Q3_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38be0346",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression,Lasso,Ridge,ElasticNet\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error,r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder,OrdinalEncoder,MinMaxScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score,KFold\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "488fed3e",
   "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>Open Date</th>\n",
       "      <th>City</th>\n",
       "      <th>City Group</th>\n",
       "      <th>Type</th>\n",
       "      <th>P1</th>\n",
       "      <th>P2</th>\n",
       "      <th>P3</th>\n",
       "      <th>P4</th>\n",
       "      <th>P5</th>\n",
       "      <th>P6</th>\n",
       "      <th>...</th>\n",
       "      <th>P29</th>\n",
       "      <th>P30</th>\n",
       "      <th>P31</th>\n",
       "      <th>P32</th>\n",
       "      <th>P33</th>\n",
       "      <th>P34</th>\n",
       "      <th>P35</th>\n",
       "      <th>P36</th>\n",
       "      <th>P37</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7/17/1999</td>\n",
       "      <td>İstanbul</td>\n",
       "      <td>Big Cities</td>\n",
       "      <td>IL</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5653753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/14/2008</td>\n",
       "      <td>Ankara</td>\n",
       "      <td>Big Cities</td>\n",
       "      <td>FC</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6923131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/9/2013</td>\n",
       "      <td>Diyarbakır</td>\n",
       "      <td>Other</td>\n",
       "      <td>IL</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2055379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/2/2012</td>\n",
       "      <td>Tokat</td>\n",
       "      <td>Other</td>\n",
       "      <td>IL</td>\n",
       "      <td>6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.5</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2675511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/9/2009</td>\n",
       "      <td>Gaziantep</td>\n",
       "      <td>Other</td>\n",
       "      <td>IL</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4316715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Open Date        City  City Group Type  P1   P2   P3   P4  P5   P6  ...  \\\n",
       "Id                                                                      ...   \n",
       "0   7/17/1999    İstanbul  Big Cities   IL   4  5.0  4.0  4.0   2  2.0  ...   \n",
       "1   2/14/2008      Ankara  Big Cities   FC   4  5.0  4.0  4.0   1  2.0  ...   \n",
       "2    3/9/2013  Diyarbakır       Other   IL   2  4.0  2.0  5.0   2  3.0  ...   \n",
       "3    2/2/2012       Tokat       Other   IL   6  4.5  6.0  6.0   4  4.0  ...   \n",
       "4    5/9/2009   Gaziantep       Other   IL   3  4.0  3.0  4.0   2  2.0  ...   \n",
       "\n",
       "    P29  P30  P31  P32  P33  P34  P35  P36  P37  revenue  \n",
       "Id                                                        \n",
       "0   3.0    5    3    4    5    5    4    3    4  5653753  \n",
       "1   3.0    0    0    0    0    0    0    0    0  6923131  \n",
       "2   3.0    0    0    0    0    0    0    0    0  2055379  \n",
       "3   7.5   25   12   10    6   18   12   12    6  2675511  \n",
       "4   3.0    5    1    3    2    3    4    3    3  4316715  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating dataframe as df and reading it \n",
    "df=pd.read_csv('https://raw.githubusercontent.com/edyoda/data-science-complete-tutorial/master/Data/Shopping_Revenue.csv')\n",
    "df.set_index('Id',inplace=True)\n",
    "\n",
    "# Getting top 5 value of data frame \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "040c5655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 137 entries, 0 to 136\n",
      "Data columns (total 42 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Open Date   137 non-null    object \n",
      " 1   City        137 non-null    object \n",
      " 2   City Group  137 non-null    object \n",
      " 3   Type        137 non-null    object \n",
      " 4   P1          137 non-null    int64  \n",
      " 5   P2          137 non-null    float64\n",
      " 6   P3          137 non-null    float64\n",
      " 7   P4          137 non-null    float64\n",
      " 8   P5          137 non-null    int64  \n",
      " 9   P6          133 non-null    float64\n",
      " 10  P7          133 non-null    float64\n",
      " 11  P8          137 non-null    int64  \n",
      " 12  P9          137 non-null    int64  \n",
      " 13  P10         137 non-null    int64  \n",
      " 14  P11         137 non-null    int64  \n",
      " 15  P12         137 non-null    int64  \n",
      " 16  P13         137 non-null    float64\n",
      " 17  P14         137 non-null    int64  \n",
      " 18  P15         137 non-null    int64  \n",
      " 19  P16         137 non-null    int64  \n",
      " 20  P17         137 non-null    int64  \n",
      " 21  P18         137 non-null    int64  \n",
      " 22  P19         137 non-null    int64  \n",
      " 23  P20         137 non-null    int64  \n",
      " 24  P21         137 non-null    int64  \n",
      " 25  P22         137 non-null    int64  \n",
      " 26  P23         137 non-null    int64  \n",
      " 27  P24         137 non-null    int64  \n",
      " 28  P25         137 non-null    int64  \n",
      " 29  P26         137 non-null    float64\n",
      " 30  P27         137 non-null    float64\n",
      " 31  P28         137 non-null    float64\n",
      " 32  P29         137 non-null    float64\n",
      " 33  P30         137 non-null    int64  \n",
      " 34  P31         137 non-null    int64  \n",
      " 35  P32         137 non-null    int64  \n",
      " 36  P33         137 non-null    int64  \n",
      " 37  P34         137 non-null    int64  \n",
      " 38  P35         137 non-null    int64  \n",
      " 39  P36         137 non-null    int64  \n",
      " 40  P37         137 non-null    int64  \n",
      " 41  revenue     137 non-null    int64  \n",
      "dtypes: float64(10), int64(28), object(4)\n",
      "memory usage: 46.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#Getting idea about the data we have like column name ,  what is the data types , how many null and not null present\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c9e4f39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 137 entries, 0 to 136\n",
      "Data columns (total 42 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   City          137 non-null    object \n",
      " 1   City Group    137 non-null    object \n",
      " 2   Type          137 non-null    object \n",
      " 3   P1            137 non-null    int64  \n",
      " 4   P2            137 non-null    float64\n",
      " 5   P3            137 non-null    float64\n",
      " 6   P4            137 non-null    float64\n",
      " 7   P5            137 non-null    int64  \n",
      " 8   P6            133 non-null    float64\n",
      " 9   P7            133 non-null    float64\n",
      " 10  P8            137 non-null    int64  \n",
      " 11  P9            137 non-null    int64  \n",
      " 12  P10           137 non-null    int64  \n",
      " 13  P11           137 non-null    int64  \n",
      " 14  P12           137 non-null    int64  \n",
      " 15  P13           137 non-null    float64\n",
      " 16  P14           137 non-null    int64  \n",
      " 17  P15           137 non-null    int64  \n",
      " 18  P16           137 non-null    int64  \n",
      " 19  P17           137 non-null    int64  \n",
      " 20  P18           137 non-null    int64  \n",
      " 21  P19           137 non-null    int64  \n",
      " 22  P20           137 non-null    int64  \n",
      " 23  P21           137 non-null    int64  \n",
      " 24  P22           137 non-null    int64  \n",
      " 25  P23           137 non-null    int64  \n",
      " 26  P24           137 non-null    int64  \n",
      " 27  P25           137 non-null    int64  \n",
      " 28  P26           137 non-null    float64\n",
      " 29  P27           137 non-null    float64\n",
      " 30  P28           137 non-null    float64\n",
      " 31  P29           137 non-null    float64\n",
      " 32  P30           137 non-null    int64  \n",
      " 33  P31           137 non-null    int64  \n",
      " 34  P32           137 non-null    int64  \n",
      " 35  P33           137 non-null    int64  \n",
      " 36  P34           137 non-null    int64  \n",
      " 37  P35           137 non-null    int64  \n",
      " 38  P36           137 non-null    int64  \n",
      " 39  P37           137 non-null    int64  \n",
      " 40  revenue       137 non-null    int64  \n",
      " 41  Age in years  137 non-null    int64  \n",
      "dtypes: float64(10), int64(29), object(3)\n",
      "memory usage: 46.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df['Age in years']=2022 - df['Open Date'].str.split('/',expand=True)[2].apply(int)\n",
    "#Dropping column open data\n",
    "df.drop('Open Date',axis=1,inplace=True)\n",
    "\n",
    "# viewing the data again \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ffd2b21c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to get mean of null values\n",
    "df.isna().mean()\n",
    "\n",
    "# instead of removing rows with missing values we remove the two columns \n",
    "\n",
    "df.drop(['P6','P7'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9f3f955",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
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       "      <th>P1</th>\n",
       "      <td>137.0</td>\n",
       "      <td>4.014599e+00</td>\n",
       "      <td>2.910391e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P2</th>\n",
       "      <td>137.0</td>\n",
       "      <td>4.408759e+00</td>\n",
       "      <td>1.514900e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P3</th>\n",
       "      <td>137.0</td>\n",
       "      <td>4.317518e+00</td>\n",
       "      <td>1.032337e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P4</th>\n",
       "      <td>137.0</td>\n",
       "      <td>4.372263e+00</td>\n",
       "      <td>1.016462e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P5</th>\n",
       "      <td>137.0</td>\n",
       "      <td>2.007299e+00</td>\n",
       "      <td>1.209620e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P8</th>\n",
       "      <td>137.0</td>\n",
       "      <td>5.153285e+00</td>\n",
       "      <td>1.858567e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P9</th>\n",
       "      <td>137.0</td>\n",
       "      <td>5.445255e+00</td>\n",
       "      <td>1.834793e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P10</th>\n",
       "      <td>137.0</td>\n",
       "      <td>5.489051e+00</td>\n",
       "      <td>1.847561e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P11</th>\n",
       "      <td>137.0</td>\n",
       "      <td>3.262774e+00</td>\n",
       "      <td>1.910767e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P12</th>\n",
       "      <td>137.0</td>\n",
       "      <td>5.299270e+00</td>\n",
       "      <td>1.941668e+00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P13</th>\n",
       "      <td>137.0</td>\n",
       "      <td>5.080292e+00</td>\n",
       "      <td>1.036527e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P14</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.416058e+00</td>\n",
       "      <td>2.729583e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P15</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.386861e+00</td>\n",
       "      <td>2.398677e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P16</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.941606e+00</td>\n",
       "      <td>3.505807e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P17</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.036496e+00</td>\n",
       "      <td>2.030679e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P18</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.941606e+00</td>\n",
       "      <td>3.300549e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P19</th>\n",
       "      <td>137.0</td>\n",
       "      <td>4.905109e+00</td>\n",
       "      <td>5.604467e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P20</th>\n",
       "      <td>137.0</td>\n",
       "      <td>4.547445e+00</td>\n",
       "      <td>3.708041e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P21</th>\n",
       "      <td>137.0</td>\n",
       "      <td>2.270073e+00</td>\n",
       "      <td>2.052630e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P22</th>\n",
       "      <td>137.0</td>\n",
       "      <td>2.226277e+00</td>\n",
       "      <td>1.230690e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P23</th>\n",
       "      <td>137.0</td>\n",
       "      <td>3.423358e+00</td>\n",
       "      <td>4.559609e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P24</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.372263e+00</td>\n",
       "      <td>2.304112e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P25</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.211679e+00</td>\n",
       "      <td>2.133179e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P26</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.470803e+00</td>\n",
       "      <td>2.612024e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P27</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.145985e+00</td>\n",
       "      <td>2.067039e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P28</th>\n",
       "      <td>137.0</td>\n",
       "      <td>3.222628e+00</td>\n",
       "      <td>2.308806e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P29</th>\n",
       "      <td>137.0</td>\n",
       "      <td>3.135036e+00</td>\n",
       "      <td>1.680887e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P30</th>\n",
       "      <td>137.0</td>\n",
       "      <td>2.729927e+00</td>\n",
       "      <td>5.536647e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P31</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.941606e+00</td>\n",
       "      <td>3.512093e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P32</th>\n",
       "      <td>137.0</td>\n",
       "      <td>2.525547e+00</td>\n",
       "      <td>5.230117e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P33</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.138686e+00</td>\n",
       "      <td>1.698540e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P34</th>\n",
       "      <td>137.0</td>\n",
       "      <td>2.489051e+00</td>\n",
       "      <td>5.165093e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P35</th>\n",
       "      <td>137.0</td>\n",
       "      <td>2.029197e+00</td>\n",
       "      <td>3.436272e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P36</th>\n",
       "      <td>137.0</td>\n",
       "      <td>2.211679e+00</td>\n",
       "      <td>4.168211e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P37</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.116788e+00</td>\n",
       "      <td>1.790768e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>137.0</td>\n",
       "      <td>4.453533e+06</td>\n",
       "      <td>2.576072e+06</td>\n",
       "      <td>1149870.0</td>\n",
       "      <td>2999068.0</td>\n",
       "      <td>3939804.0</td>\n",
       "      <td>5166635.0</td>\n",
       "      <td>19696939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age in years</th>\n",
       "      <td>137.0</td>\n",
       "      <td>1.332117e+01</td>\n",
       "      <td>4.027359e+00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count          mean           std        min        25%  \\\n",
       "P1            137.0  4.014599e+00  2.910391e+00        1.0        2.0   \n",
       "P2            137.0  4.408759e+00  1.514900e+00        1.0        4.0   \n",
       "P3            137.0  4.317518e+00  1.032337e+00        0.0        4.0   \n",
       "P4            137.0  4.372263e+00  1.016462e+00        3.0        4.0   \n",
       "P5            137.0  2.007299e+00  1.209620e+00        1.0        1.0   \n",
       "P8            137.0  5.153285e+00  1.858567e+00        1.0        4.0   \n",
       "P9            137.0  5.445255e+00  1.834793e+00        4.0        4.0   \n",
       "P10           137.0  5.489051e+00  1.847561e+00        4.0        5.0   \n",
       "P11           137.0  3.262774e+00  1.910767e+00        1.0        2.0   \n",
       "P12           137.0  5.299270e+00  1.941668e+00        2.0        4.0   \n",
       "P13           137.0  5.080292e+00  1.036527e+00        3.0        5.0   \n",
       "P14           137.0  1.416058e+00  2.729583e+00        0.0        0.0   \n",
       "P15           137.0  1.386861e+00  2.398677e+00        0.0        0.0   \n",
       "P16           137.0  1.941606e+00  3.505807e+00        0.0        0.0   \n",
       "P17           137.0  1.036496e+00  2.030679e+00        0.0        0.0   \n",
       "P18           137.0  1.941606e+00  3.300549e+00        0.0        0.0   \n",
       "P19           137.0  4.905109e+00  5.604467e+00        1.0        2.0   \n",
       "P20           137.0  4.547445e+00  3.708041e+00        1.0        2.0   \n",
       "P21           137.0  2.270073e+00  2.052630e+00        1.0        1.0   \n",
       "P22           137.0  2.226277e+00  1.230690e+00        1.0        1.0   \n",
       "P23           137.0  3.423358e+00  4.559609e+00        1.0        1.0   \n",
       "P24           137.0  1.372263e+00  2.304112e+00        0.0        0.0   \n",
       "P25           137.0  1.211679e+00  2.133179e+00        0.0        0.0   \n",
       "P26           137.0  1.470803e+00  2.612024e+00        0.0        0.0   \n",
       "P27           137.0  1.145985e+00  2.067039e+00        0.0        0.0   \n",
       "P28           137.0  3.222628e+00  2.308806e+00        1.0        2.0   \n",
       "P29           137.0  3.135036e+00  1.680887e+00        0.0        2.5   \n",
       "P30           137.0  2.729927e+00  5.536647e+00        0.0        0.0   \n",
       "P31           137.0  1.941606e+00  3.512093e+00        0.0        0.0   \n",
       "P32           137.0  2.525547e+00  5.230117e+00        0.0        0.0   \n",
       "P33           137.0  1.138686e+00  1.698540e+00        0.0        0.0   \n",
       "P34           137.0  2.489051e+00  5.165093e+00        0.0        0.0   \n",
       "P35           137.0  2.029197e+00  3.436272e+00        0.0        0.0   \n",
       "P36           137.0  2.211679e+00  4.168211e+00        0.0        0.0   \n",
       "P37           137.0  1.116788e+00  1.790768e+00        0.0        0.0   \n",
       "revenue       137.0  4.453533e+06  2.576072e+06  1149870.0  2999068.0   \n",
       "Age in years  137.0  1.332117e+01  4.027359e+00        8.0       11.0   \n",
       "\n",
       "                    50%        75%         max  \n",
       "P1                  3.0        4.0        12.0  \n",
       "P2                  5.0        5.0         7.5  \n",
       "P3                  4.0        5.0         7.5  \n",
       "P4                  4.0        5.0         7.5  \n",
       "P5                  2.0        2.0         8.0  \n",
       "P8                  5.0        5.0        10.0  \n",
       "P9                  5.0        5.0        10.0  \n",
       "P10                 5.0        5.0        10.0  \n",
       "P11                 3.0        4.0        10.0  \n",
       "P12                 5.0        5.0        10.0  \n",
       "P13                 5.0        5.0         7.5  \n",
       "P14                 0.0        2.0        15.0  \n",
       "P15                 0.0        2.0        10.0  \n",
       "P16                 0.0        3.0        15.0  \n",
       "P17                 0.0        1.0        15.0  \n",
       "P18                 0.0        4.0        12.0  \n",
       "P19                 3.0        5.0        25.0  \n",
       "P20                 4.0        5.0        15.0  \n",
       "P21                 1.0        3.0        15.0  \n",
       "P22                 2.0        3.0         5.0  \n",
       "P23                 2.0        5.0        25.0  \n",
       "P24                 0.0        2.0        10.0  \n",
       "P25                 0.0        2.0        10.0  \n",
       "P26                 0.0        2.5        12.5  \n",
       "P27                 0.0        2.0        12.5  \n",
       "P28                 2.5        4.0        12.5  \n",
       "P29                 3.0        3.0         7.5  \n",
       "P30                 0.0        4.0        25.0  \n",
       "P31                 0.0        3.0        15.0  \n",
       "P32                 0.0        3.0        25.0  \n",
       "P33                 0.0        2.0         6.0  \n",
       "P34                 0.0        3.0        24.0  \n",
       "P35                 0.0        4.0        15.0  \n",
       "P36                 0.0        3.0        20.0  \n",
       "P37                 0.0        2.0         8.0  \n",
       "revenue       3939804.0  5166635.0  19696939.0  \n",
       "Age in years       12.0       15.0        26.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting values like mean median std and min and max value\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a978d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation analysis\n",
    "plt.figure(figsize = (12,10)) \n",
    "sns.heatmap(df.drop('revenue',axis=1).corr(),cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a1e31b58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of distinct cities: 34\n",
      "No of distinct city groups: 2\n",
      "No of distinct types : 3\n"
     ]
    }
   ],
   "source": [
    "#Categorical data \n",
    "print('No of distinct cities: {}' .format(df['City'].nunique()))\n",
    "print('No of distinct city groups: {}' .format(df['City Group'].nunique()))\n",
    "print('No of distinct types : {}' .format(df['Type'].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "775fe756",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping city column \n",
    "\n",
    "df.drop(['City'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0097dfcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# getting idea about revenue \n",
    "\n",
    "df.groupby(['City Group']).agg({'revenue':'mean'}).plot(kind='bar').color = \"red\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4c8b2ca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Type'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Higher revenue is observed type 'FC' followed by 'IL' and then 'DT'. \n",
    "\n",
    "df.groupby('Type').agg({'revenue':'mean'}).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c46ad2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='City Group,Type'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['City Group','Type']).agg({'revenue':'mean'}).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa4d6ce9",
   "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>FC</th>\n",
       "      <th>IL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FC  IL\n",
       "Id         \n",
       "0     0   1\n",
       "1     1   0\n",
       "2     0   1\n",
       "3     0   1\n",
       "4     0   1\n",
       "..   ..  ..\n",
       "132   1   0\n",
       "133   1   0\n",
       "134   1   0\n",
       "135   1   0\n",
       "136   1   0\n",
       "\n",
       "[137 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type = pd.get_dummies(df['Type'],drop_first=True)\n",
    "type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bf097a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id\n",
       "0      1\n",
       "1      1\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "132    0\n",
       "133    1\n",
       "134    0\n",
       "135    1\n",
       "136    1\n",
       "Name: Big Cities, Length: 137, dtype: uint8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city=pd.get_dummies(df['City Group'])['Big Cities']\n",
    "city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "815c0e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.concat([df,city,type],axis=1)\n",
    "df.drop(['Type','City Group'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "751318b1",
   "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>P1</th>\n",
       "      <th>P2</th>\n",
       "      <th>P3</th>\n",
       "      <th>P4</th>\n",
       "      <th>P5</th>\n",
       "      <th>P8</th>\n",
       "      <th>P9</th>\n",
       "      <th>P10</th>\n",
       "      <th>P11</th>\n",
       "      <th>P12</th>\n",
       "      <th>...</th>\n",
       "      <th>P33</th>\n",
       "      <th>P34</th>\n",
       "      <th>P35</th>\n",
       "      <th>P36</th>\n",
       "      <th>P37</th>\n",
       "      <th>revenue</th>\n",
       "      <th>Age in years</th>\n",
       "      <th>Big Cities</th>\n",
       "      <th>FC</th>\n",
       "      <th>IL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5653753</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6923131</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2055379</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2675511</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4316715</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    P1   P2   P3   P4  P5  P8  P9  P10  P11  P12  ...  P33  P34  P35  P36  \\\n",
       "Id                                                ...                       \n",
       "0    4  5.0  4.0  4.0   2   4   5    5    3    5  ...    5    5    4    3   \n",
       "1    4  5.0  4.0  4.0   1   5   5    5    1    5  ...    0    0    0    0   \n",
       "2    2  4.0  2.0  5.0   2   5   5    5    2    5  ...    0    0    0    0   \n",
       "3    6  4.5  6.0  6.0   4   8  10   10    8   10  ...    6   18   12   12   \n",
       "4    3  4.0  3.0  4.0   2   5   5    5    2    5  ...    2    3    4    3   \n",
       "\n",
       "    P37  revenue  Age in years  Big Cities  FC  IL  \n",
       "Id                                                  \n",
       "0     4  5653753            23           1   0   1  \n",
       "1     0  6923131            14           1   1   0  \n",
       "2     0  2055379             9           0   0   1  \n",
       "3     6  2675511            10           0   0   1  \n",
       "4     3  4316715            13           0   0   1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the first 5 data point in dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9060a30c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature training dataset shape=(109, 39)\n",
      "feature test dataset shape=(28, 39)\n",
      "output training dataset shape=(109,)\n",
      "output test dataset shape=(28,)\n"
     ]
    }
   ],
   "source": [
    "target='revenue'\n",
    "features=df.drop(target,axis=1).columns\n",
    "X=df[features]\n",
    "y=df[target]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)\n",
    "print('feature training dataset shape='+str(X_train.shape))\n",
    "print('feature test dataset shape='+str(X_test.shape))\n",
    "print('output training dataset shape='+str(y_train.shape))\n",
    "print('output test dataset shape='+str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "90e32e94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline score = 1566761.9533709285\n"
     ]
    }
   ],
   "source": [
    "#Baseline\n",
    "y_baseline=y_train.mean()\n",
    "print('Baseline score = {}'.format(mean_absolute_error(y_train,([y_baseline]*len(y_train)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060df080",
   "metadata": {},
   "source": [
    "# Model 1 - Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "16a7c25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(25, 1154609.8147644396, 2903153.029489543),\n",
       " (27, 1155534.9144407045, 2893675.6467581084),\n",
       " (26, 1155669.3875204267, 2893517.95339635),\n",
       " (28, 1156252.8691396653, 2887707.1967395446),\n",
       " (29, 1156939.0414165661, 2895282.0248783603),\n",
       " (24, 1156946.9855527985, 2888616.023268052),\n",
       " (36, 1158813.4773167626, 2931822.7487903447),\n",
       " (38, 1158989.732143982, 2953278.855171843),\n",
       " (39, 1159027.761092665, 2953257.89727657),\n",
       " (35, 1159213.2665033957, 2925249.1351496913),\n",
       " (37, 1159441.1478985476, 2944165.52758198),\n",
       " (22, 1160700.3387115202, 2821232.3129434693),\n",
       " (21, 1161064.3990561909, 2790902.563016007),\n",
       " (18, 1162562.6369550987, 2818740.639231317),\n",
       " (23, 1162667.3100792025, 2865953.2876249715),\n",
       " (33, 1165399.684730695, 2803604.5861776024),\n",
       " (34, 1165677.5563295842, 2863139.585037233),\n",
       " (30, 1166667.3877335591, 2813899.2865966153),\n",
       " (32, 1167989.9845112083, 2819855.711495951),\n",
       " (19, 1171466.9443963675, 2820938.1422271878),\n",
       " (17, 1173066.0804640178, 2802118.6442211783),\n",
       " (31, 1173336.1406441764, 2837175.74792352),\n",
       " (20, 1174340.1778197458, 2823201.952400009),\n",
       " (15, 1189888.340338629, 2767616.5194562413),\n",
       " (16, 1193534.574905615, 2806059.8045161027),\n",
       " (14, 1204962.9379336464, 2724057.391266515),\n",
       " (12, 1214985.5026157165, 2650171.6230026605),\n",
       " (13, 1216669.1725786582, 2688788.5061681974),\n",
       " (10, 1240561.6003348504, 2662359.5150908795),\n",
       " (11, 1248343.2198617866, 2695488.500506963),\n",
       " (9, 1309949.3334139313, 2463024.2311005853),\n",
       " (8, 1310628.2539512438, 2559242.2423430597),\n",
       " (7, 1376054.0887460595, 2361561.1265150155),\n",
       " (6, 1391587.2641076052, 2361991.311561801),\n",
       " (4, 1408255.768056071, 2325924.3417606754),\n",
       " (5, 1408481.07910189, 2326284.913079744),\n",
       " (3, 1452749.9510490203, 2169643.239828111),\n",
       " (2, 1513586.0083962898, 2113443.0775977434),\n",
       " (1, 1527054.0335012784, 2091646.232947892)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model 1 - Linear Regression\n",
    "m1=LinearRegression()\n",
    "\n",
    "scores=[]\n",
    "for i in range(1,40):\n",
    "    rfe = RFE(m1, n_features_to_select=i)             \n",
    "    rfe.fit(X_train, y_train)\n",
    "    cols=X_train.columns[rfe.support_]\n",
    "    X_train_temp=X_train[cols]\n",
    "    X_test_temp=X_test[cols]\n",
    "    m1.fit(X_train_temp,y_train)\n",
    "    scores.append((i,mean_absolute_error(m1.predict(X_train_temp),y_train),mean_absolute_error(m1.predict(X_test_temp),y_test)))\n",
    "scores.sort(key=lambda x: x[1])\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ac348f07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe = RFE(m1, n_features_to_select=25)             \n",
    "rfe.fit(X_train, y_train)\n",
    "cols=X_train.columns[rfe.support_]\n",
    "X_train_temp=X_train[cols]\n",
    "X_test_temp=X_test[cols]\n",
    "m1.fit(X_train_temp,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e48ccecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2903153.029489543"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(m1.predict(X_test_temp),y_test)\n",
    "\n",
    "# As we can see above, our maximum train score is 0.5077 which is really bad. So, we move on to other methods."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6c6f8c6",
   "metadata": {},
   "source": [
    "# model 2 - SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1eb464ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature training dataset shape=(109, 39)\n",
      "feature test dataset shape=(28, 39)\n",
      "output training dataset shape=(109,)\n",
      "output test dataset shape=(28,)\n"
     ]
    }
   ],
   "source": [
    "# model 2 - SVR\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)\n",
    "print('feature training dataset shape='+str(X_train.shape))\n",
    "print('feature test dataset shape='+str(X_test.shape))\n",
    "print('output training dataset shape='+str(y_train.shape))\n",
    "print('output test dataset shape='+str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "43b80257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(26, 1147701.3543269092, 2795338.4563052515),\n",
       " (28, 1147880.1316093148, 2795966.9942848315),\n",
       " (29, 1147962.9229709583, 2795834.675766428),\n",
       " (25, 1147970.5507803657, 2804903.0373253487),\n",
       " (27, 1148193.389820709, 2799568.0533120655),\n",
       " (18, 1152916.91004878, 2732572.392476713),\n",
       " (22, 1153778.0453696907, 2748058.3554873997),\n",
       " (23, 1156446.3118121591, 2788315.039114048),\n",
       " (21, 1157323.553393231, 2718959.814667585),\n",
       " (24, 1157893.3206157845, 2785813.305788517),\n",
       " (37, 1162022.4375703896, 2857112.1988943773),\n",
       " (39, 1162117.4631177464, 2864715.114175134),\n",
       " (38, 1162138.1175625988, 2864495.813152539),\n",
       " (19, 1163280.4632621196, 2748197.755863773),\n",
       " (36, 1164192.6714438116, 2842536.8381261122),\n",
       " (17, 1164663.3504264422, 2735038.432903892),\n",
       " (35, 1164981.6760928982, 2841045.6617796766),\n",
       " (16, 1165041.8226377198, 2672384.2567289877),\n",
       " (20, 1165984.8557214371, 2746480.802599147),\n",
       " (34, 1176986.8574970476, 2776385.3671537517),\n",
       " (33, 1177044.476029565, 2776400.1661985335),\n",
       " (32, 1179190.8303778106, 2716493.0915376684),\n",
       " (15, 1179484.624831138, 2681126.46369525),\n",
       " (31, 1180199.448078592, 2708709.055265946),\n",
       " (14, 1184599.1357901527, 2654070.104344245),\n",
       " (30, 1185516.0254842497, 2704555.763130084),\n",
       " (13, 1203010.2981959442, 2643753.0724127307),\n",
       " (12, 1211103.5309812203, 2623913.750227467),\n",
       " (10, 1237826.6323145782, 2640801.8047761284),\n",
       " (11, 1246532.4232154659, 2670332.823511129),\n",
       " (9, 1309905.93377385, 2400247.999981484),\n",
       " (8, 1310034.5543695684, 2481687.5284516835),\n",
       " (7, 1374848.3837522638, 2319802.050294454),\n",
       " (6, 1393966.5614533299, 2324380.4974758266),\n",
       " (4, 1408657.2414935343, 2296326.1077361116),\n",
       " (5, 1409914.563530815, 2299301.363122708),\n",
       " (3, 1451727.6615432967, 2155972.4164710776),\n",
       " (2, 1511739.9379668753, 2108537.3079210245),\n",
       " (1, 1524589.5280289277, 2086362.644479985)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2=Ridge()\n",
    "scores=[]\n",
    "for i in range(1,40):\n",
    "    rfe = RFE(m2, n_features_to_select=i)             \n",
    "    rfe.fit(X_train, y_train)\n",
    "    cols=X_train.columns[rfe.support_]\n",
    "    X_train_temp=X_train[cols]\n",
    "    X_test_temp=X_test[cols]\n",
    "    m2.fit(X_train_temp,y_train)\n",
    "    scores.append((i,mean_absolute_error(m2.predict(X_train_temp),y_train),mean_absolute_error(m2.predict(X_test_temp),y_test)))\n",
    "scores.sort(key=lambda x: x[1])\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c0f951c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe = RFE(Ridge(), n_features_to_select=26)             \n",
    "rfe.fit(X_train, y_train)\n",
    "cols=X_train.columns[rfe.support_]\n",
    "X_train_temp=X_train[cols]\n",
    "X_test_temp=X_test[cols]\n",
    "m2.fit(X_train_temp,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f6614936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2795338.4563052515"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(m2.predict(X_test_temp),y_test)\n",
    "\n",
    "# The o/p is very bad so moving to another algorithm "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a45dbc28",
   "metadata": {},
   "source": [
    "# model 3 - Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0906c972",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature training dataset shape=(109, 39)\n",
      "feature test dataset shape=(28, 39)\n",
      "output training dataset shape=(109,)\n",
      "output test dataset shape=(28,)\n"
     ]
    }
   ],
   "source": [
    "#model 3 - Decision tree\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)\n",
    "print('feature training dataset shape='+str(X_train.shape))\n",
    "print('feature test dataset shape='+str(X_test.shape))\n",
    "print('output training dataset shape='+str(y_train.shape))\n",
    "print('output test dataset shape='+str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "76b0825c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': range(1, 25)}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4=DecisionTreeRegressor(random_state=42)\n",
    "m4\n",
    "params = {'max_depth': range(1, 25)}\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d2f3e777",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeRegressor(random_state=42),\n",
       "             param_grid={'max_depth': range(1, 25)}, verbose=True)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = GridSearchCV(m4, params, \n",
    "                    cv=5, \n",
    "                   verbose=True)\n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "457a9f46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1235712.5722921556"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(tree.predict(X_train),y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cba5a010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1921042.6986434106"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(tree.predict(X_test),y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de1f561",
   "metadata": {},
   "source": [
    "# KNN regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b55ac62f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature training dataset shape=(109, 39)\n",
      "feature test dataset shape=(28, 39)\n",
      "output training dataset shape=(109,)\n",
      "output test dataset shape=(28,)\n"
     ]
    }
   ],
   "source": [
    "#m4 KNN regressor\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)\n",
    "print('feature training dataset shape='+str(X_train.shape))\n",
    "print('feature test dataset shape='+str(X_test.shape))\n",
    "print('output training dataset shape='+str(y_train.shape))\n",
    "print('output test dataset shape='+str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7f68a0fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m5=KNeighborsRegressor()\n",
    "m5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f4d34864",
   "metadata": {},
   "outputs": [],
   "source": [
    "params={'n_neighbors':range(10,21)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "84383741",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 11 candidates, totalling 55 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
       "             param_grid={'n_neighbors': range(10, 21)}, verbose=True)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NN = GridSearchCV(m5, params, \n",
    "                    cv=5, \n",
    "                   verbose=True)\n",
    "NN.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3c565cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1356847.7798165134"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(NN.predict(X_train),y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b3d91fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2090230.5678571428"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(NN.predict(X_test),y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf3097e",
   "metadata": {},
   "source": [
    "# m6 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "308e1755",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature training dataset shape=(109, 39)\n",
      "feature test dataset shape=(28, 39)\n",
      "output training dataset shape=(109,)\n",
      "output test dataset shape=(28,)\n"
     ]
    }
   ],
   "source": [
    "#m6 Random Forest\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)\n",
    "print('feature training dataset shape='+str(X_train.shape))\n",
    "print('feature test dataset shape='+str(X_test.shape))\n",
    "print('output training dataset shape='+str(y_train.shape))\n",
    "print('output test dataset shape='+str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "96cc3794",
   "metadata": {},
   "outputs": [],
   "source": [
    "m6=RandomForestRegressor()\n",
    "m6\n",
    "params={'n_estimators':range(10,100,10),'max_depth':range(10,21)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1973b5bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 99 candidates, totalling 495 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid={'max_depth': range(10, 21),\n",
       "                         'n_estimators': range(10, 100, 10)},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf= GridSearchCV(m6, params, \n",
    "                    cv=5, \n",
    "                   verbose=True)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d9652435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "660311.2329838357"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(rf.predict(X_train),y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c6c7c6d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1830957.3918367347"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(rf.predict(X_test),y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4716b2ed",
   "metadata": {},
   "source": [
    "# model 7- Gradient Descent Regresso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fea14eb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature training dataset shape=(109, 39)\n",
      "feature test dataset shape=(28, 39)\n",
      "output training dataset shape=(109,)\n",
      "output test dataset shape=(28,)\n"
     ]
    }
   ],
   "source": [
    "#model 7- Gradient Descent Regressor\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)\n",
    "print('feature training dataset shape='+str(X_train.shape))\n",
    "print('feature test dataset shape='+str(X_test.shape))\n",
    "print('output training dataset shape='+str(y_train.shape))\n",
    "print('output test dataset shape='+str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "62e914fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "m7=GradientBoostingRegressor()\n",
    "m7\n",
    "\n",
    "params={'n_estimators':range(10,100,10),'max_depth':range(10,21),'learning_rate': [0.01,0.05,0.1,0.2,0.3,0.4,0.5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b7d7204f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 693 candidates, totalling 3465 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=GradientBoostingRegressor(),\n",
       "             param_grid={'learning_rate': [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5],\n",
       "                         'max_depth': range(10, 21),\n",
       "                         'n_estimators': range(10, 100, 10)},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb= GridSearchCV(m7, params, \n",
    "                    cv=5, \n",
    "                   verbose=True)\n",
    "gb.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d4449cce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1281465.4712254717"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(gb.predict(X_train),y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d2bd2cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1883971.7675089755"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "mean_absolute_error(gb.predict(X_test),y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80586fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "BEST MODEL IS RANDOM FOREST . SO WE WILL GO WITH IT \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "811ed3fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=0.01, max_depth=20, n_estimators=20)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.best_estimator_\n",
    "best_model=GradientBoostingRegressor(learning_rate=0.01, max_depth=20, n_estimators=20)\n",
    "best_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c0aea46e",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imp=best_model.feature_importances_\n",
    "features=X_train.columns\n",
    "importances=pd.Series(feat_imp,index=features).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2673b250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10 best features\n",
    "importances.tail(10).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a34b5e74",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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