LR_RF_Assignment3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['alcohol_by_weight', 'bitterness', 'nitrogen', 'turbidity', 'sugars',\n",
      "       'degree_of_fermentation', 'calorific_value', 'density', 'pH', 'colour',\n",
      "       'sulphites', 'rating'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol_by_weight</th>\n",
       "      <th>bitterness</th>\n",
       "      <th>nitrogen</th>\n",
       "      <th>turbidity</th>\n",
       "      <th>sugars</th>\n",
       "      <th>degree_of_fermentation</th>\n",
       "      <th>calorific_value</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>colour</th>\n",
       "      <th>sulphites</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.476190</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.6268</td>\n",
       "      <td>15.75</td>\n",
       "      <td>66.470</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9678</td>\n",
       "      <td>3.72</td>\n",
       "      <td>11.825</td>\n",
       "      <td>44.3750</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.523810</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.530</td>\n",
       "      <td>1.6268</td>\n",
       "      <td>15.12</td>\n",
       "      <td>67.252</td>\n",
       "      <td>26</td>\n",
       "      <td>0.9676</td>\n",
       "      <td>3.73</td>\n",
       "      <td>13.760</td>\n",
       "      <td>36.2500</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.714286</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.7968</td>\n",
       "      <td>17.64</td>\n",
       "      <td>82.110</td>\n",
       "      <td>32</td>\n",
       "      <td>0.9698</td>\n",
       "      <td>3.74</td>\n",
       "      <td>14.405</td>\n",
       "      <td>16.5625</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.238095</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.465</td>\n",
       "      <td>1.6268</td>\n",
       "      <td>15.75</td>\n",
       "      <td>68.816</td>\n",
       "      <td>42</td>\n",
       "      <td>0.9666</td>\n",
       "      <td>3.86</td>\n",
       "      <td>16.770</td>\n",
       "      <td>33.1250</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.476190</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.7968</td>\n",
       "      <td>15.12</td>\n",
       "      <td>60.214</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9674</td>\n",
       "      <td>3.59</td>\n",
       "      <td>12.255</td>\n",
       "      <td>10.3125</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol_by_weight  bitterness  nitrogen  turbidity  sugars  \\\n",
       "0           4.476190        10.4     0.500     1.6268   15.75   \n",
       "1           4.523810        10.3     0.530     1.6268   15.12   \n",
       "2           4.714286        11.5     0.740     0.7968   17.64   \n",
       "3           5.238095         8.4     0.465     1.6268   15.75   \n",
       "4           4.476190        11.0     0.730     0.7968   15.12   \n",
       "\n",
       "   degree_of_fermentation  calorific_value  density    pH  colour  sulphites  \\\n",
       "0                  66.470               41   0.9678  3.72  11.825    44.3750   \n",
       "1                  67.252               26   0.9676  3.73  13.760    36.2500   \n",
       "2                  82.110               32   0.9698  3.74  14.405    16.5625   \n",
       "3                  68.816               42   0.9666  3.86  16.770    33.1250   \n",
       "4                  60.214                9   0.9674  3.59  12.255    10.3125   \n",
       "\n",
       "   rating  \n",
       "0      60  \n",
       "1      61  \n",
       "2      61  \n",
       "3      60  \n",
       "4      60  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('C:\\\\Users\\\\javva\\\\Downloads\\\\beer_ratings.txt', sep=\"\\t\", header=None)\n",
    "data.columns= ['alcohol_by_weight', 'rating', 'bitterness', 'nitrogen', 'turbidity', 'sugars', 'degree_of_fermentation', 'calorific_value', 'density', 'pH', 'colour', 'sulphites']\n",
    "#Rearranging columns\n",
    "data = data[['alcohol_by_weight', 'bitterness', 'nitrogen', 'turbidity', 'sugars', 'degree_of_fermentation', 'calorific_value', 'density', 'pH', 'colour', 'sulphites', 'rating']]\n",
    "print(data.columns)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 936 entries, 0 to 935\n",
      "Data columns (total 12 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   alcohol_by_weight       936 non-null    float64\n",
      " 1   bitterness              936 non-null    float64\n",
      " 2   nitrogen                936 non-null    float64\n",
      " 3   turbidity               936 non-null    float64\n",
      " 4   sugars                  936 non-null    float64\n",
      " 5   degree_of_fermentation  936 non-null    float64\n",
      " 6   calorific_value         936 non-null    int64  \n",
      " 7   density                 936 non-null    float64\n",
      " 8   pH                      936 non-null    float64\n",
      " 9   colour                  936 non-null    float64\n",
      " 10  sulphites               936 non-null    float64\n",
      " 11  rating                  936 non-null    int64  \n",
      "dtypes: float64(10), int64(2)\n",
      "memory usage: 87.9 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>alcohol_by_weight</th>\n",
       "      <th>bitterness</th>\n",
       "      <th>nitrogen</th>\n",
       "      <th>turbidity</th>\n",
       "      <th>sugars</th>\n",
       "      <th>degree_of_fermentation</th>\n",
       "      <th>calorific_value</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>colour</th>\n",
       "      <th>sulphites</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>936.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.964243</td>\n",
       "      <td>9.637821</td>\n",
       "      <td>0.628862</td>\n",
       "      <td>0.939390</td>\n",
       "      <td>16.305962</td>\n",
       "      <td>68.374036</td>\n",
       "      <td>18.389957</td>\n",
       "      <td>0.966954</td>\n",
       "      <td>3.803814</td>\n",
       "      <td>14.106389</td>\n",
       "      <td>14.020433</td>\n",
       "      <td>67.323718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.521894</td>\n",
       "      <td>1.852364</td>\n",
       "      <td>0.187830</td>\n",
       "      <td>0.662604</td>\n",
       "      <td>8.465123</td>\n",
       "      <td>35.483428</td>\n",
       "      <td>10.147082</td>\n",
       "      <td>0.001937</td>\n",
       "      <td>0.157151</td>\n",
       "      <td>3.726049</td>\n",
       "      <td>10.288885</td>\n",
       "      <td>10.047955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.560000</td>\n",
       "      <td>9.384000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.960640</td>\n",
       "      <td>3.240000</td>\n",
       "      <td>7.095000</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.523810</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.332000</td>\n",
       "      <td>11.970000</td>\n",
       "      <td>55.522000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.965770</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>11.825000</td>\n",
       "      <td>6.562500</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.809524</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.896400</td>\n",
       "      <td>13.860000</td>\n",
       "      <td>62.560000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.966935</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>13.330000</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.238095</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.494000</td>\n",
       "      <td>17.010000</td>\n",
       "      <td>71.944000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.968100</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>15.748750</td>\n",
       "      <td>18.515625</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.095238</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>86.940000</td>\n",
       "      <td>477.020000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>0.973200</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>90.312500</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       alcohol_by_weight  bitterness    nitrogen   turbidity      sugars  \\\n",
       "count         936.000000  936.000000  936.000000  936.000000  936.000000   \n",
       "mean            4.964243    9.637821    0.628862    0.939390   16.305962   \n",
       "std             0.521894    1.852364    0.187830    0.662604    8.465123   \n",
       "min             4.000000    5.700000    0.220000    0.000000    7.560000   \n",
       "25%             4.523810    8.300000    0.490000    0.332000   11.970000   \n",
       "50%             4.809524    9.300000    0.620000    0.896400   13.860000   \n",
       "75%             5.238095   10.700000    0.740000    1.494000   17.010000   \n",
       "max             7.095238   17.000000    1.680000    3.320000   86.940000   \n",
       "\n",
       "       degree_of_fermentation  calorific_value     density          pH  \\\n",
       "count              936.000000       936.000000  936.000000  936.000000   \n",
       "mean                68.374036        18.389957    0.966954    3.803814   \n",
       "std                 35.483428        10.147082    0.001937    0.157151   \n",
       "min                  9.384000         4.000000    0.960640    3.240000   \n",
       "25%                 55.522000        10.000000    0.965770    3.700000   \n",
       "50%                 62.560000        16.000000    0.966935    3.800000   \n",
       "75%                 71.944000        24.000000    0.968100    3.900000   \n",
       "max                477.020000        69.000000    0.973200    4.400000   \n",
       "\n",
       "           colour   sulphites      rating  \n",
       "count  936.000000  936.000000  936.000000  \n",
       "mean    14.106389   14.020433   67.323718  \n",
       "std      3.726049   10.288885   10.047955  \n",
       "min      7.095000    1.875000   38.000000  \n",
       "25%     11.825000    6.562500   60.000000  \n",
       "50%     13.330000   11.250000   64.000000  \n",
       "75%     15.748750   18.515625   74.000000  \n",
       "max     43.000000   90.312500   94.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate Rows :\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol_by_weight</th>\n",
       "      <th>bitterness</th>\n",
       "      <th>nitrogen</th>\n",
       "      <th>turbidity</th>\n",
       "      <th>sugars</th>\n",
       "      <th>degree_of_fermentation</th>\n",
       "      <th>calorific_value</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>colour</th>\n",
       "      <th>sulphites</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.476190</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.7968</td>\n",
       "      <td>15.12</td>\n",
       "      <td>60.214</td>\n",
       "      <td>9</td>\n",
       "      <td>0.96740</td>\n",
       "      <td>3.59</td>\n",
       "      <td>12.255</td>\n",
       "      <td>10.3125</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>4.571429</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.1328</td>\n",
       "      <td>12.60</td>\n",
       "      <td>64.124</td>\n",
       "      <td>13</td>\n",
       "      <td>0.96760</td>\n",
       "      <td>3.72</td>\n",
       "      <td>9.460</td>\n",
       "      <td>6.8750</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>4.523810</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1.065</td>\n",
       "      <td>0.3320</td>\n",
       "      <td>13.23</td>\n",
       "      <td>87.584</td>\n",
       "      <td>14</td>\n",
       "      <td>0.96630</td>\n",
       "      <td>3.76</td>\n",
       "      <td>10.750</td>\n",
       "      <td>6.8750</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>4.523810</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.3320</td>\n",
       "      <td>18.27</td>\n",
       "      <td>69.598</td>\n",
       "      <td>20</td>\n",
       "      <td>0.96803</td>\n",
       "      <td>3.79</td>\n",
       "      <td>11.825</td>\n",
       "      <td>12.5000</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>5.333333</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.8964</td>\n",
       "      <td>14.49</td>\n",
       "      <td>43.010</td>\n",
       "      <td>17</td>\n",
       "      <td>0.96516</td>\n",
       "      <td>3.68</td>\n",
       "      <td>17.200</td>\n",
       "      <td>8.7500</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.0332</td>\n",
       "      <td>11.97</td>\n",
       "      <td>62.560</td>\n",
       "      <td>11</td>\n",
       "      <td>0.96746</td>\n",
       "      <td>4.19</td>\n",
       "      <td>15.695</td>\n",
       "      <td>7.8125</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>4.476190</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.9960</td>\n",
       "      <td>13.23</td>\n",
       "      <td>64.906</td>\n",
       "      <td>38</td>\n",
       "      <td>0.96700</td>\n",
       "      <td>3.94</td>\n",
       "      <td>11.180</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>4.523810</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.9628</td>\n",
       "      <td>13.23</td>\n",
       "      <td>58.650</td>\n",
       "      <td>22</td>\n",
       "      <td>0.96718</td>\n",
       "      <td>3.90</td>\n",
       "      <td>13.760</td>\n",
       "      <td>20.6250</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>4.857143</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.9960</td>\n",
       "      <td>28.35</td>\n",
       "      <td>53.176</td>\n",
       "      <td>23</td>\n",
       "      <td>0.96914</td>\n",
       "      <td>3.80</td>\n",
       "      <td>25.155</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>4.523810</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8.82</td>\n",
       "      <td>44.574</td>\n",
       "      <td>18</td>\n",
       "      <td>0.96448</td>\n",
       "      <td>3.86</td>\n",
       "      <td>9.675</td>\n",
       "      <td>8.1250</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>4.285714</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.590</td>\n",
       "      <td>1.0956</td>\n",
       "      <td>11.97</td>\n",
       "      <td>57.868</td>\n",
       "      <td>30</td>\n",
       "      <td>0.96706</td>\n",
       "      <td>3.91</td>\n",
       "      <td>12.470</td>\n",
       "      <td>26.5625</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>5.428571</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.1328</td>\n",
       "      <td>10.71</td>\n",
       "      <td>38.318</td>\n",
       "      <td>8</td>\n",
       "      <td>0.96420</td>\n",
       "      <td>4.22</td>\n",
       "      <td>12.470</td>\n",
       "      <td>4.0625</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>4.619048</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.8632</td>\n",
       "      <td>11.97</td>\n",
       "      <td>69.598</td>\n",
       "      <td>26</td>\n",
       "      <td>0.96636</td>\n",
       "      <td>3.76</td>\n",
       "      <td>12.685</td>\n",
       "      <td>45.9375</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>4.809524</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.4316</td>\n",
       "      <td>12.60</td>\n",
       "      <td>59.432</td>\n",
       "      <td>15</td>\n",
       "      <td>0.96546</td>\n",
       "      <td>3.79</td>\n",
       "      <td>11.610</td>\n",
       "      <td>6.2500</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>4.380952</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0.310</td>\n",
       "      <td>1.4608</td>\n",
       "      <td>13.86</td>\n",
       "      <td>58.650</td>\n",
       "      <td>13</td>\n",
       "      <td>0.97005</td>\n",
       "      <td>3.57</td>\n",
       "      <td>18.060</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>5.476190</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.760</td>\n",
       "      <td>1.2948</td>\n",
       "      <td>20.16</td>\n",
       "      <td>64.906</td>\n",
       "      <td>24</td>\n",
       "      <td>0.96890</td>\n",
       "      <td>3.87</td>\n",
       "      <td>15.265</td>\n",
       "      <td>18.4375</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>5.238095</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.340</td>\n",
       "      <td>1.5604</td>\n",
       "      <td>13.23</td>\n",
       "      <td>51.612</td>\n",
       "      <td>9</td>\n",
       "      <td>0.96780</td>\n",
       "      <td>3.65</td>\n",
       "      <td>19.350</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>5.571429</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.9296</td>\n",
       "      <td>15.12</td>\n",
       "      <td>9.384</td>\n",
       "      <td>39</td>\n",
       "      <td>0.96064</td>\n",
       "      <td>3.76</td>\n",
       "      <td>8.385</td>\n",
       "      <td>31.2500</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>5.142857</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.3320</td>\n",
       "      <td>15.12</td>\n",
       "      <td>62.560</td>\n",
       "      <td>19</td>\n",
       "      <td>0.96736</td>\n",
       "      <td>4.08</td>\n",
       "      <td>14.835</td>\n",
       "      <td>10.3125</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>5.904762</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.380</td>\n",
       "      <td>1.5936</td>\n",
       "      <td>13.23</td>\n",
       "      <td>72.726</td>\n",
       "      <td>9</td>\n",
       "      <td>0.96408</td>\n",
       "      <td>3.76</td>\n",
       "      <td>13.330</td>\n",
       "      <td>3.7500</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>4.714286</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.9296</td>\n",
       "      <td>11.97</td>\n",
       "      <td>86.020</td>\n",
       "      <td>23</td>\n",
       "      <td>0.96720</td>\n",
       "      <td>3.43</td>\n",
       "      <td>41.925</td>\n",
       "      <td>42.5000</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>6.666667</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>11.34</td>\n",
       "      <td>39.100</td>\n",
       "      <td>30</td>\n",
       "      <td>0.96160</td>\n",
       "      <td>4.18</td>\n",
       "      <td>16.985</td>\n",
       "      <td>19.6875</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>4.952381</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.3984</td>\n",
       "      <td>18.27</td>\n",
       "      <td>64.124</td>\n",
       "      <td>33</td>\n",
       "      <td>0.96865</td>\n",
       "      <td>3.80</td>\n",
       "      <td>13.760</td>\n",
       "      <td>23.1250</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>4.476190</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.360</td>\n",
       "      <td>1.3944</td>\n",
       "      <td>12.60</td>\n",
       "      <td>62.560</td>\n",
       "      <td>14</td>\n",
       "      <td>0.96740</td>\n",
       "      <td>3.71</td>\n",
       "      <td>17.200</td>\n",
       "      <td>8.4375</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>4.761905</td>\n",
       "      <td>14.8</td>\n",
       "      <td>0.515</td>\n",
       "      <td>2.2576</td>\n",
       "      <td>18.27</td>\n",
       "      <td>66.470</td>\n",
       "      <td>20</td>\n",
       "      <td>0.97140</td>\n",
       "      <td>3.56</td>\n",
       "      <td>17.200</td>\n",
       "      <td>13.4375</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol_by_weight  bitterness  nitrogen  turbidity  sugars  \\\n",
       "7             4.476190        11.0     0.730     0.7968   15.12   \n",
       "103           4.571429         9.5     0.660     0.1328   12.60   \n",
       "116           4.523810         8.8     1.065     0.3320   13.23   \n",
       "127           4.523810         9.4     0.750     0.3320   18.27   \n",
       "199           5.333333         9.7     0.570     0.8964   14.49   \n",
       "203           5.000000         7.7     0.710     0.0332   11.97   \n",
       "213           4.476190         8.3     0.510     0.9960   13.23   \n",
       "277           4.523810         8.7     0.530     0.9628   13.23   \n",
       "298           4.857143         8.1     0.700     0.9960   28.35   \n",
       "318           4.523810         7.1     0.600     0.0000    8.82   \n",
       "378           4.285714         8.7     0.590     1.0956   11.97   \n",
       "394           5.428571         6.7     0.640     0.1328   10.71   \n",
       "397           4.619048         8.8     0.640     0.8632   11.97   \n",
       "425           4.809524         8.3     0.795     0.4316   12.60   \n",
       "531           4.380952        16.1     0.310     1.4608   13.86   \n",
       "540           5.476190        10.9     0.760     1.2948   20.16   \n",
       "561           5.238095        11.6     0.340     1.5604   13.23   \n",
       "597           5.571429         7.8     0.380     0.9296   15.12   \n",
       "610           5.142857         8.5     0.735     0.3320   15.12   \n",
       "628           5.904762         9.4     0.380     1.5936   13.23   \n",
       "736           4.714286         9.7     0.590     0.9296   11.97   \n",
       "753           6.666667         6.3     0.440     0.0000   11.34   \n",
       "817           4.952381        10.7     0.870     0.3984   18.27   \n",
       "851           4.476190         9.4     0.360     1.3944   12.60   \n",
       "856           4.761905        14.8     0.515     2.2576   18.27   \n",
       "\n",
       "     degree_of_fermentation  calorific_value  density    pH  colour  \\\n",
       "7                    60.214                9  0.96740  3.59  12.255   \n",
       "103                  64.124               13  0.96760  3.72   9.460   \n",
       "116                  87.584               14  0.96630  3.76  10.750   \n",
       "127                  69.598               20  0.96803  3.79  11.825   \n",
       "199                  43.010               17  0.96516  3.68  17.200   \n",
       "203                  62.560               11  0.96746  4.19  15.695   \n",
       "213                  64.906               38  0.96700  3.94  11.180   \n",
       "277                  58.650               22  0.96718  3.90  13.760   \n",
       "298                  53.176               23  0.96914  3.80  25.155   \n",
       "318                  44.574               18  0.96448  3.86   9.675   \n",
       "378                  57.868               30  0.96706  3.91  12.470   \n",
       "394                  38.318                8  0.96420  4.22  12.470   \n",
       "397                  69.598               26  0.96636  3.76  12.685   \n",
       "425                  59.432               15  0.96546  3.79  11.610   \n",
       "531                  58.650               13  0.97005  3.57  18.060   \n",
       "540                  64.906               24  0.96890  3.87  15.265   \n",
       "561                  51.612                9  0.96780  3.65  19.350   \n",
       "597                   9.384               39  0.96064  3.76   8.385   \n",
       "610                  62.560               19  0.96736  4.08  14.835   \n",
       "628                  72.726                9  0.96408  3.76  13.330   \n",
       "736                  86.020               23  0.96720  3.43  41.925   \n",
       "753                  39.100               30  0.96160  4.18  16.985   \n",
       "817                  64.124               33  0.96865  3.80  13.760   \n",
       "851                  62.560               14  0.96740  3.71  17.200   \n",
       "856                  66.470               20  0.97140  3.56  17.200   \n",
       "\n",
       "     sulphites  rating  \n",
       "7      10.3125      60  \n",
       "103     6.8750      63  \n",
       "116     6.8750      60  \n",
       "127    12.5000      64  \n",
       "199     8.7500      63  \n",
       "203     7.8125      61  \n",
       "213    22.5000      58  \n",
       "277    20.6250      64  \n",
       "298    34.3750      58  \n",
       "318     8.1250      61  \n",
       "378    26.5625      61  \n",
       "394     4.0625      64  \n",
       "397    45.9375      61  \n",
       "425     6.2500      60  \n",
       "531     7.5000      83  \n",
       "540    18.4375      80  \n",
       "561     7.5000      80  \n",
       "597    31.2500      80  \n",
       "610    10.3125      80  \n",
       "628     3.7500      82  \n",
       "736    42.5000      73  \n",
       "753    19.6875      68  \n",
       "817    23.1250      72  \n",
       "851     8.4375      74  \n",
       "856    13.4375      70  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for duplicates\n",
    "duplicate = data[data.duplicated()] \n",
    "print(\"Duplicate Rows :\") \n",
    "duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "911\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol_by_weight</th>\n",
       "      <th>bitterness</th>\n",
       "      <th>nitrogen</th>\n",
       "      <th>turbidity</th>\n",
       "      <th>sugars</th>\n",
       "      <th>degree_of_fermentation</th>\n",
       "      <th>calorific_value</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>colour</th>\n",
       "      <th>sulphites</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>911.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.965196</td>\n",
       "      <td>9.643249</td>\n",
       "      <td>0.629468</td>\n",
       "      <td>0.943048</td>\n",
       "      <td>16.358562</td>\n",
       "      <td>68.634878</td>\n",
       "      <td>18.349067</td>\n",
       "      <td>0.966961</td>\n",
       "      <td>3.803611</td>\n",
       "      <td>14.073650</td>\n",
       "      <td>13.957876</td>\n",
       "      <td>67.305159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.521084</td>\n",
       "      <td>1.841946</td>\n",
       "      <td>0.188059</td>\n",
       "      <td>0.664428</td>\n",
       "      <td>8.550900</td>\n",
       "      <td>35.839943</td>\n",
       "      <td>10.179110</td>\n",
       "      <td>0.001924</td>\n",
       "      <td>0.155986</td>\n",
       "      <td>3.613573</td>\n",
       "      <td>10.239643</td>\n",
       "      <td>10.088791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.560000</td>\n",
       "      <td>9.384000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.960640</td>\n",
       "      <td>3.240000</td>\n",
       "      <td>7.095000</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.523810</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.332000</td>\n",
       "      <td>11.970000</td>\n",
       "      <td>55.522000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.965775</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>11.825000</td>\n",
       "      <td>6.562500</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.809524</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.896400</td>\n",
       "      <td>13.860000</td>\n",
       "      <td>62.560000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.966900</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>13.330000</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.238095</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.494000</td>\n",
       "      <td>17.010000</td>\n",
       "      <td>71.944000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.968120</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>15.695000</td>\n",
       "      <td>18.281250</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.095238</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>86.940000</td>\n",
       "      <td>477.020000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>0.973200</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>90.312500</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       alcohol_by_weight  bitterness    nitrogen   turbidity      sugars  \\\n",
       "count         911.000000  911.000000  911.000000  911.000000  911.000000   \n",
       "mean            4.965196    9.643249    0.629468    0.943048   16.358562   \n",
       "std             0.521084    1.841946    0.188059    0.664428    8.550900   \n",
       "min             4.000000    5.700000    0.220000    0.000000    7.560000   \n",
       "25%             4.523810    8.300000    0.490000    0.332000   11.970000   \n",
       "50%             4.809524    9.300000    0.620000    0.896400   13.860000   \n",
       "75%             5.238095   10.750000    0.740000    1.494000   17.010000   \n",
       "max             7.095238   17.000000    1.680000    3.320000   86.940000   \n",
       "\n",
       "       degree_of_fermentation  calorific_value     density          pH  \\\n",
       "count              911.000000       911.000000  911.000000  911.000000   \n",
       "mean                68.634878        18.349067    0.966961    3.803611   \n",
       "std                 35.839943        10.179110    0.001924    0.155986   \n",
       "min                  9.384000         4.000000    0.960640    3.240000   \n",
       "25%                 55.522000        10.000000    0.965775    3.700000   \n",
       "50%                 62.560000        16.000000    0.966900    3.800000   \n",
       "75%                 71.944000        24.000000    0.968120    3.900000   \n",
       "max                477.020000        69.000000    0.973200    4.400000   \n",
       "\n",
       "           colour   sulphites      rating  \n",
       "count  911.000000  911.000000  911.000000  \n",
       "mean    14.073650   13.957876   67.305159  \n",
       "std      3.613573   10.239643   10.088791  \n",
       "min      7.095000    1.875000   38.000000  \n",
       "25%     11.825000    6.562500   60.000000  \n",
       "50%     13.330000   11.250000   64.000000  \n",
       "75%     15.695000   18.281250   74.000000  \n",
       "max     43.000000   90.312500   94.000000  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping duplicates\n",
    "data.drop_duplicates(keep='first',inplace=True) \n",
    "print(len(data))\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1eed050c1f0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1188x180 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "#sns.pairplot(data)\n",
    "#sns.lmplot(x=\"nitrogen\", y=\"rating\", data=data, x_estimator=np.mean);\n",
    "#plotting all the independent variables against the dependent variable\n",
    "sns.pairplot(\n",
    "    data,\n",
    "    x_vars=[\"alcohol_by_weight\", \"bitterness\", \"nitrogen\", \"turbidity\", \"sugars\", \"degree_of_fermentation\", \"calorific_value\", \"density\", \"pH\", \"colour\", \"sulphites\"],\n",
    "    y_vars=[\"rating\"],\n",
    "     aspect=0.6\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(911, 11)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_data,Y_data = data.iloc[:,:-1].values, data.iloc[:,-1].values\n",
    "print(X_data.shape)\n",
    "#Splitting the data into train and test sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_data, Y_data, train_size = 2/3, random_state = 0)\n",
    "X_train\n",
    "\n",
    "#Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train_scaled = sc.fit_transform(X_train)\n",
    "X_test_scaled = sc.transform(X_test)\n",
    "# print(X_train.shape)\n",
    "# X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression R2_Score for Training Data=> 0.424396093166991\n",
      "Linear Regression R2_Score for Test Data=> 0.4165566602445099\n",
      "Linear Regression RMSE for Train data=> 7.723937643900514\n",
      "Linear Regression RMSE for Test data=> 7.544645748037056\n",
      "\n",
      "RandomForest Regressor R2_Score for Training Data=> 0.5955250742688963\n",
      "RandomForest Regressor R2_Score for Test Data=> 0.4779318130092063\n",
      "RandomForest Regressor RMSE for Train data=> 6.474745381153222\n",
      "RandomForest Regressor RMSE for Test data=> 7.136793391667345\n"
     ]
    }
   ],
   "source": [
    "#Implementing Linear regression and RandomForest Regression models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import metrics\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_scaled,Y_train)\n",
    "regressor_rf = RandomForestRegressor(n_estimators = 100, random_state = 0,max_depth=4)\n",
    "regressor_rf.fit(X_train,Y_train)\n",
    "\n",
    "#Predicting the values using the implemented models\n",
    "y_train_pred_lr=model.predict(X_train_scaled)\n",
    "y_test_pred_lr=model.predict(X_test_scaled)\n",
    "y_train_pred_RFR=regressor_rf.predict(X_train)\n",
    "y_test_pred_RFR=regressor_rf.predict(X_test)\n",
    "\n",
    "#Evaluating the performance of the two regression models\n",
    "print('Linear Regression R2_Score for Training Data=>', model.score(X_train_scaled,Y_train))\n",
    "print('Linear Regression R2_Score for Test Data=>',model.score(X_test_scaled,Y_test))\n",
    "print('Linear Regression RMSE for Train data=>',np.sqrt(mean_squared_error(Y_train,y_train_pred_lr)))\n",
    "print('Linear Regression RMSE for Test data=>',np.sqrt(mean_squared_error(Y_test,y_test_pred_lr)))\n",
    "print('')\n",
    "print('RandomForest Regressor R2_Score for Training Data=>', regressor_rf.score(X_train,Y_train))\n",
    "print('RandomForest Regressor R2_Score for Test Data=>',regressor_rf.score(X_test,Y_test))\n",
    "print('RandomForest Regressor RMSE for Train data=>',np.sqrt(mean_squared_error(Y_train,y_train_pred_RFR)))\n",
    "print('RandomForest Regressor RMSE for Test data=>',np.sqrt(mean_squared_error(Y_test,y_test_pred_RFR)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'n_estimators': 75}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using GridSearchCV to tune the hyperparameters for Random Forest regressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "gsc = GridSearchCV(\n",
    "        estimator=RandomForestRegressor(),\n",
    "        param_grid={\n",
    "            'max_depth': range(3,9),\n",
    "            'n_estimators': (10, 50,75, 100, 1000),\n",
    "        },\n",
    "        cv=5, scoring='neg_mean_squared_error', verbose=0,n_jobs=-1)\n",
    "    \n",
    "grid_result = gsc.fit(X_data, Y_data)\n",
    "best_params = grid_result.best_params_\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Mean k-Fold R Squared: 0.3861602570296035\n",
      "Linear Regression Mean k-Fold RMSE: 7.829092274965636\n",
      "RandomForest Regressor Mean k-Fold R Squared: 0.45170522337278635\n",
      "RandomForest Regressor Mean k-Fold RMSE: 7.3939757719844454\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Implementing k-fold cross-validation\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = LinearRegression()\n",
    "regressor_rf = RandomForestRegressor(n_estimators = 100,random_state = 0,max_depth=4)\n",
    "\n",
    "folds = KFold(n_splits = 10, shuffle = True,random_state=0)\n",
    "cv_r2_scores_lr = cross_val_score(model, X_data, Y_data, cv=folds,scoring='r2')\n",
    "print(\"Linear Regression Mean k-Fold R Squared: {}\".format(np.mean(cv_r2_scores_lr)))\n",
    "scores = cross_val_score(model, X_data, Y_data, scoring='neg_mean_squared_error', cv=folds)\n",
    "print(\"Linear Regression Mean k-Fold RMSE: {}\".format(np.sqrt(abs(np.mean(scores)))))\n",
    "\n",
    "cv_r2_scores_rf = cross_val_score(regressor_rf, X_data, Y_data, cv=folds,scoring='r2')\n",
    "print(\"RandomForest Regressor Mean k-Fold R Squared: {}\".format(np.mean(cv_r2_scores_rf)))\n",
    "scores = cross_val_score(regressor_rf, X_data, Y_data, scoring='neg_mean_squared_error', cv=folds)\n",
    "print(\"RandomForest Regressor Mean k-Fold RMSE: {}\".format(np.sqrt(abs(np.mean(scores)))))\n",
    "\n",
    "#Plotting the R2_score vs Iterations graph for the two algorithms\n",
    "plt.plot(range(10), cv_r2_scores_lr, label =\"Linear regression\")\n",
    "plt.plot(range(10), cv_r2_scores_rf, label =\"RandomForest regression\")\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"R2_Score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the graph of actual rating to the predicted rating for each algorithm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(Y_test, y_test_pred_lr, 'o', markersize = 7 )\n",
    "plt.title('Linear Regression')\n",
    "plt.xlabel('Actual Rating')\n",
    "plt.ylabel('Predicted Rating')\n",
    "plt.plot(np.unique(Y_test), np.poly1d(np.polyfit(Y_test, y_test_pred_lr, 1))(np.unique(Y_test)), color = 'green')\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(Y_test, y_test_pred_RFR, 'o', markersize = 7 )\n",
    "plt.title('RandomForest Regressor')\n",
    "plt.xlabel('Actual Rating')\n",
    "plt.ylabel('Predicted Rating')\n",
    "plt.plot(np.unique(Y_test), np.poly1d(np.polyfit(Y_test, y_test_pred_RFR, 1))(np.unique(Y_test)), color = 'green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using backward elimination to find significance of the variables\n",
    "1)Select a Significance level for the model, usually SL=0.05. \n",
    "2)Fit the model with all variables.\n",
    "3)Consider Predictor with highest p-value. If p-value>SL, go to step4, else our model is ready.\n",
    "4)Remove the Predictor and fit the model without this variable, repeat.\n",
    "However, even this step didn’t help me much to increase the performance of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.append (arr=np.ones([X_train.shape[0],1]).astype(int), values = X_train, axis = 1)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.424\n",
      "Model:                            OLS   Adj. R-squared:                  0.414\n",
      "Method:                 Least Squares   F-statistic:                     39.88\n",
      "Date:                Sat, 26 Dec 2020   Prob (F-statistic):           2.51e-64\n",
      "Time:                        23:51:03   Log-Likelihood:                -2102.2\n",
      "No. Observations:                 607   AIC:                             4228.\n",
      "Df Residuals:                     595   BIC:                             4281.\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        470.8343    371.672      1.267      0.206    -259.115    1200.783\n",
      "x1             6.6659      1.039      6.417      0.000       4.626       8.706\n",
      "x2             0.4873      0.487      1.001      0.317      -0.469       1.444\n",
      "x3           -12.8543      2.258     -5.693      0.000     -17.289      -8.420\n",
      "x4            -0.3239      0.915     -0.354      0.724      -2.122       1.474\n",
      "x5             0.0235      0.045      0.518      0.605      -0.066       0.113\n",
      "x6            -0.0419      0.010     -4.015      0.000      -0.062      -0.021\n",
      "x7             0.0630      0.043      1.455      0.146      -0.022       0.148\n",
      "x8          -426.8387    392.713     -1.087      0.278   -1198.112     344.434\n",
      "x9            -7.0195      3.564     -1.970      0.049     -14.018      -0.021\n",
      "x10            0.6536      0.098      6.681      0.000       0.461       0.846\n",
      "x11           -0.1072      0.046     -2.344      0.019      -0.197      -0.017\n",
      "==============================================================================\n",
      "Omnibus:                        6.384   Durbin-Watson:                   1.939\n",
      "Prob(Omnibus):                  0.041   Jarque-Bera (JB):                6.518\n",
      "Skew:                          -0.195   Prob(JB):                       0.0384\n",
      "Kurtosis:                       3.325   Cond. No.                     1.46e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.46e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X_opt = [0,1,2,3,4,5,6,7,8,9,10,11]\n",
    "regressor = sm.OLS(Y_train, X_train[:,X_opt]).fit()\n",
    "print(regressor.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.424\n",
      "Model:                            OLS   Adj. R-squared:                  0.415\n",
      "Method:                 Least Squares   F-statistic:                     43.92\n",
      "Date:                Sat, 26 Dec 2020   Prob (F-statistic):           3.92e-65\n",
      "Time:                        23:51:12   Log-Likelihood:                -2102.3\n",
      "No. Observations:                 607   AIC:                             4227.\n",
      "Df Residuals:                     596   BIC:                             4275.\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        475.5383    371.162      1.281      0.201    -253.406    1204.482\n",
      "x1             6.5844      1.012      6.505      0.000       4.597       8.572\n",
      "x2             0.4235      0.452      0.937      0.349      -0.464       1.311\n",
      "x3           -12.4215      1.897     -6.549      0.000     -16.147      -8.696\n",
      "x4             0.0232      0.045      0.512      0.609      -0.066       0.112\n",
      "x5            -0.0432      0.010     -4.436      0.000      -0.062      -0.024\n",
      "x6             0.0634      0.043      1.465      0.144      -0.022       0.148\n",
      "x7          -431.3882    392.215     -1.100      0.272   -1201.679     338.903\n",
      "x8            -6.9498      3.555     -1.955      0.051     -13.933       0.033\n",
      "x9             0.6535      0.098      6.684      0.000       0.461       0.845\n",
      "x10           -0.1100      0.045     -2.444      0.015      -0.198      -0.022\n",
      "==============================================================================\n",
      "Omnibus:                        6.530   Durbin-Watson:                   1.937\n",
      "Prob(Omnibus):                  0.038   Jarque-Bera (JB):                6.702\n",
      "Skew:                          -0.196   Prob(JB):                       0.0351\n",
      "Kurtosis:                       3.334   Cond. No.                     1.46e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.46e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X_opt = [0,1,2,3,5,6,7,8,9,10,11]\n",
    "regressor = sm.OLS(Y_train, X_train[:,X_opt]).fit()\n",
    "print(regressor.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.424\n",
      "Model:                            OLS   Adj. R-squared:                  0.415\n",
      "Method:                 Least Squares   F-statistic:                     48.83\n",
      "Date:                Sat, 26 Dec 2020   Prob (F-statistic):           6.19e-66\n",
      "Time:                        23:51:21   Log-Likelihood:                -2102.4\n",
      "No. Observations:                 607   AIC:                             4225.\n",
      "Df Residuals:                     597   BIC:                             4269.\n",
      "Df Model:                           9                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        368.3440    306.154      1.203      0.229    -232.927     969.615\n",
      "x1             6.8410      0.879      7.787      0.000       5.116       8.566\n",
      "x2             0.3298      0.413      0.799      0.425      -0.481       1.141\n",
      "x3           -12.4791      1.892     -6.595      0.000     -16.195      -8.763\n",
      "x4            -0.0431      0.010     -4.430      0.000      -0.062      -0.024\n",
      "x5             0.0650      0.043      1.508      0.132      -0.020       0.150\n",
      "x6          -318.1407    323.556     -0.983      0.326    -953.588     317.306\n",
      "x7            -7.5245      3.371     -2.232      0.026     -14.145      -0.904\n",
      "x8             0.6442      0.096      6.710      0.000       0.456       0.833\n",
      "x9            -0.1091      0.045     -2.428      0.015      -0.197      -0.021\n",
      "==============================================================================\n",
      "Omnibus:                        6.004   Durbin-Watson:                   1.936\n",
      "Prob(Omnibus):                  0.050   Jarque-Bera (JB):                6.090\n",
      "Skew:                          -0.189   Prob(JB):                       0.0476\n",
      "Kurtosis:                       3.314   Cond. No.                     1.18e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.18e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X_opt = [0,1,2,3,6,7,8,9,10,11]\n",
    "regressor = sm.OLS(Y_train, X_train[:,X_opt]).fit()\n",
    "print(regressor.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.423\n",
      "Model:                            OLS   Adj. R-squared:                  0.416\n",
      "Method:                 Least Squares   F-statistic:                     54.89\n",
      "Date:                Sat, 26 Dec 2020   Prob (F-statistic):           1.11e-66\n",
      "Time:                        23:52:13   Log-Likelihood:                -2102.7\n",
      "No. Observations:                 607   AIC:                             4223.\n",
      "Df Residuals:                     598   BIC:                             4263.\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        180.3507    195.653      0.922      0.357    -203.900     564.602\n",
      "x1             7.1943      0.759      9.482      0.000       5.704       8.684\n",
      "x2           -12.7076      1.870     -6.796      0.000     -16.380      -9.035\n",
      "x3            -0.0447      0.010     -4.696      0.000      -0.063      -0.026\n",
      "x4             0.0644      0.043      1.494      0.136      -0.020       0.149\n",
      "x5          -113.9835    198.234     -0.575      0.566    -503.302     275.335\n",
      "x6            -9.5199      2.262     -4.208      0.000     -13.963      -5.077\n",
      "x7             0.6409      0.096      6.683      0.000       0.453       0.829\n",
      "x8            -0.1154      0.044     -2.608      0.009      -0.202      -0.029\n",
      "==============================================================================\n",
      "Omnibus:                        6.010   Durbin-Watson:                   1.934\n",
      "Prob(Omnibus):                  0.050   Jarque-Bera (JB):                6.049\n",
      "Skew:                          -0.193   Prob(JB):                       0.0486\n",
      "Kurtosis:                       3.301   Cond. No.                     7.37e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 7.37e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X_opt = [0,1,3,6,7,8,9,10,11]\n",
    "regressor = sm.OLS(Y_train, X_train[:,X_opt]).fit()\n",
    "print(regressor.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.423\n",
      "Model:                            OLS   Adj. R-squared:                  0.416\n",
      "Method:                 Least Squares   F-statistic:                     62.76\n",
      "Date:                Sat, 26 Dec 2020   Prob (F-statistic):           1.58e-67\n",
      "Time:                        23:52:34   Log-Likelihood:                -2102.9\n",
      "No. Observations:                 607   AIC:                             4222.\n",
      "Df Residuals:                     599   BIC:                             4257.\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         67.9557      8.439      8.052      0.000      51.382      84.530\n",
      "x1             7.3854      0.682     10.833      0.000       6.046       8.724\n",
      "x2           -12.7555      1.867     -6.832      0.000     -16.422      -9.089\n",
      "x3            -0.0445      0.010     -4.678      0.000      -0.063      -0.026\n",
      "x4             0.0660      0.043      1.536      0.125      -0.018       0.150\n",
      "x5            -9.1674      2.176     -4.212      0.000     -13.441      -4.893\n",
      "x6             0.6321      0.095      6.681      0.000       0.446       0.818\n",
      "x7            -0.1157      0.044     -2.617      0.009      -0.203      -0.029\n",
      "==============================================================================\n",
      "Omnibus:                        5.828   Durbin-Watson:                   1.936\n",
      "Prob(Omnibus):                  0.054   Jarque-Bera (JB):                5.884\n",
      "Skew:                          -0.186   Prob(JB):                       0.0527\n",
      "Kurtosis:                       3.306   Cond. No.                     2.29e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.29e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X_opt = [0,1,3,6,7,9,10,11]\n",
    "regressor = sm.OLS(Y_train, X_train[:,X_opt]).fit()\n",
    "print(regressor.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.421\n",
      "Model:                            OLS   Adj. R-squared:                  0.415\n",
      "Method:                 Least Squares   F-statistic:                     72.66\n",
      "Date:                Sat, 26 Dec 2020   Prob (F-statistic):           5.68e-68\n",
      "Time:                        23:52:48   Log-Likelihood:                -2104.1\n",
      "No. Observations:                 607   AIC:                             4222.\n",
      "Df Residuals:                     600   BIC:                             4253.\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         67.7741      8.448      8.023      0.000      51.183      84.365\n",
      "x1             7.3190      0.681     10.746      0.000       5.981       8.657\n",
      "x2           -13.0656      1.858     -7.031      0.000     -16.715      -9.416\n",
      "x3            -0.0439      0.010     -4.615      0.000      -0.063      -0.025\n",
      "x4            -8.8401      2.168     -4.077      0.000     -13.098      -4.582\n",
      "x5             0.6302      0.095      6.654      0.000       0.444       0.816\n",
      "x6            -0.0689      0.032     -2.150      0.032      -0.132      -0.006\n",
      "==============================================================================\n",
      "Omnibus:                        5.215   Durbin-Watson:                   1.924\n",
      "Prob(Omnibus):                  0.074   Jarque-Bera (JB):                5.174\n",
      "Skew:                          -0.179   Prob(JB):                       0.0752\n",
      "Kurtosis:                       3.277   Cond. No.                     2.25e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.25e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X_opt = [0,1,3,6,9,10,11]\n",
    "regressor = sm.OLS(Y_train, X_train[:,X_opt]).fit()\n",
    "print(regressor.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(607, 6)\n"
     ]
    }
   ],
   "source": [
    "#Splitting the data into training and test sets using only significant features determined from the previous step\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_data[:,[0,2,5,8,9,10]], Y_data, train_size = 2/3, random_state = 0)\n",
    "print(X_train.shape)\n",
    "#Feature Scaling\n",
    "sc = StandardScaler()\n",
    "X_train_scaled = sc.fit_transform(X_train)\n",
    "X_test_scaled = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression R2_Score for Training Data=> 0.4208172989596938\n",
      "Linear Regression R2_Score for Test Data=> 0.42457202283128104\n",
      "Linear Regression RMSE for Train data=> 7.747912072905833\n",
      "Linear Regression RMSE for Test data=> 7.492642235969864\n",
      "\n",
      "RandomForest Regressor R2_Score for Training Data=> 0.5879353557915552\n",
      "RandomForest Regressor R2_Score for Test Data=> 0.48071168722453184\n",
      "RandomForest Regressor RMSE for Train data=> 6.535210322266394\n",
      "RandomForest Regressor RMSE for Test data=> 7.117767267457879\n"
     ]
    }
   ],
   "source": [
    "#Repeating the above steps again\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_scaled,Y_train)\n",
    "print('Linear Regression R2_Score for Training Data=>', model.score(X_train_scaled,Y_train))\n",
    "print('Linear Regression R2_Score for Test Data=>',model.score(X_test_scaled,Y_test))\n",
    "y_train_pred_lr=model.predict(X_train_scaled)\n",
    "y_test_pred_lr=model.predict(X_test_scaled)\n",
    "print('Linear Regression RMSE for Train data=>',np.sqrt(mean_squared_error(Y_train,y_train_pred_lr)))\n",
    "print('Linear Regression RMSE for Test data=>',np.sqrt(mean_squared_error(Y_test,y_test_pred_lr)))\n",
    "print('')\n",
    "\n",
    "regressor_rf = RandomForestRegressor(n_estimators = 100, random_state = 0,max_depth=4)\n",
    "regressor_rf.fit(X_train,Y_train)\n",
    "print('RandomForest Regressor R2_Score for Training Data=>', regressor_rf.score(X_train,Y_train))\n",
    "print('RandomForest Regressor R2_Score for Test Data=>',regressor_rf.score(X_test,Y_test))\n",
    "y_train_pred_RFR=regressor_rf.predict(X_train)\n",
    "y_test_pred_RFR=regressor_rf.predict(X_test)\n",
    "print('RandomForest Regressor RMSE for Train data=>',np.sqrt(mean_squared_error(Y_train,y_train_pred_RFR)))\n",
    "print('RandomForest Regressor RMSE for Test data=>',np.sqrt(mean_squared_error(Y_test,y_test_pred_RFR)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean 5-Fold R Squared: 0.3991417464933789\n",
      "Mean 5-Fold RMSE: 7.829092274965636\n",
      "Mean 5-Fold R Squared: 0.4533098533654839\n",
      "Mean 5-Fold RMSE: 7.3939757719844454\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "\n",
    "model = LinearRegression()\n",
    "regressor_rf = RandomForestRegressor(n_estimators = 100,random_state = 0,max_depth=4)\n",
    "\n",
    "folds = KFold(n_splits = 10, shuffle = True,random_state=0)\n",
    "cv_r2_scores_lr = cross_val_score(model, X_data[:,[0,2,5,8,9,10]], Y_data, cv=folds,scoring='r2')\n",
    "print(\"Mean 5-Fold R Squared: {}\".format(np.mean(cv_r2_scores_lr)))\n",
    "scores = cross_val_score(model, X_data, Y_data, scoring='neg_mean_squared_error', cv=folds)\n",
    "print(\"Mean 5-Fold RMSE: {}\".format(np.sqrt(abs(np.mean(scores)))))\n",
    "\n",
    "cv_r2_scores_rf = cross_val_score(regressor_rf, X_data[:,[0,2,5,8,9,10]], Y_data, cv=folds,scoring='r2')\n",
    "print(\"Mean 5-Fold R Squared: {}\".format(np.mean(cv_r2_scores_rf)))\n",
    "scores = cross_val_score(regressor_rf, X_data, Y_data, scoring='neg_mean_squared_error', cv=folds)\n",
    "print(\"Mean 5-Fold RMSE: {}\".format(np.sqrt(abs(np.mean(scores)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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