n",
       "      <td>3.785483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.607062</td>\n",
       "      <td>...</td>\n",
       "      <td>19.029164</td>\n",
       "      <td>20.795902</td>\n",
       "      <td>7.10653</td>\n",
       "      <td>7.037414</td>\n",
       "      <td>2.887159</td>\n",
       "      <td>2.720357</td>\n",
       "      <td>6.488753</td>\n",
       "      <td>6.93665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.500000</td>\n",
       "      <td>-4.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>6.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>-7.200000</td>\n",
       "      <td>-5.40000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1012.90000</td>\n",
       "      <td>1010.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>16.60000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
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       "      <td>22.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>52.000000</td>\n",
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       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>21.10000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1022.40000</td>\n",
       "      <td>1020.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>26.40000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.900000</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1041.00000</td>\n",
       "      <td>1039.600000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>40.200000</td>\n",
       "      <td>46.70000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Date  Location            State        MinTemp  \\\n",
       "count                145460    145460           145460  143975.000000   \n",
       "unique                 3436        49                8            NaN   \n",
       "top     2013-12-22 00:00:00  Canberra  New_South_Wales            NaN   \n",
       "freq                     49      3436            46075            NaN   \n",
       "first   2007-11-01 00:00:00       NaN              NaN            NaN   \n",
       "last    2017-06-25 00:00:00       NaN              NaN            NaN   \n",
       "mean                    NaN       NaN              NaN      12.194034   \n",
       "std                     NaN       NaN              NaN       6.398495   \n",
       "min                     NaN       NaN              NaN      -8.500000   \n",
       "25%                     NaN       NaN              NaN       7.600000   \n",
       "50%                     NaN       NaN              NaN      12.000000   \n",
       "75%                     NaN       NaN              NaN      16.900000   \n",
       "max                     NaN       NaN              NaN      33.900000   \n",
       "\n",
       "              MaxTemp       Rainfall   Evaporation      Sunshine WindGustDir  \\\n",
       "count   144199.000000  142199.000000  82670.000000  75625.000000      135134   \n",
       "unique            NaN            NaN           NaN           NaN          16   \n",
       "top               NaN            NaN           NaN           NaN           W   \n",
       "freq              NaN            NaN           NaN           NaN        9915   \n",
       "first             NaN            NaN           NaN           NaN         NaN   \n",
       "last              NaN            NaN           NaN           NaN         NaN   \n",
       "mean        23.221348       2.360918      5.468232      7.611178         NaN   \n",
       "std          7.119049       8.478060      4.193704      3.785483         NaN   \n",
       "min         -4.800000       0.000000      0.000000      0.000000         NaN   \n",
       "25%         17.900000       0.000000      2.600000      4.800000         NaN   \n",
       "50%         22.600000       0.000000      4.800000      8.400000         NaN   \n",
       "75%         28.200000       0.800000      7.400000     10.600000         NaN   \n",
       "max         48.100000     371.000000    145.000000     14.500000         NaN   \n",
       "\n",
       "        WindGustSpeed  ...    Humidity9am    Humidity3pm   Pressure9am  \\\n",
       "count   135197.000000  ...  142806.000000  140953.000000  130395.00000   \n",
       "unique            NaN  ...            NaN            NaN           NaN   \n",
       "top               NaN  ...            NaN            NaN           NaN   \n",
       "freq              NaN  ...            NaN            NaN           NaN   \n",
       "first             NaN  ...            NaN            NaN           NaN   \n",
       "last              NaN  ...            NaN            NaN           NaN   \n",
       "mean        40.035230  ...      68.880831      51.539116    1017.64994   \n",
       "std         13.607062  ...      19.029164      20.795902       7.10653   \n",
       "min          6.000000  ...       0.000000       0.000000     980.50000   \n",
       "25%         31.000000  ...      57.000000      37.000000    1012.90000   \n",
       "50%         39.000000  ...      70.000000      52.000000    1017.60000   \n",
       "75%         48.000000  ...      83.000000      66.000000    1022.40000   \n",
       "max        135.000000  ...     100.000000     100.000000    1041.00000   \n",
       "\n",
       "          Pressure3pm      Cloud9am      Cloud3pm        Temp9am  \\\n",
       "count   130432.000000  89572.000000  86102.000000  143693.000000   \n",
       "unique            NaN           NaN           NaN            NaN   \n",
       "top               NaN           NaN           NaN            NaN   \n",
       "freq              NaN           NaN           NaN            NaN   \n",
       "first             NaN           NaN           NaN            NaN   \n",
       "last              NaN           NaN           NaN            NaN   \n",
       "mean      1015.255889      4.447461      4.509930      16.990631   \n",
       "std          7.037414      2.887159      2.720357       6.488753   \n",
       "min        977.100000      0.000000      0.000000      -7.200000   \n",
       "25%       1010.400000      1.000000      2.000000      12.300000   \n",
       "50%       1015.200000      5.000000      5.000000      16.700000   \n",
       "75%       1020.000000      7.000000      7.000000      21.600000   \n",
       "max       1039.600000      9.000000      9.000000      40.200000   \n",
       "\n",
       "             Temp3pm  RainToday  RainTomorrow  \n",
       "count   141851.00000     142199        142193  \n",
       "unique           NaN          2             2  \n",
       "top              NaN         No            No  \n",
       "freq             NaN     110319        110316  \n",
       "first            NaN        NaN           NaN  \n",
       "last             NaN        NaN           NaN  \n",
       "mean        21.68339        NaN           NaN  \n",
       "std          6.93665        NaN           NaN  \n",
       "min         -5.40000        NaN           NaN  \n",
       "25%         16.60000        NaN           NaN  \n",
       "50%         21.10000        NaN           NaN  \n",
       "75%         26.40000        NaN           NaN  \n",
       "max         46.70000        NaN           NaN  \n",
       "\n",
       "[13 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe( include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145460 entries, 0 to 145459\n",
      "Data columns (total 24 columns):\n",
      " #   Column         Non-Null Count   Dtype         \n",
      "---  ------         --------------   -----         \n",
      " 0   Date           145460 non-null  datetime64[ns]\n",
      " 1   Location       145460 non-null  object        \n",
      " 2   State          145460 non-null  object        \n",
      " 3   MinTemp        143975 non-null  float64       \n",
      " 4   MaxTemp        144199 non-null  float64       \n",
      " 5   Rainfall       142199 non-null  float64       \n",
      " 6   Evaporation    82670 non-null   float64       \n",
      " 7   Sunshine       75625 non-null   float64       \n",
      " 8   WindGustDir    135134 non-null  object        \n",
      " 9   WindGustSpeed  135197 non-null  float64       \n",
      " 10  WindDir9am     134894 non-null  object        \n",
      " 11  WindDir3pm     141232 non-null  object        \n",
      " 12  WindSpeed9am   143693 non-null  float64       \n",
      " 13  WindSpeed3pm   142398 non-null  float64       \n",
      " 14  Humidity9am    142806 non-null  float64       \n",
      " 15  Humidity3pm    140953 non-null  float64       \n",
      " 16  Pressure9am    130395 non-null  float64       \n",
      " 17  Pressure3pm    130432 non-null  float64       \n",
      " 18  Cloud9am       89572 non-null   float64       \n",
      " 19  Cloud3pm       86102 non-null   float64       \n",
      " 20  Temp9am        143693 non-null  float64       \n",
      " 21  Temp3pm        141851 non-null  float64       \n",
      " 22  RainToday      142199 non-null  object        \n",
      " 23  RainTomorrow   142193 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(16), object(7)\n",
      "memory usage: 26.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date               0.0 %\n",
       "Location           0.0 %\n",
       "State              0.0 %\n",
       "MaxTemp           0.87 %\n",
       "MinTemp           1.02 %\n",
       "Temp9am           1.21 %\n",
       "WindSpeed9am      1.21 %\n",
       "Humidity9am       1.82 %\n",
       "Pressure3pm      10.33 %\n",
       "Pressure9am      10.36 %\n",
       "WindSpeed3pm      2.11 %\n",
       "Rainfall          2.24 %\n",
       "RainToday         2.24 %\n",
       "RainTomorrow      2.25 %\n",
       "Temp3pm           2.48 %\n",
       "WindDir3pm        2.91 %\n",
       "Humidity3pm        3.1 %\n",
       "Cloud9am         38.42 %\n",
       "Cloud3pm         40.81 %\n",
       "Evaporation      43.17 %\n",
       "Sunshine         48.01 %\n",
       "WindGustSpeed     7.06 %\n",
       "WindGustDir        7.1 %\n",
       "WindDir9am        7.26 %\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Thank you internet for showing me this code; percentage of NA values in each column\n",
    "empty_columns = data.apply(lambda x: f'{((x.isnull().sum()/data.shape[0])*100).round(2)} %').sort_values(ascending=True)\n",
    "empty_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am going to drop cloud 9am, cloud 3pm, evaporation, and sunshine because these have a high percentage of NA values\n",
    "data.drop(['Cloud9am', 'Cloud3pm', 'Evaporation', 'Sunshine'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Location', 'State', 'MinTemp', 'MaxTemp', 'Rainfall',\n",
       "       'WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm',\n",
       "       'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm',\n",
       "       'Pressure9am', 'Pressure3pm', 'Temp9am', 'Temp3pm', 'RainToday',\n",
       "       'RainTomorrow'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fall      0.263055\n",
      "Winter    0.252557\n",
      "Spring    0.242933\n",
      "Summer    0.241455\n",
      "Name: Season, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Going to engineer some new columns based on notes with the team\n",
    "# Making seasons\n",
    "def get_season(month):\n",
    "    if month in [1,2,12]: return 'Summer'\n",
    "    elif month in [3,4,5]: return 'Fall'\n",
    "    elif month in [6,7,8]: return 'Winter'\n",
    "    elif month in [9,10,11]: return 'Spring'\n",
    "\n",
    "data['Month'] = data['Date'].dt.month\n",
    "data['Season'] = data['Month'].apply(lambda x: get_season(x))\n",
    "# Change in temperature\n",
    "data['Temperature_Change'] = data['MaxTemp'] - data['MinTemp']\n",
    "# Average daily pressure\n",
    "data['Average_Pressure'] = (data['Pressure9am'] + data['Pressure3pm']) / 2\n",
    "# Average humidity\n",
    "data['Average_Pressure'] = (data['Humidity9am'] + data['Humidity3pm']) / 2\n",
    "# Average wind speed\n",
    "data['Average_Windspeed'] = (data['WindSpeed9am'] + data['WindSpeed3pm']) / 2\n",
    "print(data.Season.value_counts(normalize = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_num = data.dtypes != 'object'\n",
    "list_obj = data.dtypes == 'object'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                   True\n",
       "Location              False\n",
       "State                 False\n",
       "MinTemp                True\n",
       "MaxTemp                True\n",
       "Rainfall               True\n",
       "WindGustDir           False\n",
       "WindGustSpeed          True\n",
       "WindDir9am            False\n",
       "WindDir3pm            False\n",
       "WindSpeed9am           True\n",
       "WindSpeed3pm           True\n",
       "Humidity9am            True\n",
       "Humidity3pm            True\n",
       "Pressure9am            True\n",
       "Pressure3pm            True\n",
       "Temp9am                True\n",
       "Temp3pm                True\n",
       "RainToday             False\n",
       "RainTomorrow          False\n",
       "Month                  True\n",
       "Season                False\n",
       "Temperature_Change     True\n",
       "Average_Pressure       True\n",
       "Average_Windspeed      True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making lists of number and category columns\n",
    "list_num = data.dtypes != 'object'\n",
    "list_obj = data.dtypes == 'object'\n",
    "# indexing the data frame using the column lists\n",
    "num_col = data.columns[list_num].tolist()\n",
    "cat_col = data.columns[list_obj].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### SIMPLE IMPUTER #### To fill the NAs\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer_num_mean = SimpleImputer(strategy = 'mean')\n",
    "imputer_category = SimpleImputer(strategy = 'most_frequent')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For loop to apply imputation\n",
    "for col in data.columns.tolist():\n",
    "    if col in num_col:\n",
    "        data[col] = imputer_num_mean.fit_transform(data[[col]])\n",
    "    else:\n",
    "        data[col] = imputer_category.fit_transform(data[[col]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                  0.0 %\n",
       "Temperature_Change    0.0 %\n",
       "Season                0.0 %\n",
       "Month                 0.0 %\n",
       "RainTomorrow          0.0 %\n",
       "RainToday             0.0 %\n",
       "Temp3pm               0.0 %\n",
       "Temp9am               0.0 %\n",
       "Pressure3pm           0.0 %\n",
       "Pressure9am           0.0 %\n",
       "Humidity3pm           0.0 %\n",
       "Average_Pressure      0.0 %\n",
       "Humidity9am           0.0 %\n",
       "WindSpeed9am          0.0 %\n",
       "WindDir3pm            0.0 %\n",
       "WindDir9am            0.0 %\n",
       "WindGustSpeed         0.0 %\n",
       "WindGustDir           0.0 %\n",
       "Rainfall              0.0 %\n",
       "MaxTemp               0.0 %\n",
       "MinTemp               0.0 %\n",
       "State                 0.0 %\n",
       "Location              0.0 %\n",
       "WindSpeed3pm          0.0 %\n",
       "Average_Windspeed     0.0 %\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see if the loop worked\n",
    "empty_columns = data.apply(lambda x: f'{((x.isnull().sum()/data.shape[0])*100).round(2)} %').sort_values(ascending=True)\n",
    "empty_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2520x2520 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I'll start by plotting correlations for features\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams.update({'font.size': 22})\n",
    "plt.figure(figsize=(35,35))\n",
    "sns.heatmap(data.corr())\n",
    "plt.show() #it looks like a window lmao; instant regret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# need to change RainToday and RainTomorrow from yes no to 1 0 \n",
    "data['RainToday'] = data['RainToday'].apply(lambda x: 0 if x=='No' else 1)\n",
    "data['RainTomorrow'] = data['RainTomorrow'].apply(lambda x: 0 if x=='No' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>State</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>Month</th>\n",
       "      <th>Season</th>\n",
       "      <th>Temperature_Change</th>\n",
       "      <th>Average_Pressure</th>\n",
       "      <th>Average_Windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>9.5</td>\n",
       "      <td>46.5</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>17.7</td>\n",
       "      <td>34.5</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>12.8</td>\n",
       "      <td>34.0</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>18.8</td>\n",
       "      <td>30.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>14.8</td>\n",
       "      <td>57.5</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Location            State  MinTemp  MaxTemp  Rainfall WindGustDir  \\\n",
       "0   Albury  New_South_Wales     13.4     22.9       0.6           W   \n",
       "1   Albury  New_South_Wales      7.4     25.1       0.0         WNW   \n",
       "2   Albury  New_South_Wales     12.9     25.7       0.0         WSW   \n",
       "3   Albury  New_South_Wales      9.2     28.0       0.0          NE   \n",
       "4   Albury  New_South_Wales     17.5     32.3       1.0           W   \n",
       "\n",
       "   WindGustSpeed WindDir9am WindDir3pm  WindSpeed9am  ...  Pressure9am  \\\n",
       "0           44.0          W        WNW          20.0  ...       1007.7   \n",
       "1           44.0        NNW        WSW           4.0  ...       1010.6   \n",
       "2           46.0          W        WSW          19.0  ...       1007.6   \n",
       "3           24.0         SE          E          11.0  ...       1017.6   \n",
       "4           41.0        ENE         NW           7.0  ...       1010.8   \n",
       "\n",
       "   Pressure3pm  Temp9am  Temp3pm  RainToday  Month  Season  \\\n",
       "0       1007.1     16.9     21.8          0   12.0  Summer   \n",
       "1       1007.8     17.2     24.3          0   12.0  Summer   \n",
       "2       1008.7     21.0     23.2          0   12.0  Summer   \n",
       "3       1012.8     18.1     26.5          0   12.0  Summer   \n",
       "4       1006.0     17.8     29.7          0   12.0  Summer   \n",
       "\n",
       "   Temperature_Change  Average_Pressure Average_Windspeed  \n",
       "0                 9.5              46.5              22.0  \n",
       "1                17.7              34.5              13.0  \n",
       "2                12.8              34.0              22.5  \n",
       "3                18.8              30.5              10.0  \n",
       "4                14.8              57.5              13.5  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features = data.iloc[:,[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20,21,22,23,24]]\n",
    "target = data.loc[:,'RainTomorrow']\n",
    "Features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location_Adelaide</th>\n",
       "      <th>Location_Albany</th>\n",
       "      <th>Location_Albury</th>\n",
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       "      <th>...</th>\n",
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       "      <th>Season_Fall</th>\n",
       "      <th>Season_Spring</th>\n",
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       "      <th>Season_Winter</th>\n",
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       "<p>5 rows × 109 columns</p>\n",
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      ],
      "text/plain": [
       "   Location_Adelaide  Location_Albany  Location_Albury  Location_AliceSprings  \\\n",
       "0                  0                0                1                      0   \n",
       "1                  0                0                1                      0   \n",
       "2                  0                0                1                      0   \n",
       "3                  0                0                1                      0   \n",
       "4                  0                0                1                      0   \n",
       "\n",
       "   Location_BadgerysCreek  Location_Ballarat  Location_Bendigo  \\\n",
       "0                       0                  0                 0   \n",
       "1                       0                  0                 0   \n",
       "2                       0                  0                 0   \n",
       "3                       0                  0                 0   \n",
       "4                       0                  0                 0   \n",
       "\n",
       "   Location_Brisbane  Location_Cairns  Location_Canberra  ...  WindDir3pm_SSE  \\\n",
       "0                  0                0                  0  ...               0   \n",
       "1                  0                0                  0  ...               0   \n",
       "2                  0                0                  0  ...               0   \n",
       "3                  0                0                  0  ...               0   \n",
       "4                  0                0                  0  ...               0   \n",
       "\n",
       "   WindDir3pm_SSW  WindDir3pm_SW  WindDir3pm_W  WindDir3pm_WNW  \\\n",
       "0               0              0             0               1   \n",
       "1               0              0             0               0   \n",
       "2               0              0             0               0   \n",
       "3               0              0             0               0   \n",
       "4               0              0             0               0   \n",
       "\n",
       "   WindDir3pm_WSW  Season_Fall  Season_Spring  Season_Summer  Season_Winter  \n",
       "0               0            0              0              1              0  \n",
       "1               1            0              0              1              0  \n",
       "2               1            0              0              1              0  \n",
       "3               0            0              0              1              0  \n",
       "4               0            0              0              1              0  \n",
       "\n",
       "[5 rows x 109 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I need to dummy code the categorical variables\n",
    "# Categories\n",
    "list_category = Features.dtypes == 'object'\n",
    "categories = Features.columns[list_category].tolist()\n",
    "# Numerical\n",
    "list_numerical = Features.dtypes != 'object'\n",
    "numeric = Features.columns[list_numerical].tolist()\n",
    "numeric_features = Features[numeric]\n",
    "# Making dummy df\n",
    "dummy_features = pd.get_dummies(data[categories])\n",
    "\n",
    "dummy_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location               True\n",
       "State                  True\n",
       "MinTemp               False\n",
       "MaxTemp               False\n",
       "Rainfall              False\n",
       "WindGustDir            True\n",
       "WindGustSpeed         False\n",
       "WindDir9am             True\n",
       "WindDir3pm             True\n",
       "WindSpeed9am          False\n",
       "WindSpeed3pm          False\n",
       "Humidity9am           False\n",
       "Humidity3pm           False\n",
       "Pressure9am           False\n",
       "Pressure3pm           False\n",
       "Temp9am               False\n",
       "Temp3pm               False\n",
       "RainToday             False\n",
       "Month                 False\n",
       "Season                 True\n",
       "Temperature_Change    False\n",
       "Average_Pressure      False\n",
       "Average_Windspeed     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>57.5</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location_Adelaide  Location_Albany  Location_Albury  Location_AliceSprings  \\\n",
       "0                  0                0                1                      0   \n",
       "1                  0                0                1                      0   \n",
       "2                  0                0                1                      0   \n",
       "3                  0                0                1                      0   \n",
       "4                  0                0                1                      0   \n",
       "\n",
       "   Location_BadgerysCreek  Location_Ballarat  Location_Bendigo  \\\n",
       "0                       0                  0                 0   \n",
       "1                       0                  0                 0   \n",
       "2                       0                  0                 0   \n",
       "3                       0                  0                 0   \n",
       "4                       0                  0                 0   \n",
       "\n",
       "   Location_Brisbane  Location_Cairns  Location_Canberra  ...  Humidity3pm  \\\n",
       "0                  0                0                  0  ...         22.0   \n",
       "1                  0                0                  0  ...         25.0   \n",
       "2                  0                0                  0  ...         30.0   \n",
       "3                  0                0                  0  ...         16.0   \n",
       "4                  0                0                  0  ...         33.0   \n",
       "\n",
       "   Pressure9am  Pressure3pm  Temp9am  Temp3pm  RainToday  Month  \\\n",
       "0       1007.7       1007.1     16.9     21.8          0   12.0   \n",
       "1       1010.6       1007.8     17.2     24.3          0   12.0   \n",
       "2       1007.6       1008.7     21.0     23.2          0   12.0   \n",
       "3       1017.6       1012.8     18.1     26.5          0   12.0   \n",
       "4       1010.8       1006.0     17.8     29.7          0   12.0   \n",
       "\n",
       "   Temperature_Change  Average_Pressure  Average_Windspeed  \n",
       "0                 9.5              46.5               22.0  \n",
       "1                17.7              34.5               13.0  \n",
       "2                12.8              34.0               22.5  \n",
       "3                18.8              30.5               10.0  \n",
       "4                14.8              57.5               13.5  \n",
       "\n",
       "[5 rows x 126 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combining dummies with numerical features\n",
    "\n",
    "dfs = [dummy_features, numeric_features]\n",
    "Features = pd.concat(dfs, axis = 1)\n",
    "Features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ready to split data and setup randomforest model \n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Split into train and test data\n",
    "X = Features\n",
    "y = target\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=426)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    1.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    3.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=15, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=-1, oob_score=False, random_state=426,\n",
       "                       verbose=True, warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making model\n",
    "model_1 = RandomForestClassifier(n_jobs = -1, criterion = 'gini' , verbose = True, n_estimators = 100 , random_state = 426, max_depth = 15)\n",
    "model_1.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "# Saving some validation predicitons and probabilites\n",
    "val_preds = model_1.predict(val_X)\n",
    "# predict probabilities\n",
    "probs = model_1.predict_proba(val_X)\n",
    "# keep probabilities for the positive outcome only\n",
    "probs_1 = probs[:, 1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accuracy: 0.8502956139144782\n",
      "F1 Score: 0.5685528609922333\n",
      "Average Precision: 0.6913547905628683\n"
     ]
    }
   ],
   "source": [
    "# Scoring model: Accuracy, F1 Score, Average Precision\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "print(\"Mean Accuracy:\" , accuracy_score(val_y,val_preds))\n",
    "print(\"F1 Score:\",f1_score(val_y,val_preds))\n",
    "print(\"Average Precision:\", average_precision_score(val_y, probs_1)) # second input must be y probability estimates of the positive class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest: ROC AUC=0.864\n"
     ]
    }
   ],
   "source": [
    "# Going to plot the ROC \n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# AUC\n",
    "auc = roc_auc_score(val_y, probs_1)\n",
    "print('RandomForest: ROC AUC=%.3f' % (auc))\n",
    "\n",
    "# calculating roc curve\n",
    "fpr, tpr, thresholds = roc_curve(val_y, probs_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plotting the curve\n",
    "plt.plot(fpr, tpr, marker='.', label='RandomForest')\n",
    "# axis labels\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "# show the legend\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Precision-Recall curve of Model 1: AUC=0.691')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Precision recall curve\n",
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "# Calculating values\n",
    "precision, recall, thresholds = precision_recall_curve(val_y, probs_1)\n",
    "test_average_precision = average_precision_score(val_y, probs_1)\n",
    "\n",
    "plt.rcParams.update({'font.size': 10})\n",
    "plt.step(recall, precision, color='b', alpha=0.2, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "str_average_precision = \"{0:.3f}\".format(test_average_precision)\n",
    "plt.title('Precision-Recall curve of {}: AUC={}'.format(\"Model 1\", str_average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[27334,  1153],\n",
       "       [ 4291,  3587]], dtype=int64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MAKE CONFUSION MATRIX \n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(val_y,val_preds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>State</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>Month</th>\n",
       "      <th>Season</th>\n",
       "      <th>Temperature_Change</th>\n",
       "      <th>Average_Pressure</th>\n",
       "      <th>Average_Windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>W</td>\n",
       "      <td>44.00000</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>46.5</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.00000</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>17.700000</td>\n",
       "      <td>34.5</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.00000</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>34.0</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>30.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albury</td>\n",
       "      <td>New_South_Wales</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>57.5</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145455</th>\n",
       "      <td>Uluru</td>\n",
       "      <td>Northern_Territory</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>E</td>\n",
       "      <td>31.00000</td>\n",
       "      <td>SE</td>\n",
       "      <td>ENE</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1024.6</td>\n",
       "      <td>1020.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Winter</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>37.5</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145456</th>\n",
       "      <td>Uluru</td>\n",
       "      <td>Northern_Territory</td>\n",
       "      <td>3.6</td>\n",
       "      <td>25.300000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>SE</td>\n",
       "      <td>N</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>1019.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Winter</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>38.5</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145457</th>\n",
       "      <td>Uluru</td>\n",
       "      <td>Northern_Territory</td>\n",
       "      <td>5.4</td>\n",
       "      <td>26.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>37.00000</td>\n",
       "      <td>SE</td>\n",
       "      <td>WNW</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>12.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Winter</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>38.5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145458</th>\n",
       "      <td>Uluru</td>\n",
       "      <td>Northern_Territory</td>\n",
       "      <td>7.8</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>SSE</td>\n",
       "      <td>N</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Winter</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>37.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145459</th>\n",
       "      <td>Uluru</td>\n",
       "      <td>Northern_Territory</td>\n",
       "      <td>14.9</td>\n",
       "      <td>23.221348</td>\n",
       "      <td>0.0</td>\n",
       "      <td>W</td>\n",
       "      <td>40.03523</td>\n",
       "      <td>ESE</td>\n",
       "      <td>ESE</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Winter</td>\n",
       "      <td>11.029981</td>\n",
       "      <td>49.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145460 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location               State  MinTemp    MaxTemp  Rainfall WindGustDir  \\\n",
       "0        Albury     New_South_Wales     13.4  22.900000       0.6           W   \n",
       "1        Albury     New_South_Wales      7.4  25.100000       0.0         WNW   \n",
       "2        Albury     New_South_Wales     12.9  25.700000       0.0         WSW   \n",
       "3        Albury     New_South_Wales      9.2  28.000000       0.0          NE   \n",
       "4        Albury     New_South_Wales     17.5  32.300000       1.0           W   \n",
       "...         ...                 ...      ...        ...       ...         ...   \n",
       "145455    Uluru  Northern_Territory      2.8  23.400000       0.0           E   \n",
       "145456    Uluru  Northern_Territory      3.6  25.300000       0.0         NNW   \n",
       "145457    Uluru  Northern_Territory      5.4  26.900000       0.0           N   \n",
       "145458    Uluru  Northern_Territory      7.8  27.000000       0.0          SE   \n",
       "145459    Uluru  Northern_Territory     14.9  23.221348       0.0           W   \n",
       "\n",
       "        WindGustSpeed WindDir9am WindDir3pm  WindSpeed9am  ...  Pressure9am  \\\n",
       "0            44.00000          W        WNW          20.0  ...       1007.7   \n",
       "1            44.00000        NNW        WSW           4.0  ...       1010.6   \n",
       "2            46.00000          W        WSW          19.0  ...       1007.6   \n",
       "3            24.00000         SE          E          11.0  ...       1017.6   \n",
       "4            41.00000        ENE         NW           7.0  ...       1010.8   \n",
       "...               ...        ...        ...           ...  ...          ...   \n",
       "145455       31.00000         SE        ENE          13.0  ...       1024.6   \n",
       "145456       22.00000         SE          N          13.0  ...       1023.5   \n",
       "145457       37.00000         SE        WNW           9.0  ...       1021.0   \n",
       "145458       28.00000        SSE          N          13.0  ...       1019.4   \n",
       "145459       40.03523        ESE        ESE          17.0  ...       1020.2   \n",
       "\n",
       "        Pressure3pm  Temp9am  Temp3pm  RainToday  Month  Season  \\\n",
       "0            1007.1     16.9     21.8          0   12.0  Summer   \n",
       "1            1007.8     17.2     24.3          0   12.0  Summer   \n",
       "2            1008.7     21.0     23.2          0   12.0  Summer   \n",
       "3            1012.8     18.1     26.5          0   12.0  Summer   \n",
       "4            1006.0     17.8     29.7          0   12.0  Summer   \n",
       "...             ...      ...      ...        ...    ...     ...   \n",
       "145455       1020.3     10.1     22.4          0    6.0  Winter   \n",
       "145456       1019.1     10.9     24.5          0    6.0  Winter   \n",
       "145457       1016.8     12.5     26.1          0    6.0  Winter   \n",
       "145458       1016.5     15.1     26.0          0    6.0  Winter   \n",
       "145459       1017.9     15.0     20.9          0    6.0  Winter   \n",
       "\n",
       "        Temperature_Change  Average_Pressure Average_Windspeed  \n",
       "0                 9.500000              46.5              22.0  \n",
       "1                17.700000              34.5              13.0  \n",
       "2                12.800000              34.0              22.5  \n",
       "3                18.800000              30.5              10.0  \n",
       "4                14.800000              57.5              13.5  \n",
       "...                    ...               ...               ...  \n",
       "145455           20.600000              37.5              12.0  \n",
       "145456           21.700000              38.5              11.0  \n",
       "145457           21.500000              38.5               9.0  \n",
       "145458           19.200000              37.5              10.0  \n",
       "145459           11.029981              49.0              17.0  \n",
       "\n",
       "[145460 rows x 23 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:,[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20,21,22,23,24]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# maybe plot rain over time\n",
    "# maybe plot this in a specific state\n",
    "# segment for specific model for specific state\n",
    "# cluster analysis\n",
    "# Identify a proper threshold\n",
    "# find optimal point on ROC "
   ]
  }
 ],
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