Cricket_Score_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Innings Score Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing essential libraries\n",
    "import pandas as pd\n",
    "import pickle\n",
    "\n",
    "# Loading the dataset\n",
    "df = pd.read_csv('ipl.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   mid        date                  venue               bat_team  \\\n",
       "0    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "1    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "2    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "3    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "4    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "\n",
       "                     bowl_team      batsman   bowler  runs  wickets  overs  \\\n",
       "0  Royal Challengers Bangalore   SC Ganguly  P Kumar     1        0    0.1   \n",
       "1  Royal Challengers Bangalore  BB McCullum  P Kumar     1        0    0.2   \n",
       "2  Royal Challengers Bangalore  BB McCullum  P Kumar     2        0    0.2   \n",
       "3  Royal Challengers Bangalore  BB McCullum  P Kumar     2        0    0.3   \n",
       "4  Royal Challengers Bangalore  BB McCullum  P Kumar     2        0    0.4   \n",
       "\n",
       "   runs_last_5  wickets_last_5  striker  non-striker  total  \n",
       "0            1               0        0            0    222  \n",
       "1            1               0        0            0    222  \n",
       "2            2               0        0            0    222  \n",
       "3            2               0        0            0    222  \n",
       "4            2               0        0            0    222  "
      ],
      "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>mid</th>\n      <th>date</th>\n      <th>venue</th>\n      <th>bat_team</th>\n      <th>bowl_team</th>\n      <th>batsman</th>\n      <th>bowler</th>\n      <th>runs</th>\n      <th>wickets</th>\n      <th>overs</th>\n      <th>runs_last_5</th>\n      <th>wickets_last_5</th>\n      <th>striker</th>\n      <th>non-striker</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2008-04-18</td>\n      <td>M Chinnaswamy Stadium</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>SC Ganguly</td>\n      <td>P Kumar</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2008-04-18</td>\n      <td>M Chinnaswamy Stadium</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>BB McCullum</td>\n      <td>P Kumar</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>2008-04-18</td>\n      <td>M Chinnaswamy Stadium</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>BB McCullum</td>\n      <td>P Kumar</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0.2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>2008-04-18</td>\n      <td>M Chinnaswamy Stadium</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>BB McCullum</td>\n      <td>P Kumar</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0.3</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>2008-04-18</td>\n      <td>M Chinnaswamy Stadium</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>BB McCullum</td>\n      <td>P Kumar</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0.4</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Data Cleaning ---\n",
    "# Removing unwanted columns\n",
    "columns_to_remove = ['mid', 'venue', 'batsman', 'bowler', 'striker', 'non-striker']\n",
    "df.drop(labels=columns_to_remove, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         date               bat_team                    bowl_team  runs  \\\n",
       "0  2008-04-18  Kolkata Knight Riders  Royal Challengers Bangalore     1   \n",
       "1  2008-04-18  Kolkata Knight Riders  Royal Challengers Bangalore     1   \n",
       "2  2008-04-18  Kolkata Knight Riders  Royal Challengers Bangalore     2   \n",
       "3  2008-04-18  Kolkata Knight Riders  Royal Challengers Bangalore     2   \n",
       "4  2008-04-18  Kolkata Knight Riders  Royal Challengers Bangalore     2   \n",
       "\n",
       "   wickets  overs  runs_last_5  wickets_last_5  total  \n",
       "0        0    0.1            1               0    222  \n",
       "1        0    0.2            1               0    222  \n",
       "2        0    0.2            2               0    222  \n",
       "3        0    0.3            2               0    222  \n",
       "4        0    0.4            2               0    222  "
      ],
      "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>date</th>\n      <th>bat_team</th>\n      <th>bowl_team</th>\n      <th>runs</th>\n      <th>wickets</th>\n      <th>overs</th>\n      <th>runs_last_5</th>\n      <th>wickets_last_5</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2008-04-18</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2008-04-18</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2008-04-18</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0.2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2008-04-18</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0.3</td>\n      <td>2</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2008-04-18</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0.4</td>\n      <td>2</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array(['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',\n",
       "       'Mumbai Indians', 'Deccan Chargers', 'Kings XI Punjab',\n",
       "       'Royal Challengers Bangalore', 'Delhi Daredevils',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Sunrisers Hyderabad',\n",
       "       'Rising Pune Supergiants', 'Gujarat Lions',\n",
       "       'Rising Pune Supergiant'], dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "df['bat_team'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keeping only consistent teams\n",
    "consistent_teams = ['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',\n",
    "                    'Mumbai Indians', 'Kings XI Punjab', 'Royal Challengers Bangalore',\n",
    "                    'Delhi Daredevils', 'Sunrisers Hyderabad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['bat_team'].isin(consistent_teams)) & (df['bowl_team'].isin(consistent_teams))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the first 5 overs data in every match\n",
    "df = df[df['overs']>=5.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          date               bat_team                    bowl_team  runs  \\\n",
       "32  2008-04-18  Kolkata Knight Riders  Royal Challengers Bangalore    61   \n",
       "33  2008-04-18  Kolkata Knight Riders  Royal Challengers Bangalore    61   \n",
       "34  2008-04-18  Kolkata Knight Riders  Royal Challengers Bangalore    61   \n",
       "35  2008-04-18  Kolkata Knight Riders  Royal Challengers Bangalore    61   \n",
       "36  2008-04-18  Kolkata Knight Riders  Royal Challengers Bangalore    61   \n",
       "\n",
       "    wickets  overs  runs_last_5  wickets_last_5  total  \n",
       "32        0    5.1           59               0    222  \n",
       "33        1    5.2           59               1    222  \n",
       "34        1    5.3           59               1    222  \n",
       "35        1    5.4           59               1    222  \n",
       "36        1    5.5           58               1    222  "
      ],
      "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>date</th>\n      <th>bat_team</th>\n      <th>bowl_team</th>\n      <th>runs</th>\n      <th>wickets</th>\n      <th>overs</th>\n      <th>runs_last_5</th>\n      <th>wickets_last_5</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>32</th>\n      <td>2008-04-18</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>61</td>\n      <td>0</td>\n      <td>5.1</td>\n      <td>59</td>\n      <td>0</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>2008-04-18</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.2</td>\n      <td>59</td>\n      <td>1</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>2008-04-18</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.3</td>\n      <td>59</td>\n      <td>1</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>2008-04-18</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.4</td>\n      <td>59</td>\n      <td>1</td>\n      <td>222</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>2008-04-18</td>\n      <td>Kolkata Knight Riders</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.5</td>\n      <td>58</td>\n      <td>1</td>\n      <td>222</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "['Kolkata Knight Riders' 'Chennai Super Kings' 'Rajasthan Royals'\n 'Mumbai Indians' 'Kings XI Punjab' 'Royal Challengers Bangalore'\n 'Delhi Daredevils' 'Sunrisers Hyderabad']\n['Royal Challengers Bangalore' 'Kings XI Punjab' 'Delhi Daredevils'\n 'Rajasthan Royals' 'Mumbai Indians' 'Chennai Super Kings'\n 'Kolkata Knight Riders' 'Sunrisers Hyderabad']\n"
     ]
    }
   ],
   "source": [
    "print(df['bat_team'].unique())\n",
    "print(df['bowl_team'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the column 'date' from string into datetime object\n",
    "from datetime import datetime\n",
    "df['date'] = df['date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Data Preprocessing ---\n",
    "# Converting categorical features using OneHotEncoding method\n",
    "encoded_df = pd.get_dummies(data=df, columns=['bat_team', 'bowl_team'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         date  runs  wickets  overs  runs_last_5  wickets_last_5  total  \\\n",
       "32 2008-04-18    61        0    5.1           59               0    222   \n",
       "33 2008-04-18    61        1    5.2           59               1    222   \n",
       "34 2008-04-18    61        1    5.3           59               1    222   \n",
       "35 2008-04-18    61        1    5.4           59               1    222   \n",
       "36 2008-04-18    61        1    5.5           58               1    222   \n",
       "\n",
       "    bat_team_Chennai Super Kings  bat_team_Delhi Daredevils  \\\n",
       "32                             0                          0   \n",
       "33                             0                          0   \n",
       "34                             0                          0   \n",
       "35                             0                          0   \n",
       "36                             0                          0   \n",
       "\n",
       "    bat_team_Kings XI Punjab  ...  bat_team_Royal Challengers Bangalore  \\\n",
       "32                         0  ...                                     0   \n",
       "33                         0  ...                                     0   \n",
       "34                         0  ...                                     0   \n",
       "35                         0  ...                                     0   \n",
       "36                         0  ...                                     0   \n",
       "\n",
       "    bat_team_Sunrisers Hyderabad  bowl_team_Chennai Super Kings  \\\n",
       "32                             0                              0   \n",
       "33                             0                              0   \n",
       "34                             0                              0   \n",
       "35                             0                              0   \n",
       "36                             0                              0   \n",
       "\n",
       "    bowl_team_Delhi Daredevils  bowl_team_Kings XI Punjab  \\\n",
       "32                           0                          0   \n",
       "33                           0                          0   \n",
       "34                           0                          0   \n",
       "35                           0                          0   \n",
       "36                           0                          0   \n",
       "\n",
       "    bowl_team_Kolkata Knight Riders  bowl_team_Mumbai Indians  \\\n",
       "32                                0                         0   \n",
       "33                                0                         0   \n",
       "34                                0                         0   \n",
       "35                                0                         0   \n",
       "36                                0                         0   \n",
       "\n",
       "    bowl_team_Rajasthan Royals  bowl_team_Royal Challengers Bangalore  \\\n",
       "32                           0                                      1   \n",
       "33                           0                                      1   \n",
       "34                           0                                      1   \n",
       "35                           0                                      1   \n",
       "36                           0                                      1   \n",
       "\n",
       "    bowl_team_Sunrisers Hyderabad  \n",
       "32                              0  \n",
       "33                              0  \n",
       "34                              0  \n",
       "35                              0  \n",
       "36                              0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ],
      "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>date</th>\n      <th>runs</th>\n      <th>wickets</th>\n      <th>overs</th>\n      <th>runs_last_5</th>\n      <th>wickets_last_5</th>\n      <th>total</th>\n      <th>bat_team_Chennai Super Kings</th>\n      <th>bat_team_Delhi Daredevils</th>\n      <th>bat_team_Kings XI Punjab</th>\n      <th>...</th>\n      <th>bat_team_Royal Challengers Bangalore</th>\n      <th>bat_team_Sunrisers Hyderabad</th>\n      <th>bowl_team_Chennai Super Kings</th>\n      <th>bowl_team_Delhi Daredevils</th>\n      <th>bowl_team_Kings XI Punjab</th>\n      <th>bowl_team_Kolkata Knight Riders</th>\n      <th>bowl_team_Mumbai Indians</th>\n      <th>bowl_team_Rajasthan Royals</th>\n      <th>bowl_team_Royal Challengers Bangalore</th>\n      <th>bowl_team_Sunrisers Hyderabad</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>32</th>\n      <td>2008-04-18</td>\n      <td>61</td>\n      <td>0</td>\n      <td>5.1</td>\n      <td>59</td>\n      <td>0</td>\n      <td>222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>2008-04-18</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.2</td>\n      <td>59</td>\n      <td>1</td>\n      <td>222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>2008-04-18</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.3</td>\n      <td>59</td>\n      <td>1</td>\n      <td>222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>2008-04-18</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.4</td>\n      <td>59</td>\n      <td>1</td>\n      <td>222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>2008-04-18</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.5</td>\n      <td>58</td>\n      <td>1</td>\n      <td>222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "encoded_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         date  runs  wickets  overs  runs_last_5  wickets_last_5  total  \\\n",
       "32 2008-04-18    61        0    5.1           59               0    222   \n",
       "33 2008-04-18    61        1    5.2           59               1    222   \n",
       "34 2008-04-18    61        1    5.3           59               1    222   \n",
       "35 2008-04-18    61        1    5.4           59               1    222   \n",
       "36 2008-04-18    61        1    5.5           58               1    222   \n",
       "\n",
       "    bat_team_Chennai Super Kings  bat_team_Delhi Daredevils  \\\n",
       "32                             0                          0   \n",
       "33                             0                          0   \n",
       "34                             0                          0   \n",
       "35                             0                          0   \n",
       "36                             0                          0   \n",
       "\n",
       "    bat_team_Kings XI Punjab  ...  bat_team_Royal Challengers Bangalore  \\\n",
       "32                         0  ...                                     0   \n",
       "33                         0  ...                                     0   \n",
       "34                         0  ...                                     0   \n",
       "35                         0  ...                                     0   \n",
       "36                         0  ...                                     0   \n",
       "\n",
       "    bat_team_Sunrisers Hyderabad  bowl_team_Chennai Super Kings  \\\n",
       "32                             0                              0   \n",
       "33                             0                              0   \n",
       "34                             0                              0   \n",
       "35                             0                              0   \n",
       "36                             0                              0   \n",
       "\n",
       "    bowl_team_Delhi Daredevils  bowl_team_Kings XI Punjab  \\\n",
       "32                           0                          0   \n",
       "33                           0                          0   \n",
       "34                           0                          0   \n",
       "35                           0                          0   \n",
       "36                           0                          0   \n",
       "\n",
       "    bowl_team_Kolkata Knight Riders  bowl_team_Mumbai Indians  \\\n",
       "32                                0                         0   \n",
       "33                                0                         0   \n",
       "34                                0                         0   \n",
       "35                                0                         0   \n",
       "36                                0                         0   \n",
       "\n",
       "    bowl_team_Rajasthan Royals  bowl_team_Royal Challengers Bangalore  \\\n",
       "32                           0                                      1   \n",
       "33                           0                                      1   \n",
       "34                           0                                      1   \n",
       "35                           0                                      1   \n",
       "36                           0                                      1   \n",
       "\n",
       "    bowl_team_Sunrisers Hyderabad  \n",
       "32                              0  \n",
       "33                              0  \n",
       "34                              0  \n",
       "35                              0  \n",
       "36                              0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ],
      "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>date</th>\n      <th>runs</th>\n      <th>wickets</th>\n      <th>overs</th>\n      <th>runs_last_5</th>\n      <th>wickets_last_5</th>\n      <th>total</th>\n      <th>bat_team_Chennai Super Kings</th>\n      <th>bat_team_Delhi Daredevils</th>\n      <th>bat_team_Kings XI Punjab</th>\n      <th>...</th>\n      <th>bat_team_Royal Challengers Bangalore</th>\n      <th>bat_team_Sunrisers Hyderabad</th>\n      <th>bowl_team_Chennai Super Kings</th>\n      <th>bowl_team_Delhi Daredevils</th>\n      <th>bowl_team_Kings XI Punjab</th>\n      <th>bowl_team_Kolkata Knight Riders</th>\n      <th>bowl_team_Mumbai Indians</th>\n      <th>bowl_team_Rajasthan Royals</th>\n      <th>bowl_team_Royal Challengers Bangalore</th>\n      <th>bowl_team_Sunrisers Hyderabad</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>32</th>\n      <td>2008-04-18</td>\n      <td>61</td>\n      <td>0</td>\n      <td>5.1</td>\n      <td>59</td>\n      <td>0</td>\n      <td>222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>2008-04-18</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.2</td>\n      <td>59</td>\n      <td>1</td>\n      <td>222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>2008-04-18</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.3</td>\n      <td>59</td>\n      <td>1</td>\n      <td>222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>2008-04-18</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.4</td>\n      <td>59</td>\n      <td>1</td>\n      <td>222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>2008-04-18</td>\n      <td>61</td>\n      <td>1</td>\n      <td>5.5</td>\n      <td>58</td>\n      <td>1</td>\n      <td>222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "encoded_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['date', 'runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5',\n",
       "       'total', 'bat_team_Chennai Super Kings', 'bat_team_Delhi Daredevils',\n",
       "       'bat_team_Kings XI Punjab', 'bat_team_Kolkata Knight Riders',\n",
       "       'bat_team_Mumbai Indians', 'bat_team_Rajasthan Royals',\n",
       "       'bat_team_Royal Challengers Bangalore', 'bat_team_Sunrisers Hyderabad',\n",
       "       'bowl_team_Chennai Super Kings', 'bowl_team_Delhi Daredevils',\n",
       "       'bowl_team_Kings XI Punjab', 'bowl_team_Kolkata Knight Riders',\n",
       "       'bowl_team_Mumbai Indians', 'bowl_team_Rajasthan Royals',\n",
       "       'bowl_team_Royal Challengers Bangalore',\n",
       "       'bowl_team_Sunrisers Hyderabad'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "encoded_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rearranging the columns\n",
    "encoded_df = encoded_df[['date', 'bat_team_Chennai Super Kings', 'bat_team_Delhi Daredevils', 'bat_team_Kings XI Punjab',\n",
    "              'bat_team_Kolkata Knight Riders', 'bat_team_Mumbai Indians', 'bat_team_Rajasthan Royals',\n",
    "              'bat_team_Royal Challengers Bangalore', 'bat_team_Sunrisers Hyderabad',\n",
    "              'bowl_team_Chennai Super Kings', 'bowl_team_Delhi Daredevils', 'bowl_team_Kings XI Punjab',\n",
    "              'bowl_team_Kolkata Knight Riders', 'bowl_team_Mumbai Indians', 'bowl_team_Rajasthan Royals',\n",
    "              'bowl_team_Royal Challengers Bangalore', 'bowl_team_Sunrisers Hyderabad',\n",
    "              'overs', 'runs', 'wickets', 'runs_last_5', 'wickets_last_5', 'total']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into train and test set\n",
    "X_train = encoded_df.drop(labels='total', axis=1)[encoded_df['date'].dt.year <= 2016]\n",
    "X_test = encoded_df.drop(labels='total', axis=1)[encoded_df['date'].dt.year >= 2017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = encoded_df[encoded_df['date'].dt.year <= 2016]['total'].values\n",
    "y_test = encoded_df[encoded_df['date'].dt.year >= 2017]['total'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the 'date' column\n",
    "X_train.drop(labels='date', axis=True, inplace=True)\n",
    "X_test.drop(labels='date', axis=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "# --- Model Building ---\n",
    "# Linear Regression Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_lr = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([172.07093429, 175.2197967 , 174.61607874, ..., 100.37504751,\n",
       "        99.80473879,  93.14382211])"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "predict_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "metadata": {},
     "execution_count": 23
    },
    {
     "output_type": "display_data",
     "data": {
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M04htjJkiB0d6VEVR4ijKTMYjWDtHlJh0y5uI5AD3AJ8DdgDfJpBIXg5JZMbEqKrmbrJTE5gRwVONjJUQ52FmepKdcUSJybZxPA8sBP4L+PjIADzgpyJSGargjIlFB5q7WVgwg0DnwuhR4rRz2FKykW+yZxzfV9UKVf3GSNIQkUQAVV0asuiMiTE+v3KouZuFBeluhzLlSrJS6B/y09Yz6HYo5hxNNnH8wzj7Nk1lIMYYqDvZS9+Qj4WFM9wOZcqVvtdAbu0cke6Ml6pEpIDA3FHJInIxv+8imw6khDg2Y2JOVVOgYXxRQTp7jkXXkqv56YkkeD3U2QjyiDdRG8eNBBrES4DRI7i7ga+HKCZjYtaB5m48AuUz06IucXhEKMlOpq6tx+1QzDk6Y+JQ1SeBJ0Xkf6jqc9MUkzExq6qpizm5qSTFe90OJSTKclJ5vaqFUwPDpEVRr7FYM9Glqk+r6o+AMhH5ytjj48wjZYw5B1XN3VxYkuF2GCEzOzsFBXbUtXNVuc1aHakmahxPde7TgBnj3IwxU+TUwDB1J3tZVBC9f1ql2SkIUFnb7nYo5hxMdKnqe879301POMbEroPOVCPR2BV3RFK8l4KMJLYdtcQRySbVHVdEviki6SISLyKvisgJEfl0qIMzJpaMLN4UjV1xR5uVncKOunaGfZOeL9WEmcmO47jBWYHvYwQWX5oP/EXIojImBlU1dTMjMY7izMhf9e9M5uSm0jPoY29jUIt6mjAy2cQxMpHhauAnqnoyRPEYE7P2N3WxqCg96qYaGWtObqDpdNORNpcjMWdrsonjFyJSBSwFXhWRPKA/dGEZE1v8fuVAUxcVhdHbvjFiRlI85flpbKqxxBGpJjut+lpgJbBUVYeAHmBNKAMzJpbUtvXQO+ijoij6EwfAynk5VNaeZHDY2jkiUTALGi8C/khE7gL+ALghNCEZE3v2O1ONxMIZB8DKuTn0DvrY3dDhdijmLEx2WvX/AuYBOwGfs1uBp0ITljGxZX9jF/FeYf7M6O5RNWL53BwANh5pY2lZtsvRmGBNdsz/UqBCbSJ9Y0Jif1MX5+XPICEumIsAkSs7NYELitN581ArX7iu3O1wTJAm+yndCxSEMhBjYtn+xthoGB/t2gX5bK9rp93W54g4k00cucB+EXlJRNaP3EIZmDGxorV7gJbugZhpGB/x4YX5+BXePNzqdigmSJO9VPVQKIMwJpbFWsP4iMUlmeSkJvBaVQtrlhS7HY4JwmS7474B1ALxzuOtwPaJ6onITSJyUESqRWTtOMdFRL7jHN8tIpeMOrZORFpEZO+YOg+JyDER2encVk/mPRgTrvY3xmbi8HiEq+fn8cahVnx+az6NJJOdq+pPgP8HfM/ZVQz8fII6XuARYBVQAdwmIhVjiq0Cyp3bvcCjo479J3DTaZ7+W6q6xLltmMx7MCZc7W/qoiQrmYyU+IkLR5nrK2bS0TvEFhsMGFEm28ZxP3AF0AWgqoeB/AnqLAOqVbVGVQeBZ/jgoME1wFMasBnIFJFC5zXeBGxqExP19jV2xtzZxohrF+aTmuDlhZ2NbodigjDZxDHgfPkDICJxBMZxnEkxUD9qu8HZF2yZ8TzgXNpaJyJZ4xUQkXtFpFJEKltbrfHNhKfewWHePdETcw3jI5Livdx4fgEv7m1iYNg3cQUTFiabON4Qka8DySLyEeBnwC8mqDPeTG1jk81kyoz1KIHBiEuAJuBfxiukqo+r6lJVXZqXZyuNmfBU1dyNauy1b4z28SVFdPUP88ZB+4EXKSabONYCrcAe4PPABuCvJ6jTAJSO2i4Bxp6PTqbM+6jqcVX1qaofeILAJTFjItJ7DeMxesYBcOV5uWSnJvDc9ga3QzGTNNleVX4CjeF/pqp/oKpPTGIU+VagXETmiEgCcCswduzHeuAup3fVCqBTVZvO9KQjbSCOTxIYnGhMRNrX2EV6UvSvwXEm8V4Pf3RZKS/vP079yV63wzGTcMbE4XyhPyQiJ4Aq4KCItIrI30z0xKo6DDwAvAQcAJ5V1X0icp+I3OcU2wDUANUEzh7+bNRr/wTYBCwQkQYR+axz6JsiskdEdgMfBr4czBs2JpzsOdbBhSUZUb8Gx0TuXDEbEeGpTbVuh2ImYaIBgF8i0JvqMlV9F0BE5gKPisiXVfVbZ6rsdJXdMGbfY6MeK4EeW+PVve00+++cIGZjIkL/kI+qpm7u/dBct0NxXVFmMqsuKOCZrfV88fr5pCVOdmyyccNEl6ruAm4bSRoAqloDfNo5Zow5S/ubuhj2KxeVZLodSlj4/Ifm0d0/zKO/rXY7FDOBiRJHvKqeGLtTVVv5/XKyxpizsLu+A4DFpRnuBhImLizJ4FMXF/PE7961to4wN9H54JmmrbQpLY05B7saOsmfkUhBepLboYSNv7hpAS/ubeZ/vbCXdXdfhscTaPt5ekvduOVvXz5rOsMzjokSx2IR6RpnvwD2aTfmHOxq6CAnNYGfvFM/ceEYUZiRzIOrF/I3L+zjP16vtrU6wtQZE4eqeqcrEGNiSVf/EDWtPXykYqbboYSdO1fMZmddB9965RA5aQncsXy22yGZMazrgjEu2OW0b5Rkxe74jdMREf7vpy6kvXeQv/rvvRxt66U4M5l4b2ysjhgJ7H/CGBdU1rbjESjNSnE7lLCUFO/libuWcsfyWTz+Zg3/8Vo1h1u63Q7LOCxxGOOC7XXtLChIJynergafTpzXw//55IU8+cfLGPb7+eHbtTy5sZbjXf1uhxbz7FKVMdPM51d21HXwiYuL3A4lIlw9P48vXz+fTTVtvH6whX9/7TDXLMjn2oX5p+1tBdbjKpQscRgzzQ4d7+bUwDBLZ2fTO2hTiU9GnNfDVeV5XDIriw17mnitqoXath7uXD6bRDtrm3aWOIyZZpVH2wG4dHYWvzv8gfG1Me1MZxAAqYlx3LK0lLl5qfz3jmP8cGMt91xeZpf8ppm1cRgzzbbVniRvRqL1qDoHl87O5tbLZtHQ3suzlfX4J5ys20wlSxzGTCNVZcu7J1lWlh3zM+KeqwuKM1h9YSFVzd28ccgWgZpOljiMmUZH23pp6uxnxbwct0OJCivn5nBRSQavHjhOY0ef2+HEDGvjMGYabappAwJfeLFsoraMyRIRbl5cxJHWHn6+8xj3XT0Pj53JhZydcRgzjTYdaSNvRiLz8lLdDiVqpCTE8dELC2lo72Nr7Um3w4kJljiMmSaqyuaaNlbOzbH2jSm2uCSDspwUXjvQwuCw3+1wop4lDmOmSc2JHlq6B1gR45epQkFEuPH8AroHhtl4xLo4h5olDmOmye+cnj9XnpfrciTRaXZOKgsLZvDm4Vb6h2xgZShZ4jBmmrxxqJW5uanMyrGJDUPl2oX59A/53xtkaULDEocx06B/yMemmjY+ND/P7VCiWklWCmU5qWysPsGQz9o6QsUShzHTYGvtSfqH/Fy9wBJHqH2oPJeOviE27GlyO5SoZYnDmGnwxsFWEuI8rJhjDeOhNr9gBnlpiTzxuxrUpiIJCUscxkyD16paWD4nm+QEm4wv1DwiXFmey95jXe8NuDRTK6SJQ0RuEpGDIlItImvHOS4i8h3n+G4RuWTUsXUi0iIie8fUyRaRl0XksHOfFcr3YMy5qm45Rc2JHrJSEnh6S937biY0lpRmkpuWwBNv1rgdSlQKWeIQES/wCLAKqABuE5GKMcVWAeXO7V7g0VHH/hO4aZynXgu8qqrlwKvOtjFh6zf7mwFYVJjuciSxI97r4a6VZbx+sJVqW3J2yoXyjGMZUK2qNao6CDwDrBlTZg3wlAZsBjJFpBBAVd8Exps/YA3wpPP4SeAToQjemKnym33HKc5MJiM53u1QYsody2eREOfhyY1H3Q4l6oQycRQD9aO2G5x9wZYZa6aqNgE49/njFRKRe0WkUkQqW1ttymXjjuNd/eys76CiyM42pltOWiI3Ly7iue0NdPYNuR1OVAll4hhvMp6xXRwmU+asqOrjqrpUVZfm5VkXSOOOl/YFLlNV2GUqV9xzeRm9gz5+Vlk/cWEzaaFMHA1A6ajtEqDxLMqMdXzkcpZz33KOcRoTMr/Y1cj8mWnMTE9yO5SYdEFxBpeVZfHUpqP4/NY1d6qEMnFsBcpFZI6IJAC3AuvHlFkP3OX0rloBdI5chjqD9cDdzuO7gRemMmhjpkpTZx9ba9v5+EVFbocSk0Z6rs3LS6PuZC9/t36f9WSbIiFbyElVh0XkAeAlwAusU9V9InKfc/wxYAOwGqgGeoHPjNQXkZ8A1wC5ItIA/K2q/gB4GHhWRD4L1AG3hOo9GBOs0V9Mbx0OtK3Z71x3nV+UQXpSExtr2lholwynREhXAFTVDQSSw+h9j416rMD9p6l722n2twHXTWGYxoTEroZOijKSyE1LdDuUmOb1CCvm5vCb/cc53tXvdjhRwUaOGxMCx7v6OdbRx5JZNj41HCwtyybOI2y2keRTwhKHMSGw/Wg7HgmMYDbuS0uMY3FJJtvr2q1r7hSwxGHMFPP5lR31HSwoSCctMaRXg00QVs7LYcinPLvVuuaeK0scxkyxQ8e7OTUwzKV2mSqsFGUmMyc3lR+89a6tS36OLHEYM8W217WTmhjHgoIZbodixrh6fh7NXf08v73B7VAimiUOY6ZQz8AwVU3dXFyaidcz3sQIxk3l+WlcWJzBo28cYdhWCDxrljiMmUI76zvwqXKJXaYKSyLC/R+ex9G2XjbsbXY7nIhlicOYKaKqbDvaTnFmMgUZNsVIuLqhooDz8tP47uvV+G0akrNiicOYKbK9rp3mrn6WltnZRjjzeIQ/u2YeVc3dvFZlU92dDUscxkyR/9p0lMQ4j43diAA3Ly6iNDuZf3v1kJ11nAVLHMZMgROnBtiwp5lLZmWRGGfrioe7OK+HL18/n73HuvjF7okm5DZjWeIwZgr8dGs9gz4/y+dkux2KmaRPLClmUWE6//ybgwwM+9wOJ6JY4jDmHPn8ytNb6rh8Xg75tu5GxPB4hLWrFlJ/so8fb7bp1oNh8yEYc45er2rhWEcff/3RRbT32jxIkeRD5blceV4u//7aYQCS4j94mfH25bOmO6ywZ2ccxpyjpzYfZWZ6ItdXzHQ7FBMkkcBZR3vvEG8canU7nIhhicOYc3D4eDdvHmrl9mWziffan1MkuqA4g08sKeLt6hOc7Bl0O5yIYJ90Y87BE7+rISnew50rZ7sdijkHa1ctwiPChj0TrVxtwBKHMWetpaufn+9o5JZLS8lOTXA7HHMOCjKSuGZBHvubujjc0u12OGHPGseNOUv/ubGWIb+fz101x+1QTBBGrws/2hXn5VJ5tJ1f7m7iC9em2SSVZ2BnHMachR++/S7r3n6XisJ03q5u4+ktdaf9QjKRId7r4aMXFtLaPWBLzE7AEocxZ6Gytp3+IT8fKs9zOxQzhRYWzGD+zDReOXCcUwPDbocTtixxGBOkIZ+ft4+cYHZOCqXZKW6HY6aQiPDRC4sY8vn5zT6bdv10LHEYE6TntjXQ0TvENfPtbCMa5c1I5Ip5uWw72k5De6/b4YSlkCYOEblJRA6KSLWIrB3nuIjId5zju0XkkonqishDInJMRHY6t9WhfA/GjDY47OffX6umNCuZ+TNtadho9eGF+aQmxvGLXY02e+44QpY4RMQLPAKsAiqA20SkYkyxVUC5c7sXeHSSdb+lqkuc24ZQvQdjxvrp1jqOdfRx3aKZiFivm2iVFO/lxvMLqG/v4793HHM7nLATyjOOZUC1qtao6iDwDLBmTJk1wFMasBnIFJHCSdY1Zlp19Q/xb68cZllZNuX5aW6HY0Ls4lmZlGQl8/Cvq6yhfIxQJo5ioH7UdoOzbzJlJqr7gHNpa52IjLvcmojcKyKVIlLZ2mpz0Jhz98jr1ZzsHeR/fazCzjZigEeEj19URGv3wHuTIJqAUCaO8f6yxl4sPF2ZM9V9FJgHLAGagH8Z78VV9XFVXaqqS/PyrBHTnJvqllP88K1aPnVxCReWZLgdjpkmpdkp3HJpCeveepea1lNuhxM2Qpk4GoDSUdslwNiltk5X5rR1VfW4qvpU1Q88QeCyljEh4/crX3tuN8kJXr62aoHb4Zhp9pc3LSQpzsvf/3K/26GEjVAmjq1AuYjMEZEE4FZg/Zgy64G7nN5VK4BOVW06U12nDWTEJ4G9IXwPxvCfG2vZdrSdv/lYBfkzbKGmWPPy/uNcVZ7L6wdb+ZsX9tosAYRwripVHRaRB4CXAC+wTlX3ich9zvHHgA3AaqAa6AU+c6a6zlN/U0SWELh0VQt8PlTvwZgdde1848UDXLcwn09dMraJzsSKFfNy2Frbzq92N3FeXhpxMT6FfkgnOXS6ym4Ys++xUY8VuH+ydZ39d05xmMaM63hXP/f/eDsz05P4lz9cbA3iMSzO4+FjFxXyw421vH2kjatjfPBnbKdNY07jZM8gn/7+Fjr7hnjs05eSmWLTpse68pkzWFQwg9erWujqi+0lgi1xGDNG7YkebnlsI3Une/nBPZdxQbH1ojIBqy8sxKfKr2N8Hitbj8PEjDM1aN6+fBaqygs7G/nb9fvwCDz1x8tYPjdnGiM04S4nLZGrzsvlt4daqaw9ydKybLdDcoWdcZiYp6psOtLGrY9v5ks/3cncvFReuP9KSxpmXFcvyCM9KY6HfrEPX4zOY2VnHCYmqSqNnf3saehkz7EO2nuHyJuRyN+vOZ/bl8+21d/MaSXGeVl1QSE/razn2cp6bls2y+2Qpp0lDhMzVJWmzn72HOtkz7FOTvYM4hE4Lz+NB1cv4ubFRSTFe90O00SAi0oyqDlxin966SCrLygkIyXe7ZCmlSUOE9VUlarmbn61u4mfvFNHm5Ms5uWlcc38PCoK00lJjOMPl5aOWz/WB3qZ8YkID918Ph//97f41iuHeOjm890OaVpZ4jBR58mNtRxt66Wm9RT7GrtoPTWAEEgWHyrPo6IondTE93/0LUGYYJ1flMFty2bxX5uPctuyWSwoiJ31WSxxmIg3MOxjR10Hm460sammjW1H2/H5FY9AWU4ql5+Xw/lFGaQl2sfdTK2v3rCAX+5u4u9+sY8ff255zAwStb8kE3GGfH52N/w+UVTWtjMw7EcELijK4PK5OczNS6UsJ5VEa7MwIZSdmsBXPjKfv12/j5f2NXPTBYUTV4oCljhMROjuH+LVAy38ak8Tb1efoHfQB8DCghncvnwWl8/LZdmcbDKS4+2yk5lWdyyfxdNb6vj7Xx7gmgX5MdHBwhKHCRvjfeE3tPfyu8MnONDUxbBfSU+K48LiDOblpfHnNy4gO9WmAjHuivN6+NubK7j9iS189/VqvnJD9E+9b4nDhKX23kFe3NPE3sYukuI9XDYnm4uKMyjNTsHjXEf+9d7YnvbBhI/L5+XyiSVFfPe3R7jh/IKon6bGEocJK0M+P28cauXNQ62IwPWL8rliXq61VZiwM/YM+YLiDF490MKf/2wXLzxwBYlx0fuZtSlHTNho6ern0d8e4bWqFiqK0vny9fO5duFMSxomIqQkxPHJS4qpau7mGxuq3A4npOyMw7hOVXlmaz2P/LaaeK+Hu1bOZmFButthGRO0hQXpfOaKMn74di0r5+Vw4/kFbocUEnbGYVzV2TvEn/14Ow8+v4fZ2al84bpySxomoq1dtZCLSjL4yk93crC52+1wQsISh3HNO++eZNW33+Tl/cd5cNVC7rmijPSk2Jrzx0SfxDgv37vzUlIT4/jsk1tp6ep3O6QpZ4nDTLu+QR/fePEAtz6+ifg4D8/96eV8/up57/WWMibSFWYk88RdSznZM8gd39/CyZ5Bt0OaUpY4zLR6u/oEN/7bm3zvjRpuubSUX33hKhaXZrodljFTbnFpJt+/eyl1J3u59fFNNHX2uR3SlLHEYabFweZu/vRH27jj+1vwCDz9J8v5xz+4yOaPMlHt8nm5/PCey2js6OdT393IzvoOt0OaEqIa/StYLV26VCsrK90O4wNONzXG7cuDWxhmoiVR3dLaPcDrVS08v6OBzTUnSUuM47NXziE7NYF4r/1mMdFpvL+5/Y1d/MlTlbR09/PVGxbwuSvnEBcBfwMisk1Vl47dbz/3zKSMJCefX+kf8tE/5KPPufUP+ekfDDzuHfTR0t1PZ98QTZ2BRsHS7GT+8qYF3HbZLLJSE2wuKRNzKorS+dUXruRrz+3m4RereGFnIw+uWshV5bkROaOuJY4Ip6p09w9xsmfwfbdTA8MMDPt5alMtw34l3ushwSskxnlJSfSSmhBHSoKX1MTAfWKcF58qPr+fIZ/S2TtER98gnX1DdPYN09jRR9+Qj8Fh/xnj8QjkpiWycl4OFYXpXHFeLucXpUfkH4cxZ+NMP4we+/SlvLSvmb//5QHuWvcOi0sz+aOlpXxscWFE9SgM6aUqEbkJ+DbgBb6vqg+POS7O8dVAL3CPqm4/U10RyQZ+CpQBtcAfqmr7meJw+1LVsM9PU2c/xzr6ONbeR2NHH519Q+yo66B/2IcqiIAQmDBtUWE6yfFekhM8JMd7SYr34lcN/LIf8tF2apCmrn6aOvpoaA98oY8QID05nhlJcSTGeUiM8+IR8CnvJYXBYT9J8R56B330DAzTM+jD5w98DuI8QrzXQ3pyHJnJCWQkx5OeHM/JngEnpkA8yc4tKd5LUsLIYw8JXg8icsZLZHbGYWLdsM9P5dF2Nte00dI9QFK8h2sX5rNyXi4r52YzLy8tLH5sTfulKhHxAo8AHwEagK0isl5V948qtgood27LgUeB5RPUXQu8qqoPi8haZ/troXofI1SVYb/i8ytDPr9zr/QMDNPVP+T8Mh+ivWeQxs5+GjsCCeJYex/NXf34x+Tn1AQvcV4PiXEePCL4VVECH6iG9r73LgX5xlSM8whZqQkUZSQxNy+VK8tzae0eICc1gezURDJT4ifVfjD6i11V8WvgbOF0H1b7sjdm6sR5PayYm8PyOdkcc35IvnqghQ17AhN3pifFcV5+GvPy0ijLTSVvRiJ5aYlkpyaQkhD4AZcc7yUlIY54r+ARCfz4nKZkE7IzDhFZCTykqjc62w8CqOo3RpX5HvBbVf2Js30QuIbA2cS4dUfKqGqTiBQ69c84j/HZnnH871/s50dbjuJzEsZkxXmEwswkijKSKc5MpigzmZKsZEqyUijOSqYwI4mkeO+kGseHfH56B314PUJSnGfcBjX7Ujcmst2+fBaqSt3JXjYdaWP3sU5qWk9xpLWH1u6BST+PCHhE8Lx3L3zvzkv50Py8s4rLjcbxYqB+1HYDgbOKicoUT1B3pqo2ATjJI3+8FxeRe4F7nc1TTsIJVi5w4izqceRsKjnuOIe6o5x17GEgUmOP1LjBYndLLnBiiv7mx3X1P5xT9dnj7Qxl4hjvnGnsz/bTlZlM3TNS1ceBx4OpM5aIVI6XbSOBxT79IjVusNjdEqmxh7IjcQNQOmq7BGicZJkz1T3uXKLCuW+ZwpiNMcZMIJSJYytQLiJzRCQBuBVYP6bMeuAuCVgBdDqXoc5Udz1wt/P4buCFEL4HY4wxY4TsUpWqDovIA8BLBLrUrlPVfSJyn3P8MWADga641QS6437mTHWdp34YeFZEPgvUAbeE6j1wjpe6XGaxT79IjRssdrdEZOwxMeWIMcaYqRP+k6UYY4wJK5Y4jDHGBMUSxzhEZImIbBaRnSJSKSLLRh17UESqReSgiNzoZpzjEZH/6cS2T0S+OWp/WMc9QkT+XERURHJH7Qvr2EXkn0SkSkR2i8h/i0jmqGNhHTsEpvdx4qt2ZmMISyJSKiKvi8gB5/P9RWd/toi8LCKHnfsst2M9HRHxisgOEfmlsx0xsb+PqtptzA34DbDKebyawOh0gApgF5AIzCEwzs/rdryj4v4w8AqQ6GznR0Lco+IvJdAh4iiQGymxAzcAcc7jfwT+MYJi9zpxzQUSnHgr3I7rNLEWApc4j2cAh5x/428Ca539a0f+/cPxBnw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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(y_test-predict_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "MAE: 12.118617546193295\nMSE: 251.00792310417438\nRMSE: 15.843229566732106\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predict_lr))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predict_lr))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predict_lr)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Ridge Regression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.36321e-21): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.50168e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.57167e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.54752e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.55275e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.54641e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Ridge(),\n",
       "             param_grid={'alpha': [1e-15, 1e-10, 1e-08, 0.001, 0.01, 1, 5, 10,\n",
       "                                   20, 30, 35, 40]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "ridge=Ridge()\n",
    "parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40]}\n",
    "ridge_regressor=GridSearchCV(ridge,parameters,scoring='neg_mean_squared_error',cv=5)\n",
    "ridge_regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'alpha': 40}\n-328.4152792487922\n"
     ]
    }
   ],
   "source": [
    "print(ridge_regressor.best_params_)\n",
    "print(ridge_regressor.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_rr=ridge_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "metadata": {},
     "execution_count": 29
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(y_test-predict_rr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "MAE: 12.117294527005031\nMSE: 251.03172964112676\nRMSE: 15.843980864704639\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predict_rr))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predict_rr))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predict_rr)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4520941.271631272, tolerance: 2529.9556965945626\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4476004.438981964, tolerance: 2547.0380710286627\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4615139.222871701, tolerance: 2667.8126904366463\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4290538.335985664, tolerance: 2712.348891397669\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4100652.344291282, tolerance: 2646.143766019284\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 589205.0955887791, tolerance: 2529.9556965945626\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 482835.0105109336, tolerance: 2547.0380710286627\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 253600.3806913374, tolerance: 2667.8126904366463\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273553.96067946125, tolerance: 2712.348891397669\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 328228.21182156634, tolerance: 2646.143766019284\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 589202.5896418132, tolerance: 2529.9556965945626\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 482835.7832631953, tolerance: 2547.0380710286627\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 253599.47923878953, tolerance: 2667.8126904366463\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273552.7436873028, tolerance: 2712.348891397669\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 328228.1954832338, tolerance: 2646.143766019284\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "{'alpha': 1}\n",
      "-320.82637898585256\n"
     ]
    }
   ],
   "source": [
    "lasso=Lasso()\n",
    "parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40]}\n",
    "lasso_regressor=GridSearchCV(lasso,parameters,scoring='neg_mean_squared_error',cv=5)\n",
    "\n",
    "lasso_regressor.fit(X_train,y_train)\n",
    "print(lasso_regressor.best_params_)\n",
    "print(lasso_regressor.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_lasr = lasso_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\subha\\anaconda3\\envs\\ML\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "metadata": {},
     "execution_count": 34
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(y_test-predict_lasr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "MAE: 12.214053814850246\nMSE: 262.37973664007154\nRMSE: 16.198139912967523\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "print('MAE:', metrics.mean_absolute_error(y_test,predict_lasr))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predict_lasr))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predict_lasr)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from above models linear regression is best one. So we will save that as a pickle file \n",
    "# Creating a pickle file for the classifier\n",
    "filename = 'IPL-score-lr-model.pkl'\n",
    "pickle.dump(regressor, open(filename, 'wb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python388jvsc74a57bd0ee3f911196a4973fb8814cb027efcbeb8a49ed735f0d8d24476be6ad7539dca9",
   "display_name": "Python 3.8.8 64-bit ('ML': conda)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}