Energy_predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle - Energy dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing, model_selection, metrics\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>...</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>rv1</th>\n",
       "      <th>rv2</th>\n",
       "      <th>Day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>38.163333</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>40.326667</td>\n",
       "      <td>20.230</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>19.926667</td>\n",
       "      <td>34.526667</td>\n",
       "      <td>...</td>\n",
       "      <td>36.533333</td>\n",
       "      <td>4.63</td>\n",
       "      <td>755.033333</td>\n",
       "      <td>75.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>6.455464</td>\n",
       "      <td>6.455464</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>40.450000</td>\n",
       "      <td>18.390000</td>\n",
       "      <td>44.090000</td>\n",
       "      <td>22.000</td>\n",
       "      <td>38.290000</td>\n",
       "      <td>19.856667</td>\n",
       "      <td>38.863333</td>\n",
       "      <td>...</td>\n",
       "      <td>43.163333</td>\n",
       "      <td>6.42</td>\n",
       "      <td>753.433333</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>50.833333</td>\n",
       "      <td>5.92</td>\n",
       "      <td>6.797277</td>\n",
       "      <td>6.797277</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>24.69</td>\n",
       "      <td>49.476000</td>\n",
       "      <td>24.329730</td>\n",
       "      <td>47.159009</td>\n",
       "      <td>26.834</td>\n",
       "      <td>43.840000</td>\n",
       "      <td>23.997297</td>\n",
       "      <td>46.901351</td>\n",
       "      <td>...</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>17.50</td>\n",
       "      <td>751.533333</td>\n",
       "      <td>82.333333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>14.40</td>\n",
       "      <td>8.524160</td>\n",
       "      <td>8.524160</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>23.79</td>\n",
       "      <td>37.730000</td>\n",
       "      <td>21.633333</td>\n",
       "      <td>39.066667</td>\n",
       "      <td>24.500</td>\n",
       "      <td>38.126667</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>36.663333</td>\n",
       "      <td>...</td>\n",
       "      <td>43.290000</td>\n",
       "      <td>12.00</td>\n",
       "      <td>752.033333</td>\n",
       "      <td>64.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>5.47</td>\n",
       "      <td>25.197838</td>\n",
       "      <td>25.197838</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>23.79</td>\n",
       "      <td>39.326667</td>\n",
       "      <td>28.178000</td>\n",
       "      <td>31.376000</td>\n",
       "      <td>25.100</td>\n",
       "      <td>37.290000</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>42.760000</td>\n",
       "      <td>18.90</td>\n",
       "      <td>752.466667</td>\n",
       "      <td>52.166667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>8.85</td>\n",
       "      <td>23.039471</td>\n",
       "      <td>23.039471</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Appliances  lights     T1       RH_1         T2       RH_2      T3  \\\n",
       "0          40       0  21.00  38.163333  18.500000  40.326667  20.230   \n",
       "1          40       0  21.00  40.450000  18.390000  44.090000  22.000   \n",
       "2          60       0  24.69  49.476000  24.329730  47.159009  26.834   \n",
       "3          40       0  23.79  37.730000  21.633333  39.066667  24.500   \n",
       "4         100       0  23.79  39.326667  28.178000  31.376000  25.100   \n",
       "\n",
       "        RH_3         T4       RH_4  ...       RH_9  T_out  Press_mm_hg  \\\n",
       "0  38.000000  19.926667  34.526667  ...  36.533333   4.63   755.033333   \n",
       "1  38.290000  19.856667  38.863333  ...  43.163333   6.42   753.433333   \n",
       "2  43.840000  23.997297  46.901351  ...  48.400000  17.50   751.533333   \n",
       "3  38.126667  23.600000  36.663333  ...  43.290000  12.00   752.033333   \n",
       "4  37.290000  23.600000  37.500000  ...  42.760000  18.90   752.466667   \n",
       "\n",
       "      RH_out  Windspeed  Visibility  Tdewpoint        rv1        rv2  \\\n",
       "0  75.333333   4.666667   40.000000       0.60   6.455464   6.455464   \n",
       "1  97.000000   6.000000   50.833333       5.92   6.797277   6.797277   \n",
       "2  82.333333   2.000000   40.000000      14.40   8.524160   8.524160   \n",
       "3  64.666667   1.000000   40.000000       5.47  25.197838  25.197838   \n",
       "4  52.166667   3.000000   40.000000       8.85  23.039471  23.039471   \n",
       "\n",
       "   Day_of_week  \n",
       "0    Wednesday  \n",
       "1       Friday  \n",
       "2       Sunday  \n",
       "3      Tuesday  \n",
       "4      Tuesday  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>...</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>rv1</th>\n",
       "      <th>rv2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "      <td>14801.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>98.002838</td>\n",
       "      <td>3.828120</td>\n",
       "      <td>21.684882</td>\n",
       "      <td>40.258743</td>\n",
       "      <td>20.340154</td>\n",
       "      <td>40.422551</td>\n",
       "      <td>22.262874</td>\n",
       "      <td>39.243878</td>\n",
       "      <td>20.847201</td>\n",
       "      <td>39.040304</td>\n",
       "      <td>...</td>\n",
       "      <td>19.481392</td>\n",
       "      <td>41.564688</td>\n",
       "      <td>7.410142</td>\n",
       "      <td>755.535791</td>\n",
       "      <td>79.815677</td>\n",
       "      <td>4.031558</td>\n",
       "      <td>38.336201</td>\n",
       "      <td>3.770643</td>\n",
       "      <td>25.026830</td>\n",
       "      <td>25.026830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>103.457709</td>\n",
       "      <td>7.983508</td>\n",
       "      <td>1.607712</td>\n",
       "      <td>3.964974</td>\n",
       "      <td>2.194775</td>\n",
       "      <td>4.069910</td>\n",
       "      <td>2.003559</td>\n",
       "      <td>3.248654</td>\n",
       "      <td>2.045689</td>\n",
       "      <td>4.332139</td>\n",
       "      <td>...</td>\n",
       "      <td>2.017567</td>\n",
       "      <td>4.157938</td>\n",
       "      <td>5.327065</td>\n",
       "      <td>7.380735</td>\n",
       "      <td>14.908459</td>\n",
       "      <td>2.438925</td>\n",
       "      <td>11.825196</td>\n",
       "      <td>4.200420</td>\n",
       "      <td>14.496293</td>\n",
       "      <td>14.496293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.790000</td>\n",
       "      <td>27.023333</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>20.596667</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>28.766667</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>28.424286</td>\n",
       "      <td>...</td>\n",
       "      <td>14.890000</td>\n",
       "      <td>29.166667</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>729.300000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-6.600000</td>\n",
       "      <td>0.006033</td>\n",
       "      <td>0.006033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.760000</td>\n",
       "      <td>37.363333</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>37.900000</td>\n",
       "      <td>20.790000</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>35.566667</td>\n",
       "      <td>...</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>38.518000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>750.933333</td>\n",
       "      <td>70.333333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>12.511717</td>\n",
       "      <td>12.511717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>39.633333</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>38.560000</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>38.430000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.390000</td>\n",
       "      <td>40.900000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>756.100000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.430000</td>\n",
       "      <td>24.930300</td>\n",
       "      <td>24.930300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>43.060000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>43.290000</td>\n",
       "      <td>23.290000</td>\n",
       "      <td>41.760000</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>42.156667</td>\n",
       "      <td>...</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>44.363333</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>760.966667</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.580000</td>\n",
       "      <td>37.660334</td>\n",
       "      <td>37.660334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1080.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>26.260000</td>\n",
       "      <td>57.423333</td>\n",
       "      <td>29.856667</td>\n",
       "      <td>54.766667</td>\n",
       "      <td>29.236000</td>\n",
       "      <td>50.163333</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>51.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>53.326667</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>772.283333</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>15.400000</td>\n",
       "      <td>49.996530</td>\n",
       "      <td>49.996530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Appliances        lights            T1          RH_1            T2  \\\n",
       "count  14801.000000  14801.000000  14801.000000  14801.000000  14801.000000   \n",
       "mean      98.002838      3.828120     21.684882     40.258743     20.340154   \n",
       "std      103.457709      7.983508      1.607712      3.964974      2.194775   \n",
       "min       10.000000      0.000000     16.790000     27.023333     16.100000   \n",
       "25%       50.000000      0.000000     20.760000     37.363333     18.790000   \n",
       "50%       60.000000      0.000000     21.600000     39.633333     20.000000   \n",
       "75%      100.000000      0.000000     22.600000     43.060000     21.500000   \n",
       "max     1080.000000     70.000000     26.260000     57.423333     29.856667   \n",
       "\n",
       "               RH_2            T3          RH_3            T4          RH_4  \\\n",
       "count  14801.000000  14801.000000  14801.000000  14801.000000  14801.000000   \n",
       "mean      40.422551     22.262874     39.243878     20.847201     39.040304   \n",
       "std        4.069910      2.003559      3.248654      2.045689      4.332139   \n",
       "min       20.596667     17.200000     28.766667     15.100000     28.424286   \n",
       "25%       37.900000     20.790000     36.900000     19.500000     35.566667   \n",
       "50%       40.500000     22.100000     38.560000     20.600000     38.430000   \n",
       "75%       43.290000     23.290000     41.760000     22.100000     42.156667   \n",
       "max       54.766667     29.236000     50.163333     26.200000     51.090000   \n",
       "\n",
       "       ...            T9          RH_9         T_out   Press_mm_hg  \\\n",
       "count  ...  14801.000000  14801.000000  14801.000000  14801.000000   \n",
       "mean   ...     19.481392     41.564688      7.410142    755.535791   \n",
       "std    ...      2.017567      4.157938      5.327065      7.380735   \n",
       "min    ...     14.890000     29.166667     -5.000000    729.300000   \n",
       "25%    ...     18.000000     38.518000      3.600000    750.933333   \n",
       "50%    ...     19.390000     40.900000      6.900000    756.100000   \n",
       "75%    ...     20.600000     44.363333     10.400000    760.966667   \n",
       "max    ...     24.500000     53.326667     26.100000    772.283333   \n",
       "\n",
       "             RH_out     Windspeed    Visibility     Tdewpoint           rv1  \\\n",
       "count  14801.000000  14801.000000  14801.000000  14801.000000  14801.000000   \n",
       "mean      79.815677      4.031558     38.336201      3.770643     25.026830   \n",
       "std       14.908459      2.438925     11.825196      4.200420     14.496293   \n",
       "min       24.000000      0.000000      1.000000     -6.600000      0.006033   \n",
       "25%       70.333333      2.000000     29.000000      0.900000     12.511717   \n",
       "50%       84.000000      3.666667     40.000000      3.430000     24.930300   \n",
       "75%       91.666667      5.333333     40.000000      6.580000     37.660334   \n",
       "max      100.000000     14.000000     66.000000     15.400000     49.996530   \n",
       "\n",
       "                rv2  \n",
       "count  14801.000000  \n",
       "mean      25.026830  \n",
       "std       14.496293  \n",
       "min        0.006033  \n",
       "25%       12.511717  \n",
       "50%       24.930300  \n",
       "75%       37.660334  \n",
       "max       49.996530  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14801 entries, 0 to 14800\n",
      "Data columns (total 29 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Appliances   14801 non-null  int64  \n",
      " 1   lights       14801 non-null  int64  \n",
      " 2   T1           14801 non-null  float64\n",
      " 3   RH_1         14801 non-null  float64\n",
      " 4   T2           14801 non-null  float64\n",
      " 5   RH_2         14801 non-null  float64\n",
      " 6   T3           14801 non-null  float64\n",
      " 7   RH_3         14801 non-null  float64\n",
      " 8   T4           14801 non-null  float64\n",
      " 9   RH_4         14801 non-null  float64\n",
      " 10  T5           14801 non-null  float64\n",
      " 11  RH_5         14801 non-null  float64\n",
      " 12  T6           14801 non-null  float64\n",
      " 13  RH_6         14801 non-null  float64\n",
      " 14  T7           14801 non-null  float64\n",
      " 15  RH_7         14801 non-null  float64\n",
      " 16  T8           14801 non-null  float64\n",
      " 17  RH_8         14801 non-null  float64\n",
      " 18  T9           14801 non-null  float64\n",
      " 19  RH_9         14801 non-null  float64\n",
      " 20  T_out        14801 non-null  float64\n",
      " 21  Press_mm_hg  14801 non-null  float64\n",
      " 22  RH_out       14801 non-null  float64\n",
      " 23  Windspeed    14801 non-null  float64\n",
      " 24  Visibility   14801 non-null  float64\n",
      " 25  Tdewpoint    14801 non-null  float64\n",
      " 26  rv1          14801 non-null  float64\n",
      " 27  rv2          14801 non-null  float64\n",
      " 28  Day_of_week  14801 non-null  object \n",
      "dtypes: float64(26), int64(2), object(1)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Appliances     0\n",
       "rv1            0\n",
       "Tdewpoint      0\n",
       "Visibility     0\n",
       "Windspeed      0\n",
       "RH_out         0\n",
       "Press_mm_hg    0\n",
       "T_out          0\n",
       "RH_9           0\n",
       "T9             0\n",
       "RH_8           0\n",
       "T8             0\n",
       "RH_7           0\n",
       "rv2            0\n",
       "T7             0\n",
       "T6             0\n",
       "RH_5           0\n",
       "T5             0\n",
       "RH_4           0\n",
       "T4             0\n",
       "RH_3           0\n",
       "T3             0\n",
       "RH_2           0\n",
       "T2             0\n",
       "RH_1           0\n",
       "T1             0\n",
       "lights         0\n",
       "RH_6           0\n",
       "Day_of_week    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of null values in all columns\n",
    "data.isnull().sum().sort_values(ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conversion des données catégorielles en chiffres "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "data[\"Day_of_week\"] = encoder.fit_transform(data[\"Day_of_week\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop de la variable lights inutile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop([\"lights\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1600x1400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the weather , temperature , applainces and random column to see the correlation\n",
    "train_corr = data.drop(['Day_of_week'], axis=1)\n",
    "corr = train_corr.corr()\n",
    "# Mask the repeated values\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "  \n",
    "f, ax = plt.subplots(figsize=(16, 14))\n",
    "#Generate Heat Map, allow annotations and place floats in map\n",
    "sns.heatmap(corr, annot=True, fmt=\".2f\" , mask=mask,)\n",
    "    #Apply xticks\n",
    "plt.xticks(range(len(corr.columns)), corr.columns);\n",
    "    #Apply yticks\n",
    "plt.yticks(range(len(corr.columns)), corr.columns)\n",
    "    #show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Absolute Correlations\n",
      "rv1    rv2          1.000000\n",
      "T6     T_out        0.974985\n",
      "T7     T9           0.944683\n",
      "T5     T9           0.910924\n",
      "T3     T9           0.902327\n",
      "RH_3   RH_4         0.899665\n",
      "RH_4   RH_7         0.894385\n",
      "T1     T3           0.892386\n",
      "T4     T9           0.890157\n",
      "T3     T5           0.888455\n",
      "T1     T5           0.885910\n",
      "RH_7   RH_8         0.883725\n",
      "T7     T8           0.882703\n",
      "RH_1   RH_4         0.880386\n",
      "T4     T7           0.878383\n",
      "T1     T4           0.877274\n",
      "T4     T5           0.872505\n",
      "T5     T7           0.870636\n",
      "T8     T9           0.869874\n",
      "RH_7   RH_9         0.858151\n",
      "RH_8   RH_9         0.856419\n",
      "RH_4   RH_9         0.856063\n",
      "T3     T4           0.853219\n",
      "       T7           0.848788\n",
      "RH_4   RH_8         0.848108\n",
      "RH_1   RH_3         0.844879\n",
      "T1     T9           0.844449\n",
      "       T7           0.839018\n",
      "       T2           0.836712\n",
      "RH_3   RH_9         0.834587\n",
      "       RH_7         0.834040\n",
      "       RH_8         0.831325\n",
      "T1     T8           0.827216\n",
      "T5     T8           0.825838\n",
      "T2     T6           0.801387\n",
      "RH_1   RH_7         0.801086\n",
      "T3     T8           0.797787\n",
      "RH_1   RH_2         0.797520\n",
      "T4     T8           0.797106\n",
      "T_out  Tdewpoint    0.791984\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def get_redundant_pairs(df):\n",
    "    '''Get diagonal and lower triangular pairs of correlation matrix'''\n",
    "    pairs_to_drop = set()\n",
    "    cols = df.columns\n",
    "    for i in range(0, df.shape[1]):\n",
    "        for j in range(0, i+1):\n",
    "            pairs_to_drop.add((cols[i], cols[j]))\n",
    "    return pairs_to_drop\n",
    "\n",
    "# Function to get top correlations \n",
    "\n",
    "def get_top_abs_correlations(df, n=5):\n",
    "    au_corr = df.corr().abs().unstack()\n",
    "    labels_to_drop = get_redundant_pairs(df)\n",
    "    au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False)\n",
    "    return au_corr[0:n]\n",
    "\n",
    "print(\"Top Absolute Correlations\")\n",
    "print(get_top_abs_correlations(train_corr, 40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Appliances', 'T1', 'RH_1', 'T2', 'RH_2', 'T3', 'RH_3', 'T4', 'RH_4',\n",
       "       'T5', 'RH_5', 'T6', 'RH_6', 'T7', 'RH_7', 'T8', 'RH_8', 'T9', 'RH_9',\n",
       "       'T_out', 'Press_mm_hg', 'RH_out', 'Windspeed', 'Visibility',\n",
       "       'Tdewpoint', 'rv1', 'rv2', 'Day_of_week'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Séparation en dataset de train et test et isolation de la target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 75% of the data is usedfor the training of the models and the rest is used for testing\n",
    "train_data, test_data = train_test_split(data,test_size=0.25,random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Appliances</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>T5</th>\n",
       "      <th>...</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>rv1</th>\n",
       "      <th>rv2</th>\n",
       "      <th>Day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>50</td>\n",
       "      <td>22.890000</td>\n",
       "      <td>41.590000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>45.126667</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>38.700000</td>\n",
       "      <td>21.290000</td>\n",
       "      <td>41.700000</td>\n",
       "      <td>21.175000</td>\n",
       "      <td>...</td>\n",
       "      <td>40.333333</td>\n",
       "      <td>8.73</td>\n",
       "      <td>753.866667</td>\n",
       "      <td>80.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.63</td>\n",
       "      <td>24.572602</td>\n",
       "      <td>24.572602</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13690</th>\n",
       "      <td>100</td>\n",
       "      <td>21.390000</td>\n",
       "      <td>40.730000</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>22.390000</td>\n",
       "      <td>37.966667</td>\n",
       "      <td>20.263333</td>\n",
       "      <td>40.626667</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>...</td>\n",
       "      <td>42.090000</td>\n",
       "      <td>8.90</td>\n",
       "      <td>739.100000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>9.382576</td>\n",
       "      <td>9.382576</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>60</td>\n",
       "      <td>23.892208</td>\n",
       "      <td>40.437403</td>\n",
       "      <td>22.818701</td>\n",
       "      <td>40.410390</td>\n",
       "      <td>24.948052</td>\n",
       "      <td>38.475974</td>\n",
       "      <td>23.682208</td>\n",
       "      <td>39.453247</td>\n",
       "      <td>21.322468</td>\n",
       "      <td>...</td>\n",
       "      <td>42.097186</td>\n",
       "      <td>8.87</td>\n",
       "      <td>757.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>8.17</td>\n",
       "      <td>7.969307</td>\n",
       "      <td>7.969307</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14187</th>\n",
       "      <td>30</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>45.560000</td>\n",
       "      <td>21.890000</td>\n",
       "      <td>43.200000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>20.323333</td>\n",
       "      <td>47.900000</td>\n",
       "      <td>18.710000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.863333</td>\n",
       "      <td>9.00</td>\n",
       "      <td>758.100000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>23.933729</td>\n",
       "      <td>23.933729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8466</th>\n",
       "      <td>70</td>\n",
       "      <td>21.790000</td>\n",
       "      <td>42.730000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>46.466667</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>39.790000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>40.163333</td>\n",
       "      <td>20.852500</td>\n",
       "      <td>...</td>\n",
       "      <td>44.900000</td>\n",
       "      <td>6.50</td>\n",
       "      <td>751.600000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>11.189903</td>\n",
       "      <td>11.189903</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Appliances         T1       RH_1         T2       RH_2         T3  \\\n",
       "1189           50  22.890000  41.590000  20.200000  45.126667  24.600000   \n",
       "13690         100  21.390000  40.730000  18.790000  45.000000  22.390000   \n",
       "2983           60  23.892208  40.437403  22.818701  40.410390  24.948052   \n",
       "14187          30  22.500000  45.560000  21.890000  43.200000  22.500000   \n",
       "8466           70  21.790000  42.730000  19.100000  46.466667  23.100000   \n",
       "\n",
       "            RH_3         T4       RH_4         T5  ...       RH_9  T_out  \\\n",
       "1189   38.700000  21.290000  41.700000  21.175000  ...  40.333333   8.73   \n",
       "13690  37.966667  20.263333  40.626667  19.790000  ...  42.090000   8.90   \n",
       "2983   38.475974  23.682208  39.453247  21.322468  ...  42.097186   8.87   \n",
       "14187  45.290000  20.323333  47.900000  18.710000  ...  48.863333   9.00   \n",
       "8466   39.790000  21.700000  40.163333  20.852500  ...  44.900000   6.50   \n",
       "\n",
       "       Press_mm_hg     RH_out  Windspeed  Visibility  Tdewpoint        rv1  \\\n",
       "1189    753.866667  80.666667        3.0        40.0       5.63  24.572602   \n",
       "13690   739.100000  83.000000       12.0        40.0       6.20   9.382576   \n",
       "2983    757.000000  95.000000        1.0        48.0       8.17   7.969307   \n",
       "14187   758.100000  87.000000        6.0        40.0       7.00  23.933729   \n",
       "8466    751.600000  93.000000        1.0        40.0       5.40  11.189903   \n",
       "\n",
       "             rv2  Day_of_week  \n",
       "1189   24.572602            6  \n",
       "13690   9.382576            1  \n",
       "2983    7.969307            4  \n",
       "14187  23.933729            1  \n",
       "8466   11.189903            3  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Appliances'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(df):\n",
    "    X = df.drop([target], axis=1)\n",
    "    y = df[target]\n",
    "    \n",
    "    return X,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = preprocessing(train_data)\n",
    "X_test, y_test = preprocessing(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>T5</th>\n",
       "      <th>RH_5</th>\n",
       "      <th>...</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>rv1</th>\n",
       "      <th>rv2</th>\n",
       "      <th>Day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>22.890000</td>\n",
       "      <td>41.590000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>45.126667</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>38.700000</td>\n",
       "      <td>21.290000</td>\n",
       "      <td>41.700000</td>\n",
       "      <td>21.175000</td>\n",
       "      <td>53.647500</td>\n",
       "      <td>...</td>\n",
       "      <td>40.333333</td>\n",
       "      <td>8.73</td>\n",
       "      <td>753.866667</td>\n",
       "      <td>80.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.63</td>\n",
       "      <td>24.572602</td>\n",
       "      <td>24.572602</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>13690</th>\n",
       "      <td>21.390000</td>\n",
       "      <td>40.730000</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>22.390000</td>\n",
       "      <td>37.966667</td>\n",
       "      <td>20.263333</td>\n",
       "      <td>40.626667</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>42.090000</td>\n",
       "      <td>8.90</td>\n",
       "      <td>739.100000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>9.382576</td>\n",
       "      <td>9.382576</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>23.892208</td>\n",
       "      <td>40.437403</td>\n",
       "      <td>22.818701</td>\n",
       "      <td>40.410390</td>\n",
       "      <td>24.948052</td>\n",
       "      <td>38.475974</td>\n",
       "      <td>23.682208</td>\n",
       "      <td>39.453247</td>\n",
       "      <td>21.322468</td>\n",
       "      <td>50.518019</td>\n",
       "      <td>...</td>\n",
       "      <td>42.097186</td>\n",
       "      <td>8.87</td>\n",
       "      <td>757.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>8.17</td>\n",
       "      <td>7.969307</td>\n",
       "      <td>7.969307</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14187</th>\n",
       "      <td>22.500000</td>\n",
       "      <td>45.560000</td>\n",
       "      <td>21.890000</td>\n",
       "      <td>43.200000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>20.323333</td>\n",
       "      <td>47.900000</td>\n",
       "      <td>18.710000</td>\n",
       "      <td>55.950000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.863333</td>\n",
       "      <td>9.00</td>\n",
       "      <td>758.100000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>23.933729</td>\n",
       "      <td>23.933729</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8466</th>\n",
       "      <td>21.790000</td>\n",
       "      <td>42.730000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>46.466667</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>39.790000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>40.163333</td>\n",
       "      <td>20.852500</td>\n",
       "      <td>45.296250</td>\n",
       "      <td>...</td>\n",
       "      <td>44.900000</td>\n",
       "      <td>6.50</td>\n",
       "      <td>751.600000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>11.189903</td>\n",
       "      <td>11.189903</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              T1       RH_1         T2       RH_2         T3       RH_3  \\\n",
       "1189   22.890000  41.590000  20.200000  45.126667  24.600000  38.700000   \n",
       "13690  21.390000  40.730000  18.790000  45.000000  22.390000  37.966667   \n",
       "2983   23.892208  40.437403  22.818701  40.410390  24.948052  38.475974   \n",
       "14187  22.500000  45.560000  21.890000  43.200000  22.500000  45.290000   \n",
       "8466   21.790000  42.730000  19.100000  46.466667  23.100000  39.790000   \n",
       "\n",
       "              T4       RH_4         T5       RH_5  ...       RH_9  T_out  \\\n",
       "1189   21.290000  41.700000  21.175000  53.647500  ...  40.333333   8.73   \n",
       "13690  20.263333  40.626667  19.790000  47.500000  ...  42.090000   8.90   \n",
       "2983   23.682208  39.453247  21.322468  50.518019  ...  42.097186   8.87   \n",
       "14187  20.323333  47.900000  18.710000  55.950000  ...  48.863333   9.00   \n",
       "8466   21.700000  40.163333  20.852500  45.296250  ...  44.900000   6.50   \n",
       "\n",
       "       Press_mm_hg     RH_out  Windspeed  Visibility  Tdewpoint        rv1  \\\n",
       "1189    753.866667  80.666667        3.0        40.0       5.63  24.572602   \n",
       "13690   739.100000  83.000000       12.0        40.0       6.20   9.382576   \n",
       "2983    757.000000  95.000000        1.0        48.0       8.17   7.969307   \n",
       "14187   758.100000  87.000000        6.0        40.0       7.00  23.933729   \n",
       "8466    751.600000  93.000000        1.0        40.0       5.40  11.189903   \n",
       "\n",
       "             rv2  Day_of_week  \n",
       "1189   24.572602            6  \n",
       "13690   9.382576            1  \n",
       "2983    7.969307            4  \n",
       "14187  23.933729            1  \n",
       "8466   11.189903            3  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalisation avec StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 21.68051409  40.23132338  20.32786676  40.41342199  22.2584877\n",
      "  39.22852988  20.84589297  39.0219034   19.57957845  50.91695304\n",
      "   7.87550536  54.7656252   20.2542631   35.38843295  22.02450074\n",
      "  42.95927488  19.47636611  41.56151233   7.39408081 755.62269141\n",
      "  79.8680527    4.03323931  38.26788038   3.76918491  25.05073413\n",
      "  25.05073413   2.95468468]\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler().fit(X_train)\n",
    "print(scaler.mean_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "import xgboost as xgb\n",
    "from sklearn import neighbors\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparaison of the different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [\n",
    "           ['Lasso: ', Lasso()],\n",
    "           ['Ridge: ', Ridge()],\n",
    "           ['KNeighborsRegressor: ',  neighbors.KNeighborsRegressor()],\n",
    "           ['SVR:' , SVR(kernel='rbf')],\n",
    "           ['RandomForest ',RandomForestRegressor()],\n",
    "           ['ExtraTreeRegressor :',ExtraTreesRegressor()],\n",
    "           ['GradientBoostingRegressor: ', GradientBoostingRegressor()] ,\n",
    "           ['XGBRegressor: ', xgb.XGBRegressor()] ,\n",
    "           ['MLPRegressor: ', MLPRegressor(  activation='relu', solver='adam',learning_rate='adaptive',max_iter=1000,learning_rate_init=0.01,alpha=0.01)]\n",
    "         ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run all the proposed models and update the information in a list model_data\n",
    "import time\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "model_data = []\n",
    "for name,curr_model in models :\n",
    "    curr_model_data = {}\n",
    "    curr_model.random_state = 78\n",
    "    curr_model_data[\"Name\"] = name\n",
    "    start = time.time()\n",
    "    curr_model.fit(X_train,y_train)\n",
    "    end = time.time()\n",
    "    curr_model_data[\"Train_Time\"] = end - start\n",
    "    curr_model_data[\"Train_R2_Score\"] = metrics.r2_score(y_train,curr_model.predict(X_train))\n",
    "    curr_model_data[\"Test_R2_Score\"] = metrics.r2_score(y_test,curr_model.predict(X_test))\n",
    "    curr_model_data[\"Test_RMSE_Score\"] = sqrt(mean_squared_error(y_test,curr_model.predict(X_test)))\n",
    "    model_data.append(curr_model_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Name</th>\n",
       "      <th>Train_Time</th>\n",
       "      <th>Train_R2_Score</th>\n",
       "      <th>Test_R2_Score</th>\n",
       "      <th>Test_RMSE_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lasso:</td>\n",
       "      <td>0.024508</td>\n",
       "      <td>0.132222</td>\n",
       "      <td>-65.575003</td>\n",
       "      <td>884.325112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ridge:</td>\n",
       "      <td>0.016017</td>\n",
       "      <td>0.150873</td>\n",
       "      <td>-63.250048</td>\n",
       "      <td>868.746537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsRegressor:</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.558062</td>\n",
       "      <td>-0.353913</td>\n",
       "      <td>126.110651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVR:</td>\n",
       "      <td>5.732175</td>\n",
       "      <td>-0.010038</td>\n",
       "      <td>-0.053715</td>\n",
       "      <td>111.254571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>17.202532</td>\n",
       "      <td>0.930677</td>\n",
       "      <td>-2.925105</td>\n",
       "      <td>214.724710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ExtraTreeRegressor :</td>\n",
       "      <td>4.544047</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.734311</td>\n",
       "      <td>142.731514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoostingRegressor:</td>\n",
       "      <td>4.376606</td>\n",
       "      <td>0.344863</td>\n",
       "      <td>-21.764943</td>\n",
       "      <td>517.117990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBRegressor:</td>\n",
       "      <td>0.648615</td>\n",
       "      <td>0.879678</td>\n",
       "      <td>-25.046238</td>\n",
       "      <td>553.132096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MLPRegressor:</td>\n",
       "      <td>7.261649</td>\n",
       "      <td>0.421665</td>\n",
       "      <td>-33761.626110</td>\n",
       "      <td>19914.732144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Name  Train_Time  Train_R2_Score  Test_R2_Score  \\\n",
       "0                      Lasso:     0.024508        0.132222     -65.575003   \n",
       "1                      Ridge:     0.016017        0.150873     -63.250048   \n",
       "2        KNeighborsRegressor:     0.000000        0.558062      -0.353913   \n",
       "3                         SVR:    5.732175       -0.010038      -0.053715   \n",
       "4                RandomForest    17.202532        0.930677      -2.925105   \n",
       "5         ExtraTreeRegressor :    4.544047        1.000000      -0.734311   \n",
       "6  GradientBoostingRegressor:     4.376606        0.344863     -21.764943   \n",
       "7               XGBRegressor:     0.648615        0.879678     -25.046238   \n",
       "8               MLPRegressor:     7.261649        0.421665  -33761.626110   \n",
       "\n",
       "   Test_RMSE_Score  \n",
       "0       884.325112  \n",
       "1       868.746537  \n",
       "2       126.110651  \n",
       "3       111.254571  \n",
       "4       214.724710  \n",
       "5       142.731514  \n",
       "6       517.117990  \n",
       "7       553.132096  \n",
       "8     19914.732144  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert list to dataframe\n",
    "df = pd.DataFrame(model_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter Tuning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=ExtraTreesRegressor(random_state=40), n_jobs=-1,\n",
       "             param_grid=[{'max_depth': [80, 150, 200, 250],\n",
       "                          'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                          'n_estimators': [100, 150, 200, 250]}],\n",
       "             scoring='r2', verbose=2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = [{\n",
    "              'max_depth': [80, 150, 200,250],\n",
    "              'n_estimators' : [100,150,200,250],\n",
    "              'max_features': [\"auto\", \"sqrt\", \"log2\"]\n",
    "            }]\n",
    "reg = ExtraTreesRegressor(random_state=40)\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = reg, param_grid = param_grid, cv = 5, n_jobs = -1 , scoring='r2' , verbose=2)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 80, 'max_features': 'sqrt', 'n_estimators': 200}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tuned parameter set\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123.61899779748667"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calcul du RMSE pour ce modèle avec les meilleurs parametres\n",
    "np.sqrt(mean_squared_error(y_test, grid_search.best_estimator_.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Best features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get sorted list of features in order of importance\n",
    "feature_indices = np.argsort(grid_search.best_estimator_.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = grid_search.best_estimator_.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "names = [X_train.columns[i] for i in indices]\n",
    "# Create plot\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Create plot title\n",
    "plt.title(\"Feature Importance\")\n",
    "\n",
    "# Add bars\n",
    "plt.bar(range(X_train.shape[1]), importances[indices])\n",
    "\n",
    "# Add feature names as x-axis labels\n",
    "plt.xticks(range(X_train.shape[1]), names, rotation=90)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RH_1',\n",
       " 'RH_8',\n",
       " 'RH_3',\n",
       " 'RH_out',\n",
       " 'T3',\n",
       " 'RH_2',\n",
       " 'T6',\n",
       " 'T2',\n",
       " 'RH_7',\n",
       " 'Press_mm_hg',\n",
       " 'RH_6',\n",
       " 'Tdewpoint',\n",
       " 'Windspeed',\n",
       " 'RH_9',\n",
       " 'T8',\n",
       " 'T7',\n",
       " 'RH_4',\n",
       " 'T_out',\n",
       " 'T4',\n",
       " 'RH_5',\n",
       " 'T1',\n",
       " 'T5']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get top 20 most important feature \n",
    "names[0:22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T9', 'Visibility', 'Day_of_week', 'rv1', 'rv2']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get 5 least important feature \n",
    "names[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce test & training set to 5 feature set\n",
    "train_5F = X_train[names[0:22]]\n",
    "test_5F = X_test[names[0:22]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RH_1</th>\n",
       "      <th>RH_8</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T6</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_7</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>...</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T8</th>\n",
       "      <th>T7</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>T_out</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_5</th>\n",
       "      <th>T1</th>\n",
       "      <th>T5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>41.590000</td>\n",
       "      <td>39.693333</td>\n",
       "      <td>38.700000</td>\n",
       "      <td>80.666667</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>45.126667</td>\n",
       "      <td>7.030000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>34.933333</td>\n",
       "      <td>753.866667</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.333333</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>21.790000</td>\n",
       "      <td>41.700000</td>\n",
       "      <td>8.73</td>\n",
       "      <td>21.290000</td>\n",
       "      <td>53.647500</td>\n",
       "      <td>22.890000</td>\n",
       "      <td>21.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13690</th>\n",
       "      <td>40.730000</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>37.966667</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>22.390000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>8.895000</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>39.090000</td>\n",
       "      <td>739.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>42.090000</td>\n",
       "      <td>22.760000</td>\n",
       "      <td>21.230000</td>\n",
       "      <td>40.626667</td>\n",
       "      <td>8.90</td>\n",
       "      <td>20.263333</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>21.390000</td>\n",
       "      <td>19.790000</td>\n",
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       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>40.437403</td>\n",
       "      <td>42.810714</td>\n",
       "      <td>38.475974</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>24.948052</td>\n",
       "      <td>40.410390</td>\n",
       "      <td>14.050866</td>\n",
       "      <td>22.818701</td>\n",
       "      <td>37.836364</td>\n",
       "      <td>757.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.097186</td>\n",
       "      <td>23.975974</td>\n",
       "      <td>23.261688</td>\n",
       "      <td>39.453247</td>\n",
       "      <td>8.87</td>\n",
       "      <td>23.682208</td>\n",
       "      <td>50.518019</td>\n",
       "      <td>23.892208</td>\n",
       "      <td>21.322468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14187</th>\n",
       "      <td>45.560000</td>\n",
       "      <td>51.891111</td>\n",
       "      <td>45.290000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>43.200000</td>\n",
       "      <td>9.033333</td>\n",
       "      <td>21.890000</td>\n",
       "      <td>43.220000</td>\n",
       "      <td>758.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>48.863333</td>\n",
       "      <td>20.969444</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>47.900000</td>\n",
       "      <td>9.00</td>\n",
       "      <td>20.323333</td>\n",
       "      <td>55.950000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>18.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8466</th>\n",
       "      <td>42.730000</td>\n",
       "      <td>43.272000</td>\n",
       "      <td>39.790000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>46.466667</td>\n",
       "      <td>6.463333</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>35.700000</td>\n",
       "      <td>751.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.900000</td>\n",
       "      <td>22.434000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>40.163333</td>\n",
       "      <td>6.50</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>45.296250</td>\n",
       "      <td>21.790000</td>\n",
       "      <td>20.852500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            RH_1       RH_8       RH_3     RH_out         T3       RH_2  \\\n",
       "1189   41.590000  39.693333  38.700000  80.666667  24.600000  45.126667   \n",
       "13690  40.730000  44.400000  37.966667  83.000000  22.390000  45.000000   \n",
       "2983   40.437403  42.810714  38.475974  95.000000  24.948052  40.410390   \n",
       "14187  45.560000  51.891111  45.290000  87.000000  22.500000  43.200000   \n",
       "8466   42.730000  43.272000  39.790000  93.000000  23.100000  46.466667   \n",
       "\n",
       "              T6         T2       RH_7  Press_mm_hg  ...  Windspeed  \\\n",
       "1189    7.030000  20.200000  34.933333   753.866667  ...        3.0   \n",
       "13690   8.895000  18.790000  39.090000   739.100000  ...       12.0   \n",
       "2983   14.050866  22.818701  37.836364   757.000000  ...        1.0   \n",
       "14187   9.033333  21.890000  43.220000   758.100000  ...        6.0   \n",
       "8466    6.463333  19.100000  35.700000   751.600000  ...        1.0   \n",
       "\n",
       "            RH_9         T8         T7       RH_4  T_out         T4  \\\n",
       "1189   40.333333  23.500000  21.790000  41.700000   8.73  21.290000   \n",
       "13690  42.090000  22.760000  21.230000  40.626667   8.90  20.263333   \n",
       "2983   42.097186  23.975974  23.261688  39.453247   8.87  23.682208   \n",
       "14187  48.863333  20.969444  18.600000  47.900000   9.00  20.323333   \n",
       "8466   44.900000  22.434000  21.500000  40.163333   6.50  21.700000   \n",
       "\n",
       "            RH_5         T1         T5  \n",
       "1189   53.647500  22.890000  21.175000  \n",
       "13690  47.500000  21.390000  19.790000  \n",
       "2983   50.518019  23.892208  21.322468  \n",
       "14187  55.950000  22.500000  18.710000  \n",
       "8466   45.296250  21.790000  20.852500  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_5F.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(max_depth=80, max_features='sqrt', n_estimators=200,\n",
       "                    random_state=40)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clone the Gridsearch model with his parameter and fit on reduced dataset\n",
    "\n",
    "from sklearn.base import clone\n",
    "cloned_model = clone(grid_search.best_estimator_)\n",
    "cloned_model.fit(train_5F , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set R2 Score -  0.9999990643126436\n",
      "Testing set R2 Score -  0.5611150138289435\n",
      "Testing set RMSE Score -  71.80120319934609\n"
     ]
    }
   ],
   "source": [
    "# Reduced dataset scores \n",
    "\n",
    "print('Training set R2 Score - ', metrics.r2_score(y_train,cloned_model.predict(train_5F)))\n",
    "print('Testing set R2 Score - ', metrics.r2_score(y_test,cloned_model.predict(test_5F)))\n",
    "print('Testing set RMSE Score - ', np.sqrt(mean_squared_error(y_test, cloned_model.predict(test_5F))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best RMSE Score -  71.80120319934609 (22 best param, ExtraTreesRegressor(max_depth=80, max_features='sqrt', n_estimators=250,\n",
    "                    random_state=40))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We train this model with the full train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Appliances</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>RH_8</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>T3</th>\n",
       "      <th>T6</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_7</th>\n",
       "      <th>...</th>\n",
       "      <th>T8</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>T7</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>T_out</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_5</th>\n",
       "      <th>T1</th>\n",
       "      <th>T5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>38.163333</td>\n",
       "      <td>38.550000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>75.333333</td>\n",
       "      <td>20.230</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>40.326667</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>32.126667</td>\n",
       "      <td>...</td>\n",
       "      <td>20.890</td>\n",
       "      <td>36.533333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>18.323333</td>\n",
       "      <td>34.526667</td>\n",
       "      <td>4.63</td>\n",
       "      <td>19.926667</td>\n",
       "      <td>44.800000</td>\n",
       "      <td>21.00</td>\n",
       "      <td>18.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>40.450000</td>\n",
       "      <td>43.526667</td>\n",
       "      <td>38.290000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>22.000</td>\n",
       "      <td>6.690000</td>\n",
       "      <td>44.090000</td>\n",
       "      <td>18.390000</td>\n",
       "      <td>38.656667</td>\n",
       "      <td>...</td>\n",
       "      <td>21.790</td>\n",
       "      <td>43.163333</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>20.230000</td>\n",
       "      <td>38.863333</td>\n",
       "      <td>6.42</td>\n",
       "      <td>19.856667</td>\n",
       "      <td>49.530000</td>\n",
       "      <td>21.00</td>\n",
       "      <td>19.323333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>49.476000</td>\n",
       "      <td>51.220000</td>\n",
       "      <td>43.840000</td>\n",
       "      <td>82.333333</td>\n",
       "      <td>26.834</td>\n",
       "      <td>22.394595</td>\n",
       "      <td>47.159009</td>\n",
       "      <td>24.329730</td>\n",
       "      <td>43.877973</td>\n",
       "      <td>...</td>\n",
       "      <td>24.416</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>23.886486</td>\n",
       "      <td>46.901351</td>\n",
       "      <td>17.50</td>\n",
       "      <td>23.997297</td>\n",
       "      <td>52.576622</td>\n",
       "      <td>24.69</td>\n",
       "      <td>22.927027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>37.730000</td>\n",
       "      <td>43.200000</td>\n",
       "      <td>38.126667</td>\n",
       "      <td>64.666667</td>\n",
       "      <td>24.500</td>\n",
       "      <td>10.625714</td>\n",
       "      <td>39.066667</td>\n",
       "      <td>21.633333</td>\n",
       "      <td>34.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>23.700</td>\n",
       "      <td>43.290000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.790000</td>\n",
       "      <td>36.663333</td>\n",
       "      <td>12.00</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>48.126667</td>\n",
       "      <td>23.79</td>\n",
       "      <td>21.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>39.326667</td>\n",
       "      <td>42.363333</td>\n",
       "      <td>37.290000</td>\n",
       "      <td>52.166667</td>\n",
       "      <td>25.100</td>\n",
       "      <td>23.730000</td>\n",
       "      <td>31.376000</td>\n",
       "      <td>28.178000</td>\n",
       "      <td>34.897500</td>\n",
       "      <td>...</td>\n",
       "      <td>23.600</td>\n",
       "      <td>42.760000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.790000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>18.90</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>82.030000</td>\n",
       "      <td>23.79</td>\n",
       "      <td>22.693333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Appliances       RH_1       RH_8       RH_3     RH_out      T3         T6  \\\n",
       "0          40  38.163333  38.550000  38.000000  75.333333  20.230   5.000000   \n",
       "1          40  40.450000  43.526667  38.290000  97.000000  22.000   6.690000   \n",
       "2          60  49.476000  51.220000  43.840000  82.333333  26.834  22.394595   \n",
       "3          40  37.730000  43.200000  38.126667  64.666667  24.500  10.625714   \n",
       "4         100  39.326667  42.363333  37.290000  52.166667  25.100  23.730000   \n",
       "\n",
       "        RH_2         T2       RH_7  ...      T8       RH_9  Windspeed  \\\n",
       "0  40.326667  18.500000  32.126667  ...  20.890  36.533333   4.666667   \n",
       "1  44.090000  18.390000  38.656667  ...  21.790  43.163333   6.000000   \n",
       "2  47.159009  24.329730  43.877973  ...  24.416  48.400000   2.000000   \n",
       "3  39.066667  21.633333  34.090000  ...  23.700  43.290000   1.000000   \n",
       "4  31.376000  28.178000  34.897500  ...  23.600  42.760000   3.000000   \n",
       "\n",
       "          T7       RH_4  T_out         T4       RH_5     T1         T5  \n",
       "0  18.323333  34.526667   4.63  19.926667  44.800000  21.00  18.730000  \n",
       "1  20.230000  38.863333   6.42  19.856667  49.530000  21.00  19.323333  \n",
       "2  23.886486  46.901351  17.50  23.997297  52.576622  24.69  22.927027  \n",
       "3  22.790000  36.663333  12.00  23.600000  48.126667  23.79  21.100000  \n",
       "4  22.790000  37.500000  18.90  23.600000  82.030000  23.79  22.693333  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creation of the full train dataset with the 22 best features + Appliances\n",
    "train_full = data[['Appliances', 'RH_1',\n",
    " 'RH_8',\n",
    " 'RH_3',\n",
    " 'RH_out',\n",
    " 'T3',\n",
    " 'T6',\n",
    " 'RH_2',\n",
    " 'T2',\n",
    " 'RH_7',\n",
    " 'Press_mm_hg',\n",
    " 'RH_6',\n",
    " 'Tdewpoint',\n",
    " 'T8',\n",
    " 'RH_9',\n",
    " 'Windspeed',\n",
    " 'T7',\n",
    " 'RH_4',\n",
    " 'T_out',\n",
    " 'T4',\n",
    " 'RH_5',\n",
    " 'T1',\n",
    " 'T5']]\n",
    "train_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_full, y_train_full = preprocessing(train_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 40.25874339  42.95179187  39.24387778  79.8156774   22.2628739\n",
      "   7.90221296  40.42255095  20.3401535   35.40349554 755.53579099\n",
      "  54.71484136   3.7706434   22.02627245  41.56468822   4.03155782\n",
      "  20.26140938  39.04030368   7.41014163  20.84720107  50.93821698\n",
      "  21.68488204  19.58890735]\n"
     ]
    }
   ],
   "source": [
    "#Standardization\n",
    "scaler2 = StandardScaler().fit(X_train_full)\n",
    "print(scaler2.mean_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.52849795, -0.84236118, -0.3829031 , ..., -0.68260175,\n",
       "        -0.42601243, -0.46507048],\n",
       "       [ 0.04823816,  0.11001252, -0.29363235, ..., -0.15660107,\n",
       "        -0.42601243, -0.14379972],\n",
       "       [ 2.32474842,  1.58226871,  1.41482506, ...,  0.18219914,\n",
       "         1.86925269,  1.80748356],\n",
       "       ...,\n",
       "       [-1.14979239, -0.23955264, -0.50911347, ..., -1.15277741,\n",
       "        -0.8676488 , -0.58960802],\n",
       "       [-1.18510276, -0.66534597, -0.72151628, ..., -0.53303073,\n",
       "         0.0653858 , -0.83326843],\n",
       "       [-0.63779196,  0.13265771, -0.81694363, ..., -0.92836528,\n",
       "        -0.22489164, -0.21058072]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full = scaler2.transform(X_train_full)\n",
    "X_train_full"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying the model to the real  test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>T5</th>\n",
       "      <th>...</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>rv1</th>\n",
       "      <th>rv2</th>\n",
       "      <th>Day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>20.890000</td>\n",
       "      <td>35.400000</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>39.163333</td>\n",
       "      <td>20.290000</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>19.760000</td>\n",
       "      <td>34.200000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.03</td>\n",
       "      <td>1.770</td>\n",
       "      <td>768.90</td>\n",
       "      <td>88.333333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.622221</td>\n",
       "      <td>25.622221</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>21.890000</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>21.290000</td>\n",
       "      <td>45.360000</td>\n",
       "      <td>21.633333</td>\n",
       "      <td>49.226667</td>\n",
       "      <td>20.533333</td>\n",
       "      <td>40.966667</td>\n",
       "      <td>17.677778</td>\n",
       "      <td>...</td>\n",
       "      <td>44.20</td>\n",
       "      <td>2.700</td>\n",
       "      <td>754.60</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.20</td>\n",
       "      <td>23.474485</td>\n",
       "      <td>23.474485</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>21.390000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>17.633333</td>\n",
       "      <td>40.530000</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>35.200000</td>\n",
       "      <td>20.290000</td>\n",
       "      <td>33.760000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>38.29</td>\n",
       "      <td>0.200</td>\n",
       "      <td>766.20</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>0.143368</td>\n",
       "      <td>0.143368</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>21.390000</td>\n",
       "      <td>41.033333</td>\n",
       "      <td>23.890000</td>\n",
       "      <td>34.840000</td>\n",
       "      <td>22.033333</td>\n",
       "      <td>36.933333</td>\n",
       "      <td>22.390000</td>\n",
       "      <td>35.236000</td>\n",
       "      <td>19.633333</td>\n",
       "      <td>...</td>\n",
       "      <td>38.56</td>\n",
       "      <td>8.850</td>\n",
       "      <td>767.80</td>\n",
       "      <td>70.833333</td>\n",
       "      <td>5.166667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.72</td>\n",
       "      <td>10.293451</td>\n",
       "      <td>10.293451</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>19.963333</td>\n",
       "      <td>35.126667</td>\n",
       "      <td>16.463333</td>\n",
       "      <td>40.126667</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>19.260000</td>\n",
       "      <td>34.966667</td>\n",
       "      <td>17.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>40.70</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>769.65</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>48.833333</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>10.754162</td>\n",
       "      <td>10.754162</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   lights         T1       RH_1         T2       RH_2         T3       RH_3  \\\n",
       "0       0  20.890000  35.400000  17.760000  39.163333  20.290000  36.900000   \n",
       "1      10  21.890000  53.100000  21.290000  45.360000  21.633333  49.226667   \n",
       "2       0  21.390000  35.500000  17.633333  40.530000  21.666667  35.200000   \n",
       "3       0  21.390000  41.033333  23.890000  34.840000  22.033333  36.933333   \n",
       "4       0  19.963333  35.126667  16.463333  40.126667  20.000000  36.400000   \n",
       "\n",
       "          T4       RH_4         T5  ...   RH_9  T_out  Press_mm_hg     RH_out  \\\n",
       "0  19.760000  34.200000  18.600000  ...  39.03  1.770       768.90  88.333333   \n",
       "1  20.533333  40.966667  17.677778  ...  44.20  2.700       754.60  90.000000   \n",
       "2  20.290000  33.760000  19.600000  ...  38.29  0.200       766.20  83.000000   \n",
       "3  22.390000  35.236000  19.633333  ...  38.56  8.850       767.80  70.833333   \n",
       "4  19.260000  34.966667  17.890000  ...  40.70 -0.467       769.65  93.333333   \n",
       "\n",
       "   Windspeed  Visibility  Tdewpoint        rv1        rv2  Day_of_week  \n",
       "0   2.333333   63.000000       0.00  25.622221  25.622221       Monday  \n",
       "1   3.000000   27.000000       1.20  23.474485  23.474485     Saturday  \n",
       "2   2.000000   65.000000      -2.40   0.143368   0.143368      Tuesday  \n",
       "3   5.166667   40.000000       3.72  10.293451  10.293451    Wednesday  \n",
       "4   4.000000   48.833333      -1.45  10.754162  10.754162       Sunday  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Day_of_week\"] = encoder.fit_transform(test[\"Day_of_week\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>RH_1</th>\n",
       "      <th>RH_8</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>T3</th>\n",
       "      <th>T6</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_7</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>...</th>\n",
       "      <th>T8</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>T7</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>T_out</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_5</th>\n",
       "      <th>T1</th>\n",
       "      <th>T5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.400000</td>\n",
       "      <td>39.747500</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>88.333333</td>\n",
       "      <td>20.290000</td>\n",
       "      <td>1.198571</td>\n",
       "      <td>39.163333</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>33.663333</td>\n",
       "      <td>768.90</td>\n",
       "      <td>...</td>\n",
       "      <td>23.068750</td>\n",
       "      <td>39.03</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>21.60</td>\n",
       "      <td>34.200000</td>\n",
       "      <td>1.770</td>\n",
       "      <td>19.760000</td>\n",
       "      <td>51.290000</td>\n",
       "      <td>20.890000</td>\n",
       "      <td>18.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.100000</td>\n",
       "      <td>47.563333</td>\n",
       "      <td>49.226667</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>21.633333</td>\n",
       "      <td>2.530000</td>\n",
       "      <td>45.360000</td>\n",
       "      <td>21.290000</td>\n",
       "      <td>39.345000</td>\n",
       "      <td>754.60</td>\n",
       "      <td>...</td>\n",
       "      <td>20.616667</td>\n",
       "      <td>44.20</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.89</td>\n",
       "      <td>40.966667</td>\n",
       "      <td>2.700</td>\n",
       "      <td>20.533333</td>\n",
       "      <td>58.204444</td>\n",
       "      <td>21.890000</td>\n",
       "      <td>17.677778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.500000</td>\n",
       "      <td>39.626667</td>\n",
       "      <td>35.200000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>-0.266667</td>\n",
       "      <td>40.530000</td>\n",
       "      <td>17.633333</td>\n",
       "      <td>27.290000</td>\n",
       "      <td>766.20</td>\n",
       "      <td>...</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>38.29</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.79</td>\n",
       "      <td>33.760000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>20.290000</td>\n",
       "      <td>40.290000</td>\n",
       "      <td>21.390000</td>\n",
       "      <td>19.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.033333</td>\n",
       "      <td>38.090000</td>\n",
       "      <td>36.933333</td>\n",
       "      <td>70.833333</td>\n",
       "      <td>22.033333</td>\n",
       "      <td>11.160000</td>\n",
       "      <td>34.840000</td>\n",
       "      <td>23.890000</td>\n",
       "      <td>30.390000</td>\n",
       "      <td>767.80</td>\n",
       "      <td>...</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>38.56</td>\n",
       "      <td>5.166667</td>\n",
       "      <td>20.05</td>\n",
       "      <td>35.236000</td>\n",
       "      <td>8.850</td>\n",
       "      <td>22.390000</td>\n",
       "      <td>43.266667</td>\n",
       "      <td>21.390000</td>\n",
       "      <td>19.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.126667</td>\n",
       "      <td>39.590000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>-0.733333</td>\n",
       "      <td>40.126667</td>\n",
       "      <td>16.463333</td>\n",
       "      <td>34.060000</td>\n",
       "      <td>769.65</td>\n",
       "      <td>...</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>40.70</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>20.39</td>\n",
       "      <td>34.966667</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>19.260000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.963333</td>\n",
       "      <td>17.890000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RH_1       RH_8       RH_3     RH_out         T3         T6  \\\n",
       "0  35.400000  39.747500  36.900000  88.333333  20.290000   1.198571   \n",
       "1  53.100000  47.563333  49.226667  90.000000  21.633333   2.530000   \n",
       "2  35.500000  39.626667  35.200000  83.000000  21.666667  -0.266667   \n",
       "3  41.033333  38.090000  36.933333  70.833333  22.033333  11.160000   \n",
       "4  35.126667  39.590000  36.400000  93.333333  20.000000  -0.733333   \n",
       "\n",
       "        RH_2         T2       RH_7  Press_mm_hg  ...         T8   RH_9  \\\n",
       "0  39.163333  17.760000  33.663333       768.90  ...  23.068750  39.03   \n",
       "1  45.360000  21.290000  39.345000       754.60  ...  20.616667  44.20   \n",
       "2  40.530000  17.633333  27.290000       766.20  ...  22.100000  38.29   \n",
       "3  34.840000  23.890000  30.390000       767.80  ...  21.200000  38.56   \n",
       "4  40.126667  16.463333  34.060000       769.65  ...  22.100000  40.70   \n",
       "\n",
       "   Windspeed     T7       RH_4  T_out         T4       RH_5         T1  \\\n",
       "0   2.333333  21.60  34.200000  1.770  19.760000  51.290000  20.890000   \n",
       "1   3.000000  16.89  40.966667  2.700  20.533333  58.204444  21.890000   \n",
       "2   2.000000  20.79  33.760000  0.200  20.290000  40.290000  21.390000   \n",
       "3   5.166667  20.05  35.236000  8.850  22.390000  43.266667  21.390000   \n",
       "4   4.000000  20.39  34.966667 -0.467  19.260000  49.000000  19.963333   \n",
       "\n",
       "          T5  \n",
       "0  18.600000  \n",
       "1  17.677778  \n",
       "2  19.600000  \n",
       "3  19.633333  \n",
       "4  17.890000  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_final = test[[ 'RH_1',\n",
    " 'RH_8',\n",
    " 'RH_3',\n",
    " 'RH_out',\n",
    " 'T3',\n",
    " 'T6',\n",
    " 'RH_2',\n",
    " 'T2',\n",
    " 'RH_7',\n",
    " 'Press_mm_hg',\n",
    " 'RH_6',\n",
    " 'Tdewpoint',\n",
    " 'T8',\n",
    " 'RH_9',\n",
    " 'Windspeed',\n",
    " 'T7',\n",
    " 'RH_4',\n",
    " 'T_out',\n",
    " 'T4',\n",
    " 'RH_5',\n",
    " 'T1',\n",
    " 'T5']]\n",
    "test_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = ExtraTreesRegressor(max_depth=80, max_features='sqrt', n_estimators=250,\n",
    "                    random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(max_depth=80, max_features='sqrt', n_estimators=250,\n",
       "                    random_state=40)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.fit(X_train_full, y_train_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 56.92, 108.08,  49.6 , ..., 135.16, 124.92, 155.04])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = final_model.predict(test_final)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_be_submitted = pd.DataFrame({\"id\": test.index+1, \"Appliances\": predictions})\n",
    "to_be_submitted.to_csv(\"to_be_submitted2.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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