T20_Score_Prediction_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ffcb0d47-a241-4185-a494-290d19764dba",
   "metadata": {},
   "source": [
    "# Final Score Prediction Model\n",
    "I have optimized the initial data to only the required variables, so now we have final csv file as T20I.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "12ae8b3d-6256-4700-bd4d-68028da0638f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89b64cd8-adc5-484b-934c-b70e43bb26b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset successfully Imported of Shape : (125063, 13)\n"
     ]
    }
   ],
   "source": [
    "#Importing dataset\n",
    "T20I_df = pd.read_csv('T20I.csv')\n",
    "print(f\"Dataset successfully Imported of Shape : {T20I_df.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb4780a-1d36-43bc-99ce-a54432669533",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e28b8470-d8c7-4ed8-9712-511cc7fbce41",
   "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>match_id</th>\n",
       "      <th>overs</th>\n",
       "      <th>wickets</th>\n",
       "      <th>runs</th>\n",
       "      <th>total</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.250630e+05</td>\n",
       "      <td>125063.000000</td>\n",
       "      <td>125063.000000</td>\n",
       "      <td>125063.000000</td>\n",
       "      <td>125063.000000</td>\n",
       "      <td>125063.000000</td>\n",
       "      <td>125063.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.310604e+05</td>\n",
       "      <td>9.716194</td>\n",
       "      <td>2.591798</td>\n",
       "      <td>70.763007</td>\n",
       "      <td>149.410977</td>\n",
       "      <td>32.129167</td>\n",
       "      <td>1.249162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.368122e+05</td>\n",
       "      <td>5.742609</td>\n",
       "      <td>2.142688</td>\n",
       "      <td>47.578876</td>\n",
       "      <td>36.780281</td>\n",
       "      <td>15.357969</td>\n",
       "      <td>1.113531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.110280e+05</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.937280e+05</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.089778e+06</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.202366e+06</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.335808e+06</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           match_id          overs        wickets           runs  \\\n",
       "count  1.250630e+05  125063.000000  125063.000000  125063.000000   \n",
       "mean   9.310604e+05       9.716194       2.591798      70.763007   \n",
       "std    3.368122e+05       5.742609       2.142688      47.578876   \n",
       "min    2.110280e+05       0.100000       0.000000       0.000000   \n",
       "25%    5.937280e+05       4.600000       1.000000      32.000000   \n",
       "50%    1.089778e+06       9.500000       2.000000      65.000000   \n",
       "75%    1.202366e+06      14.500000       4.000000     103.000000   \n",
       "max    1.335808e+06      19.600000      10.000000     263.000000   \n",
       "\n",
       "               total    runs_last_5  wickets_last_5  \n",
       "count  125063.000000  125063.000000   125063.000000  \n",
       "mean      149.410977      32.129167        1.249162  \n",
       "std        36.780281      15.357969        1.113531  \n",
       "min         2.000000       0.000000        0.000000  \n",
       "25%       125.000000      22.000000        0.000000  \n",
       "50%       149.000000      32.000000        1.000000  \n",
       "75%       173.000000      42.000000        2.000000  \n",
       "max       263.000000     104.000000        8.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T20I_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6eee246a-875c-4b29-8515-cd16e9e05c2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 125063 entries, 0 to 125062\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   match_id        125063 non-null  int64  \n",
      " 1   start_date      125063 non-null  object \n",
      " 2   venue           125063 non-null  object \n",
      " 3   overs           125063 non-null  float64\n",
      " 4   batting_team    125063 non-null  object \n",
      " 5   bowling_team    125063 non-null  object \n",
      " 6   batsman         125063 non-null  object \n",
      " 7   bowler          125063 non-null  object \n",
      " 8   wickets         125063 non-null  int64  \n",
      " 9   runs            125063 non-null  int64  \n",
      " 10  total           125063 non-null  int64  \n",
      " 11  runs_last_5     125063 non-null  int64  \n",
      " 12  wickets_last_5  125063 non-null  int64  \n",
      "dtypes: float64(1), int64(6), object(6)\n",
      "memory usage: 12.4+ MB\n"
     ]
    }
   ],
   "source": [
    "T20I_df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e82cd2c-849f-48e4-aadd-df1be5e64cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "match_id          1070\n",
       "start_date         809\n",
       "venue              193\n",
       "overs              123\n",
       "batting_team        10\n",
       "bowling_team        10\n",
       "batsman            970\n",
       "bowler             746\n",
       "wickets             11\n",
       "runs               258\n",
       "total              189\n",
       "runs_last_5        101\n",
       "wickets_last_5       9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T20I_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "64077ea8-d281-4834-b4ca-282866d58577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "match_id            int64\n",
       "start_date         object\n",
       "venue              object\n",
       "overs             float64\n",
       "batting_team       object\n",
       "bowling_team       object\n",
       "batsman            object\n",
       "bowler             object\n",
       "wickets             int64\n",
       "runs                int64\n",
       "total               int64\n",
       "runs_last_5         int64\n",
       "wickets_last_5      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T20I_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5775a4f4-06b2-4185-8d04-68ba7cc27cf9",
   "metadata": {},
   "source": [
    "# Plot Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ba563ca-70d8-459b-928c-b4afcbfa099c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(T20I_df['wickets'],kde=False,bins=10)\n",
    "plt.title(\"Wickets Distribution\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e471656d-0f27-4478-8cd4-b6bbaca9b986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(T20I_df['total'],kde=False,bins=10)\n",
    "plt.title(\"Total score Distribution\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8d3d8835-c9c0-42bb-80c9-fc74b1225ebc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Removing Irrelevant Columns : (125063, 13)\n",
      "After Removing Irrelevant Columns : (125063, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overs</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>wickets</th>\n",
       "      <th>runs</th>\n",
       "      <th>total</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>214</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>214</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>214</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>214</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   overs batting_team bowling_team  wickets  runs  total  runs_last_5  \\\n",
       "0    0.1    Australia  New Zealand        0     2    214            2   \n",
       "1    0.2    Australia  New Zealand        0     2    214            2   \n",
       "2    0.3    Australia  New Zealand        0     3    214            3   \n",
       "3    0.4    Australia  New Zealand        0     4    214            4   \n",
       "4    0.5    Australia  New Zealand        0    10    214           10   \n",
       "\n",
       "   wickets_last_5  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irrelevant = ['match_id','start_date','venue','batsman','bowler']\n",
    "print(f'Before Removing Irrelevant Columns : {T20I_df.shape}')\n",
    "T20I_df = T20I_df.drop(irrelevant, axis=1) # Drop Irrelevant Columns\n",
    "print(f'After Removing Irrelevant Columns : {T20I_df.shape}')\n",
    "T20I_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "215a01a4-b84b-445c-9ccc-1227591766a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_order = ['batting_team', 'bowling_team', 'runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5', 'total']\n",
    "T20I_df = T20I_df[new_order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf7c924f-e02d-4139-ae3d-9af5f698b502",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Removing Overs : (125063, 8)\n",
      "After Removing Overs : (93068, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_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>30</th>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Australia</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>47</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   batting_team bowling_team  runs  wickets  overs  runs_last_5  \\\n",
       "30    Australia  New Zealand    54        4    5.1           52   \n",
       "31    Australia  New Zealand    55        4    5.2           53   \n",
       "32    Australia  New Zealand    56        4    5.3           53   \n",
       "33    Australia  New Zealand    57        4    5.4           53   \n",
       "34    Australia  New Zealand    57        4    5.5           47   \n",
       "\n",
       "    wickets_last_5  total  \n",
       "30               4    214  \n",
       "31               4    214  \n",
       "32               4    214  \n",
       "33               4    214  \n",
       "34               4    214  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'Before Removing Overs : {T20I_df.shape}')\n",
    "T20I_df = T20I_df[T20I_df['overs'] >= 5.0]\n",
    "print(f'After Removing Overs : {T20I_df.shape}')\n",
    "T20I_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a49d4c08-366b-4c61-aa66-7fead24769da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_14992\\1982911172.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  heatmap(data=T20I_df.corr(), annot=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from seaborn import heatmap\n",
    "heatmap(data=T20I_df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491f679f-333b-446c-a8b2-1d865e92c9b9",
   "metadata": {},
   "source": [
    "# Data Preprocessing and Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f63e3cf-ae7f-4350-b9ec-7dc0c33a5417",
   "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>batting_team</th>\n",
       "      <th>bowling_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>30</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>47</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    batting_team  bowling_team  runs  wickets  overs  runs_last_5  \\\n",
       "30             1             5    54        4    5.1           52   \n",
       "31             1             5    55        4    5.2           53   \n",
       "32             1             5    56        4    5.3           53   \n",
       "33             1             5    57        4    5.4           53   \n",
       "34             1             5    57        4    5.5           47   \n",
       "\n",
       "    wickets_last_5  total  \n",
       "30               4    214  \n",
       "31               4    214  \n",
       "32               4    214  \n",
       "33               4    214  \n",
       "34               4    214  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "le = LabelEncoder()\n",
    "for col in ['batting_team', 'bowling_team']:\n",
    "  T20I_df[col] = le.fit_transform(T20I_df[col])\n",
    "T20I_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "669476c6-ba83-46c0-918c-b0d097b55618",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "columnTransformer = ColumnTransformer([('encoder', \n",
    "                                        OneHotEncoder(), \n",
    "                                        [0, 1])], \n",
    "                                      remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94aaa553-f8ed-4a49-a805-9a1878ddb0fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "T20I_df = np.array(columnTransformer.fit_transform(T20I_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e1e3afc-071b-4486-8b35-999e77d1cbca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['batting_team_India', 'batting_team_South Africa', 'batting_team_Australia', 'batting_team_New Zealand',\n",
    "        'batting_team_Pakistan', 'batting_team_Afghanistan', 'batting_team_Sri Lanka', 'batting_team_England',\n",
    "        'batting_team_Bangladesh', 'batting_team_West Indies',\n",
    "        'bowling_team_India', 'bowling_team_South Africa', 'bowling_team_Australia', 'bowling_team_New Zealand',\n",
    "        'bowling_team_Pakistan', 'bowling_team_Afghanistan', 'bowling_team_Sri Lanka', 'bowling_team_England',\n",
    "        'bowling_team_Bangladesh', 'bowling_team_West Indies', 'runs', 'wickets', 'overs',\n",
    "        'runs_last_5', 'wickets_last_5', 'total']\n",
    "\n",
    "len(cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "afd81607-be09-466a-bb25-a8657e202531",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(T20I_df, columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ea6f0562-a2ae-403a-80d9-06fe7e155845",
   "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>batting_team_India</th>\n",
       "      <th>batting_team_South Africa</th>\n",
       "      <th>batting_team_Australia</th>\n",
       "      <th>batting_team_New Zealand</th>\n",
       "      <th>batting_team_Pakistan</th>\n",
       "      <th>batting_team_Afghanistan</th>\n",
       "      <th>batting_team_Sri Lanka</th>\n",
       "      <th>batting_team_England</th>\n",
       "      <th>batting_team_Bangladesh</th>\n",
       "      <th>batting_team_West Indies</th>\n",
       "      <th>...</th>\n",
       "      <th>bowling_team_Sri Lanka</th>\n",
       "      <th>bowling_team_England</th>\n",
       "      <th>bowling_team_Bangladesh</th>\n",
       "      <th>bowling_team_West Indies</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   batting_team_India  batting_team_South Africa  batting_team_Australia  \\\n",
       "0                 0.0                        1.0                     0.0   \n",
       "1                 0.0                        1.0                     0.0   \n",
       "2                 0.0                        1.0                     0.0   \n",
       "3                 0.0                        1.0                     0.0   \n",
       "4                 0.0                        1.0                     0.0   \n",
       "\n",
       "   batting_team_New Zealand  batting_team_Pakistan  batting_team_Afghanistan  \\\n",
       "0                       0.0                    0.0                       0.0   \n",
       "1                       0.0                    0.0                       0.0   \n",
       "2                       0.0                    0.0                       0.0   \n",
       "3                       0.0                    0.0                       0.0   \n",
       "4                       0.0                    0.0                       0.0   \n",
       "\n",
       "   batting_team_Sri Lanka  batting_team_England  batting_team_Bangladesh  \\\n",
       "0                     0.0                   0.0                      0.0   \n",
       "1                     0.0                   0.0                      0.0   \n",
       "2                     0.0                   0.0                      0.0   \n",
       "3                     0.0                   0.0                      0.0   \n",
       "4                     0.0                   0.0                      0.0   \n",
       "\n",
       "   batting_team_West Indies  ...  bowling_team_Sri Lanka  \\\n",
       "0                       0.0  ...                     0.0   \n",
       "1                       0.0  ...                     0.0   \n",
       "2                       0.0  ...                     0.0   \n",
       "3                       0.0  ...                     0.0   \n",
       "4                       0.0  ...                     0.0   \n",
       "\n",
       "   bowling_team_England  bowling_team_Bangladesh  bowling_team_West Indies  \\\n",
       "0                   0.0                      0.0                       0.0   \n",
       "1                   0.0                      0.0                       0.0   \n",
       "2                   0.0                      0.0                       0.0   \n",
       "3                   0.0                      0.0                       0.0   \n",
       "4                   0.0                      0.0                       0.0   \n",
       "\n",
       "   runs  wickets  overs  runs_last_5  wickets_last_5  total  \n",
       "0  54.0      4.0    5.1         52.0             4.0  214.0  \n",
       "1  55.0      4.0    5.2         53.0             4.0  214.0  \n",
       "2  56.0      4.0    5.3         53.0             4.0  214.0  \n",
       "3  57.0      4.0    5.4         53.0             4.0  214.0  \n",
       "4  57.0      4.0    5.5         47.0             4.0  214.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "544364a4-0511-4cb2-a9f2-4d2e2e5d2bc6",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c6980f9-97e8-4676-b970-8dbd421354ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df.drop(['total'], axis=1)\n",
    "labels = df['total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2c9c2455-4911-46bb-9d4b-ce3f1f0f7eb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set : (74454, 25)\n",
      "Testing Set : (18614, 25)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.20, shuffle=True)\n",
    "print(f\"Training Set : {train_features.shape}\\nTesting Set : {test_features.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "391303a2-214e-41e1-9726-8fef6ee84366",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "591ba6d4-58f6-4207-bacf-1dc364af0364",
   "metadata": {},
   "source": [
    "### Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a62c3aa-5e5c-4393-88a9-7718a649f3c2",
   "metadata": {},
   "source": [
    "### Decision Tree regressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "96ffa147-2e3e-41de-a97a-3d82cad08b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "tree = DecisionTreeRegressor()\n",
    "# Train Model\n",
    "tree.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b2ad1a7a-0acc-4fe0-82a9-ecb4166a8879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score : 99.96%\n",
      "Test Score : 81.00%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate Model\n",
    "train_score_tree = str(tree.score(train_features, train_labels) * 100)\n",
    "test_score_tree = str(tree.score(test_features, test_labels) * 100)\n",
    "print(f'Train Score : {train_score_tree[:5]}%\\nTest Score : {test_score_tree[:5]}%')\n",
    "models[\"tree\"] = test_score_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "902adb46-fa6d-4459-9b82-66bd6550384e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Decision Tree Regressor - Model Evaluation ----\n",
      "Mean Absolute Error (MAE): 7.128908348554851\n",
      "Mean Squared Error (MSE): 251.89057967121522\n",
      "Root Mean Squared Error (RMSE): 15.871061075782402\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error as mae, mean_squared_error as mse\n",
    "print(\"---- Decision Tree Regressor - Model Evaluation ----\")\n",
    "print(\"Mean Absolute Error (MAE): {}\".format(mae(test_labels, tree.predict(test_features))))\n",
    "print(\"Mean Squared Error (MSE): {}\".format(mse(test_labels, tree.predict(test_features))))\n",
    "print(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(test_labels, tree.predict(test_features)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4212ef2b-9729-4969-8310-f8027ba31635",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2cb3d0da-caeb-46fe-a2b7-7eb2ecb5ed6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linreg = LinearRegression()\n",
    "# Train Model\n",
    "linreg.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a46f58ac-33a5-4b31-8fa2-03a1f27c54e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score : 72.22%\n",
      "Test Score : 72.11%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate Model\n",
    "train_score_linreg = str(linreg.score(train_features, train_labels) * 100)\n",
    "test_score_linreg = str(linreg.score(test_features, test_labels) * 100)\n",
    "print(f'Train Score : {train_score_linreg[:5]}%\\nTest Score : {test_score_linreg[:5]}%')\n",
    "models[\"linreg\"] = test_score_linreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "282123dd-83ed-4dfe-a5f2-5fbf43dd857f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Linear Regression - Model Evaluation ----\n",
      "Mean Absolute Error (MAE): 14.223079108802514\n",
      "Mean Squared Error (MSE): 369.8595008418257\n",
      "Root Mean Squared Error (RMSE): 19.231731613191407\n"
     ]
    }
   ],
   "source": [
    "print(\"---- Linear Regression - Model Evaluation ----\")\n",
    "print(\"Mean Absolute Error (MAE): {}\".format(mae(test_labels, linreg.predict(test_features))))\n",
    "print(\"Mean Squared Error (MSE): {}\".format(mse(test_labels, linreg.predict(test_features))))\n",
    "print(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(test_labels, linreg.predict(test_features)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f1a6d3e-ffa6-45a7-a1f2-71904ba9c73c",
   "metadata": {},
   "source": [
    "### Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e4142caa-e47e-40fd-bb00-ae7aeaccfca4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imported\n",
      "loaded\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "print(\"imported\")\n",
    "forest = RandomForestRegressor()\n",
    "print(\"loaded\")\n",
    "# Train Model\n",
    "forest.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "832b0058-5a96-4a2a-8a7c-07b3987e54e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score : 98.68%\n",
      "Test Score : 90.82%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate Model\n",
    "train_score_forest = str(forest.score(train_features, train_labels)*100)\n",
    "test_score_forest = str(forest.score(test_features, test_labels)*100)\n",
    "print(f'Train Score : {train_score_forest[:5]}%\\nTest Score : {test_score_forest[:5]}%')\n",
    "models[\"forest\"] = test_score_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c92b45b8-4bd8-4829-b7ee-71a1fdb95b7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Random Forest Regression - Model Evaluation ----\n",
      "Mean Absolute Error (MAE): 6.986689396613916\n",
      "Mean Squared Error (MSE): 121.6494288109588\n",
      "Root Mean Squared Error (RMSE): 11.029479988238737\n"
     ]
    }
   ],
   "source": [
    "print(\"---- Random Forest Regression - Model Evaluation ----\")\n",
    "print(\"Mean Absolute Error (MAE): {}\".format(mae(test_labels, forest.predict(test_features))))\n",
    "print(\"Mean Squared Error (MSE): {}\".format(mse(test_labels, forest.predict(test_features))))\n",
    "print(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(test_labels, forest.predict(test_features)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfcdf8db-ff3f-4cab-b4cd-0c868e12e9df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imported\n",
      "loaded\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "print(\"imported\")\n",
    "svm = SVR()\n",
    "print(\"loaded\")\n",
    "# Train Model\n",
    "svm.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92acedfc-a6b7-4752-858c-fbb6acf025fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score_svm = str(svm.score(train_features, train_labels)*100)\n",
    "test_score_svm = str(svm.score(test_features, test_labels)*100)\n",
    "print(f'Train Score : {train_score_svm[:5]}%\\nTest Score : {test_score_svm[:5]}%')\n",
    "models[\"svm\"] = test_score_svm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a333dba2-179a-46ee-9501-7c516933d031",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"---- Support Vector Regression - Model Evaluation ----\")\n",
    "print(\"Mean Absolute Error (MAE): {}\".format(mae(test_labels, svm.predict(test_features))))\n",
    "print(\"Mean Squared Error (MSE): {}\".format(mse(test_labels, svm.predict(test_features))))\n",
    "print(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(test_labels, svm.predict(test_features)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "046407e8-4358-4073-ad19-d7a785de213b",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5b23368a-e8c8-426a-9b09-232ffe943199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "xgb = XGBRegressor()\n",
    "# Train Model\n",
    "xgb.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "181b11f6-7a59-41e3-9505-4e372a3c8086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score : 85.50%\n",
      "Test Score : 83.13%\n"
     ]
    }
   ],
   "source": [
    "train_score_xgb = str(xgb.score(train_features, train_labels)*100)\n",
    "test_score_xgb = str(xgb.score(test_features, test_labels)*100)\n",
    "print(f'Train Score : {train_score_xgb[:5]}%\\nTest Score : {test_score_xgb[:5]}%')\n",
    "models[\"xgb\"] = test_score_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4377393b-4774-4f65-8518-c819d1d60f79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- XGB Regression - Model Evaluation ----\n",
      "Mean Absolute Error (MAE): 10.795554480875962\n",
      "Mean Squared Error (MSE): 223.67044117890515\n",
      "Root Mean Squared Error (RMSE): 14.955615707115008\n"
     ]
    }
   ],
   "source": [
    "print(\"---- XGB Regression - Model Evaluation ----\")\n",
    "print(\"Mean Absolute Error (MAE): {}\".format(mae(test_labels, xgb.predict(test_features))))\n",
    "print(\"Mean Squared Error (MSE): {}\".format(mse(test_labels, xgb.predict(test_features))))\n",
    "print(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(test_labels, xgb.predict(test_features)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3be81dc-0ed4-4801-b40f-99e2e0db95cc",
   "metadata": {},
   "source": [
    "### KNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4c2c9253-500c-4402-82ae-70fcc48752ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knr = KNeighborsRegressor()\n",
    "# Train Model\n",
    "knr.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0d53f678-9e6b-44fb-a2a2-4e343700c0fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score : 89.10%\n",
      "Test Score : 81.98%\n"
     ]
    }
   ],
   "source": [
    "train_score_knr = str(knr.score(train_features, train_labels)*100)\n",
    "test_score_knr = str(knr.score(test_features, test_labels)*100)\n",
    "print(f'Train Score : {train_score_knr[:5]}%\\nTest Score : {test_score_knr[:5]}%')\n",
    "models[\"knr\"] = test_score_knr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "eb319358-7bb6-40e6-b6fb-191f14ccbbbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- KNR - Model Evaluation ----\n",
      "Mean Absolute Error (MAE): 10.48187385838616\n",
      "Mean Squared Error (MSE): 238.96951541850223\n",
      "Root Mean Squared Error (RMSE): 15.458638860472233\n"
     ]
    }
   ],
   "source": [
    "print(\"---- KNR - Model Evaluation ----\")\n",
    "print(\"Mean Absolute Error (MAE): {}\".format(mae(test_labels, knr.predict(test_features))))\n",
    "print(\"Mean Squared Error (MSE): {}\".format(mse(test_labels, knr.predict(test_features))))\n",
    "print(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(test_labels, knr.predict(test_features)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebebfa52-4434-4470-bce7-851fbd1e5f7a",
   "metadata": {},
   "source": [
    "## Comparision of the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1710d4f1-402f-403b-8024-90b71a485c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "model_names = list(models.keys())\n",
    "accuracy = list(map(float, models.values()))\n",
    "# creating the bar plot\n",
    "plt.bar(model_names, accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b964a63-480d-4a41-bfe2-5ab449766ed8",
   "metadata": {},
   "source": [
    "Clearly Forest wins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47b7e37-9686-4e16-aea3-7f27bbb31eeb",
   "metadata": {},
   "source": [
    "## Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "84b43f09-b07f-46c6-9b13-222f338ee598",
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_predict(batting_team, bowling_team, runs, wickets, overs, runs_last_5, wickets_last_5, model=forest):\n",
    "  prediction_array = []\n",
    "  # Batting Team\n",
    "  if batting_team == 'India':\n",
    "    prediction_array = prediction_array + [1,0,0,0,0,0,0,0,0,0]\n",
    "  elif batting_team == 'South Africa':\n",
    "    prediction_array = prediction_array + [0,1,0,0,0,0,0,0,0,0]\n",
    "  elif batting_team == 'Australia':\n",
    "    prediction_array = prediction_array + [0,0,1,0,0,0,0,0,0,0]\n",
    "  elif batting_team == 'New Zealand':\n",
    "    prediction_array = prediction_array + [0,0,0,1,0,0,0,0,0,0]\n",
    "  elif batting_team == 'Pakistan':\n",
    "    prediction_array = prediction_array + [0,0,0,0,1,0,0,0,0,0]\n",
    "  elif batting_team == 'Afghanistan':\n",
    "    prediction_array = prediction_array + [0,0,0,0,0,1,0,0,0,0]\n",
    "  elif batting_team == 'Sri Lanka':\n",
    "    prediction_array = prediction_array + [0,0,0,0,0,0,1,0,0,0]\n",
    "  elif batting_team == 'England':\n",
    "    prediction_array = prediction_array + [0,0,0,0,0,0,0,1,0,0]\n",
    "  elif batting_team == 'Bangladesh':\n",
    "    prediction_array = prediction_array + [0,0,0,0,0,0,0,0,1,0]\n",
    "  elif batting_team == 'West Indies':\n",
    "    prediction_array = prediction_array + [0,0,0,0,0,0,0,0,0,1]\n",
    "  # Bowling Team\n",
    "  if bowling_team == 'India':\n",
    "    prediction_array = prediction_array + [1,0,0,0,0,0,0,0,0,0]\n",
    "  elif bowling_team == 'South Africa':\n",
    "    prediction_array = prediction_array + [0,1,0,0,0,0,0,0,0,0]\n",
    "  elif bowling_team == 'Australia':\n",
    "    prediction_array = prediction_array + [0,0,1,0,0,0,0,0,0,0]\n",
    "  elif bowling_team == 'New Zealand':\n",
    "    prediction_array = prediction_array + [0,0,0,1,0,0,0,0,0,0]\n",
    "  elif bowling_team == 'Pakistan':\n",
    "    prediction_array = prediction_array + [0,0,0,0,1,0,0,0,0,0]\n",
    "  elif bowling_team == 'Afghanistan':\n",
    "    prediction_array = prediction_array + [0,0,0,0,0,1,0,0,0,0]\n",
    "  elif bowling_team == 'Sri Lanka':\n",
    "    prediction_array = prediction_array + [0,0,0,0,0,0,1,0,0,0]\n",
    "  elif bowling_team == 'England':\n",
    "    prediction_array = prediction_array + [0,0,0,0,0,0,0,1,0,0]\n",
    "  elif bowling_team == 'Bangladesh':\n",
    "    prediction_array = prediction_array + [0,0,0,0,0,0,0,0,1,0]\n",
    "  elif bowling_team == 'West Indies':\n",
    "    prediction_array = prediction_array + [0,0,0,0,0,0,0,0,0,1]\n",
    "  prediction_array = prediction_array + [runs, wickets, overs, runs_last_5, wickets_last_5]\n",
    "  prediction_array = np.array([prediction_array])\n",
    "  pred = model.predict(prediction_array)\n",
    "  return int(round(pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65fecf48-233a-4693-a8b7-19b992eff576",
   "metadata": {},
   "source": [
    "### Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "049cff13-774d-4e48-83e9-5dfa546610a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score : 165 || Actual Score : 172\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#example 1\n",
    "batting_team='Afghanistan'\n",
    "bowling_team='India'\n",
    "score = score_predict(batting_team, bowling_team, overs=8.0, runs=63, wickets=3, runs_last_5=41, wickets_last_5=1)\n",
    "print(f'Predicted Score : {score} || Actual Score : 172')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8b251a14-cc2d-4774-8f3a-3f826ec3df3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score : 196 || Actual Score : 194\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#example 2\n",
    "batting_team='New Zealand'\n",
    "bowling_team='Pakistan'\n",
    "score = score_predict(batting_team, bowling_team, overs=10.0, runs=111, wickets=1, runs_last_5=52, wickets_last_5=1)\n",
    "print(f'Predicted Score : {score} || Actual Score : 194')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "823f232f-d0dc-4205-8450-5fac498ac290",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score : 134 || Actual Score : 134\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#example 3\n",
    "batting_team='New Zealand'\n",
    "bowling_team='Bangladesh'\n",
    "score = score_predict(batting_team, bowling_team, overs=10.0, runs=53, wickets=5, runs_last_5=31, wickets_last_5=1)\n",
    "print(f'Predicted Score : {score} || Actual Score : 134')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "13343a54-d731-4c8e-a2dc-8eac306ed4f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score : 158 || Actual Score : 137\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#example 4 WT20 2022 final\n",
    "batting_team='Pakistan'\n",
    "bowling_team='England'\n",
    "score = score_predict(batting_team, bowling_team, overs=10.0, runs=68, wickets=2, runs_last_5=39, wickets_last_5=2)\n",
    "print(f'Predicted Score : {score} || Actual Score : 137')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "da79ed1f-771f-45e0-a9f0-f3bd78c7eb9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score : 140 || Actual Score : 172\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#example 5 WT20 2021 final\n",
    "batting_team='New Zealand'\n",
    "bowling_team='Australia'\n",
    "score = score_predict(batting_team, bowling_team, overs=10.0, runs=57, wickets=1, runs_last_5=27, wickets_last_5=0)\n",
    "print(f'Predicted Score : {score} || Actual Score : 172')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b72a4f62-bde7-42d1-8627-db9f0d1db46a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score : 170 || Actual Score : 183\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#example 6 asia cup 2022\n",
    "batting_team='Bangladesh'\n",
    "bowling_team='Sri Lanka'\n",
    "score = score_predict(batting_team, bowling_team, overs=15.0, runs=123, wickets=4, runs_last_5=38, wickets_last_5=1)\n",
    "print(f'Predicted Score : {score} || Actual Score : 183')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d95ee653-651a-4b4a-b025-1be760fdbe1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score : 223 || Actual Score : 258\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#example 7 \n",
    "batting_team='West Indies'\n",
    "bowling_team='South Africa'\n",
    "score = score_predict(batting_team, bowling_team, overs=10.0, runs=137, wickets=1, runs_last_5=84, wickets_last_5=0)\n",
    "print(f'Predicted Score : {score} || Actual Score : 258')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc4eccf-dc95-4c6e-825c-24fd0625f3b4",
   "metadata": {},
   "source": [
    " ## Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2b7e64cc-c177-431f-9790-86eb85f0c7da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename = \"T20I.pkl\"\n",
    "pickle.dump(forest, open(filename, \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4701067-b096-4025-abf6-178a9e572d9d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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