First Innings Score Prediction - IPL.ipynb

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   "source": [
    "# Importing essential libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Z8utzElrdPub"
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   "source": [
    "# Loading the dataset\n",
    "df = pd.read_csv('ipl.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5vWE2y4ldZC4"
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   "source": [
    "## **Exploring the dataset**"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mid', 'date', 'venue', 'bat_team', 'bowl_team', 'batsman', 'bowler',\n",
       "       'runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5', 'striker',\n",
       "       'non-striker', 'total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76014, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
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   "execution_count": 5,
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    "outputId": "55a278d8-cbc8-467a-892f-c33ed14fd8c1"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "mid                 int64\n",
       "date               object\n",
       "venue              object\n",
       "bat_team           object\n",
       "bowl_team          object\n",
       "batsman            object\n",
       "bowler             object\n",
       "runs                int64\n",
       "wickets             int64\n",
       "overs             float64\n",
       "runs_last_5         int64\n",
       "wickets_last_5      int64\n",
       "striker             int64\n",
       "non-striker         int64\n",
       "total               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.dtypes"
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       "<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",
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       "      <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",
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       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
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       "    <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",
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       "      <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",
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       "      <td>BB McCullum</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
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      "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  "
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    "df.head()"
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   "source": [
    "## **Data Cleaning**\n",
    "Points covered under this section:<br/>\n",
    "*• Removing unwanted columns*<br/>\n",
    "*• Keeping only consistent teams*<br/>\n",
    "*• Removing the first 5 overs data in every match*<br/>\n",
    "*• Converting the column 'date' from string into datetime object*<br/>"
   ]
  },
  {
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   "execution_count": 7,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mid', 'date', 'venue', 'bat_team', 'bowl_team', 'batsman', 'bowler',\n",
       "       'runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5', 'striker',\n",
       "       'non-striker', 'total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
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  {
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    },
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    "id": "bOyVrvsSdyFx",
    "outputId": "4a70f21e-7f2d-439a-bfb1-d0f7037cca49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before removing unwanted columns: (76014, 15)\n",
      "After removing unwanted columns: (76014, 9)\n"
     ]
    }
   ],
   "source": [
    "# Removing unwanted columns\n",
    "columns_to_remove = ['mid', 'venue', 'batsman', 'bowler', 'striker', 'non-striker']\n",
    "\n",
    "print('Before removing unwanted columns: {}'.format(df.shape))\n",
    "df.drop(labels=columns_to_remove, axis=1, inplace=True)\n",
    "print('After removing unwanted columns: {}'.format(df.shape))"
   ]
  },
  {
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   "execution_count": 9,
   "metadata": {
    "colab": {
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    "id": "VeYhEaB6fQdC",
    "outputId": "93c091c8-16ce-47be-a79b-46e5dbe1f691"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'bat_team', 'bowl_team', 'runs', 'wickets', 'overs',\n",
       "       'runs_last_5', 'wickets_last_5', 'total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "df.columns"
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       "      <th></th>\n",
       "      <th>date</th>\n",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>222</td>\n",
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       "    <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",
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       "    <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",
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       "      <td>222</td>\n",
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       "    <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",
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      ],
      "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  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 34
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    "colab_type": "code",
    "id": "W2jQTWJhmIrt",
    "outputId": "23463ddc-6c24-43f7-d60d-f43f64ad1a6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=76014, step=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
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    "colab_type": "code",
    "id": "HD-7eP27iWab",
    "outputId": "2477db91-0a4e-41e3-9689-2abf870a2d4d"
   },
   "outputs": [
    {
     "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)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['bat_team'].unique()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VcC_Crmhih1r"
   },
   "outputs": [],
   "source": [
    "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": 14,
   "metadata": {
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    "colab_type": "code",
    "id": "MXmAt3Colmdu",
    "outputId": "9874be5e-a71f-418a-8d90-45268c6c634f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before removing inconsistent teams: (76014, 9)\n",
      "After removing inconsistent teams: (53811, 9)\n"
     ]
    }
   ],
   "source": [
    "# Keeping only consistent teams\n",
    "print('Before removing inconsistent teams: {}'.format(df.shape))\n",
    "df = df[(df['bat_team'].isin(consistent_teams)) & (df['bowl_team'].isin(consistent_teams))]\n",
    "print('After removing inconsistent teams: {}'.format(df.shape))"
   ]
  },
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    "colab_type": "code",
    "id": "AB8uOLpHoKtB",
    "outputId": "89633380-9611-4552-e964-2d7fdbe8839c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',\n",
       "       'Mumbai Indians', 'Kings XI Punjab', 'Royal Challengers Bangalore',\n",
       "       'Delhi Daredevils', 'Sunrisers Hyderabad'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bat_team'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "HVouIS8QqFwX",
    "outputId": "5cf9861c-e3e4-41c4-937e-bf1de77c6556"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before removing first 5 overs data: (53811, 9)\n",
      "After removing first 5 overs data: (40108, 9)\n"
     ]
    }
   ],
   "source": [
    "# Removing the first 5 overs data in every match\n",
    "print('Before removing first 5 overs data: {}'.format(df.shape))\n",
    "df = df[df['overs']>=5.0]\n",
    "print('After removing first 5 overs data: {}'.format(df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "J_R7UNqkf3Mt",
    "outputId": "e2383f98-4771-41d9-c6a4-ebd96a7aa575"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before converting 'date' column from string to datetime object: <class 'str'>\n",
      "After converting 'date' column from string to datetime object: <class 'pandas._libs.tslibs.timestamps.Timestamp'>\n"
     ]
    }
   ],
   "source": [
    "# Converting the column 'date' from string into datetime object\n",
    "from datetime import datetime\n",
    "print(\"Before converting 'date' column from string to datetime object: {}\".format(type(df.iloc[0,0])))\n",
    "df['date'] = df['date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d'))\n",
    "print(\"After converting 'date' column from string to datetime object: {}\".format(type(df.iloc[0,0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 651
    },
    "colab_type": "code",
    "id": "try0O4SSxjBE",
    "outputId": "3bde5d22-653f-49a4-965b-f992b2a6374d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Selecting correlated features using Heatmap\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Get correlation of all the features of the dataset\n",
    "corr_matrix = df.corr()\n",
    "top_corr_features = corr_matrix.index\n",
    "\n",
    "# Plotting the heatmap\n",
    "plt.figure(figsize=(13,10))\n",
    "g = sns.heatmap(data=df[top_corr_features].corr(), annot=True, cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o9Qt4bOGpKh3"
   },
   "source": [
    "## **Data Preprocessing**\n",
    "*• Handling categorical features*<br/>\n",
    "*• Splitting dataset into train and test set on the basis of date*<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 212
    },
    "colab_type": "code",
    "id": "ZpJLe1YVfrr6",
    "outputId": "ced0b921-0d9e-4f51-f3ce-841d16fcff0f"
   },
   "outputs": [
    {
     "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')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting categorical features using OneHotEncoding method\n",
    "encoded_df = pd.get_dummies(data=df, columns=['bat_team', 'bowl_team'])\n",
    "encoded_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "colab_type": "code",
    "id": "ZtrtRO7AiLPz",
    "outputId": "925d1844-a1b0-49ac-bced-79940195d950"
   },
   "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>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>"
      ],
      "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]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dVj9eyGJj5-J"
   },
   "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": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "8raq2VVXeHyr",
    "outputId": "c89b9399-6397-438f-cd3c-3e7ca0ae63ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set: (37330, 21) and Test set: (2778, 21)\n"
     ]
    }
   ],
   "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]\n",
    "\n",
    "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\n",
    "\n",
    "# Removing the 'date' column\n",
    "X_train.drop(labels='date', axis=True, inplace=True)\n",
    "X_test.drop(labels='date', axis=True, inplace=True)\n",
    "\n",
    "print(\"Training set: {} and Test set: {}\".format(X_train.shape, X_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QRZoptrOlsT5"
   },
   "source": [
    "## **Model Building**\n",
    "I will experiment with 5 different algorithms, they are as follows:<br/>\n",
    "*• Linear Regression*<br/>\n",
    "*• Decision Tree Regression*<br/>\n",
    "*• Random Forest Regression*<br/>\n",
    "\n",
    "----- Boosting Algorithm -----<br/>\n",
    "*• Adaptive Boosting (AdaBoost) Algorithm*<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w5NJZyB8oFEw"
   },
   "source": [
    "### *Linear Regression*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "TAbGSLrVln6Q",
    "outputId": "d33e0b5f-511c-42dd-f4e3-da8f8b6b2610"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linear_regressor = LinearRegression()\n",
    "linear_regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JeKomR6-nfaX"
   },
   "outputs": [],
   "source": [
    "# Predicting results\n",
    "y_pred_lr = linear_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "id": "xRPc6nsmmlbo",
    "outputId": "24f2c7ee-a0dd-43c3-9efc-ba1d0856cad7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Linear Regression - Model Evaluation ----\n",
      "Mean Absolute Error (MAE): 12.128479487266018\n",
      "Mean Squared Error (MSE): 251.87963986465448\n",
      "Root Mean Squared Error (RMSE): 15.870716425689626\n"
     ]
    }
   ],
   "source": [
    "# Linear Regression - Model Evaluation\n",
    "from sklearn.metrics import mean_absolute_error as mae, mean_squared_error as mse, accuracy_score\n",
    "print(\"---- Linear Regression - Model Evaluation ----\")\n",
    "print(\"Mean Absolute Error (MAE): {}\".format(mae(y_test, y_pred_lr)))\n",
    "print(\"Mean Squared Error (MSE): {}\".format(mse(y_test, y_pred_lr)))\n",
    "print(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(y_test, y_pred_lr))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fuPztrQQoLNg"
   },
   "source": [
    "### *Decision Tree*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "id": "drFWmrvBoC6x",
    "outputId": "83a98e88-4307-4bbd-f941-74bb30dc5170"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree Regression Model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "decision_regressor = DecisionTreeRegressor()\n",
    "decision_regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cCl-LXmpofeq"
   },
   "outputs": [],
   "source": [
    "# Predicting results\n",
    "y_pred_dt = decision_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "id": "fxQBkteHoj2Y",
    "outputId": "bc4f87bf-0ce4-44fb-c957-11f85d3526e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Decision Tree Regression - Model Evaluation ----\n",
      "Mean Absolute Error (MAE): 16.872210223182144\n",
      "Mean Squared Error (MSE): 522.3581713462924\n",
      "Root Mean Squared Error (RMSE): 22.855156340447387\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree Regression - Model Evaluation\n",
    "print(\"---- Decision Tree Regression - Model Evaluation ----\")\n",
    "print(\"Mean Absolute Error (MAE): {}\".format(mae(y_test, y_pred_dt)))\n",
    "print(\"Mean Squared Error (MSE): {}\".format(mse(y_test, y_pred_dt)))\n",
    "print(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(y_test, y_pred_dt))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "scpqVJxCpFyB"
   },
   "source": [
    "### *Random Forest*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "colab_type": "code",
    "id": "MNNGZMlRpKnq",
    "outputId": "8d67934e-f177-4270-b2ba-93feba78fef6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Regression Model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "random_regressor = RandomForestRegressor()\n",
    "random_regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UpqtMy1jpYTd"
   },
   "outputs": [],
   "source": [
    "# Predicting results\n",
    "y_pred_rf = random_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "id": "VPsdbFk_pdrH",
    "outputId": "ad050bf7-70e5-4df5-a7ca-7fc0700cf7d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Random Forest Regression - Model Evaluation ----\n",
      "Mean Absolute Error (MAE): 13.760095992320613\n",
      "Mean Squared Error (MSE): 332.5215985776338\n",
      "Root Mean Squared Error (RMSE): 18.235174761368036\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Regression - Model Evaluation\n",
    "print(\"---- Random Forest Regression - Model Evaluation ----\")\n",
    "print(\"Mean Absolute Error (MAE): {}\".format(mae(y_test, y_pred_rf)))\n",
    "print(\"Mean Squared Error (MSE): {}\".format(mse(y_test, y_pred_rf)))\n",
    "print(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(y_test, y_pred_rf))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wMd5-w5Tpv-Y"
   },
   "source": [
    "*Note: Since Linear Regression model performs best as compared to other two, we use this model and boost it's performance using AdaBoost Algorithm*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cJNnp9xHssI8"
   },
   "source": [
    "### *AdaBoost Algorithm*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "id": "eua4hjEus0W2",
    "outputId": "2290f73b-67f2-4551-a77a-7f2942e465c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostRegressor(base_estimator=LinearRegression(), n_estimators=100)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# AdaBoost Model using Linear Regression as the base learner\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "adb_regressor = AdaBoostRegressor(base_estimator=linear_regressor, n_estimators=100)\n",
    "adb_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mBRmYqGvtdaJ"
   },
   "outputs": [],
   "source": [
    "# Predicting results\n",
    "y_pred_adb = adb_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "id": "67pZWWEKtgiF",
    "outputId": "709cb77d-a0eb-43f5-aded-3c16ff2d0c98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- AdaBoost Regression - Model Evaluation ----\n",
      "Mean Absolute Error (MAE): 12.146877588171852\n",
      "Mean Squared Error (MSE): 246.8020090931219\n",
      "Root Mean Squared Error (RMSE): 15.70993345285466\n"
     ]
    }
   ],
   "source": [
    "# AdaBoost Regression - Model Evaluation\n",
    "print(\"---- AdaBoost Regression - Model Evaluation ----\")\n",
    "print(\"Mean Absolute Error (MAE): {}\".format(mae(y_test, y_pred_adb)))\n",
    "print(\"Mean Squared Error (MSE): {}\".format(mse(y_test, y_pred_adb)))\n",
    "print(\"Root Mean Squared Error (RMSE): {}\".format(np.sqrt(mse(y_test, y_pred_adb))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nbvk3I7ZvVG4"
   },
   "source": [
    "*Note: Using AdaBoost did not reduce the error to a significant level. Hence, we will you simple linear regression model for prediction*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2Xl7f4FQybFg"
   },
   "source": [
    "## **Predictions**\n",
    "• Model *trained on* the data from **IPL Seasons 1 to 9** ie: (2008 to 2016)<br/>\n",
    "• Model *tested on* data from **IPL Season 10** ie: (2017)<br/>\n",
    "• Model *predicts on* data from **IPL Seasons 11 to 12** ie: (2018 to 2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-C4QKIa-yhYB"
   },
   "outputs": [],
   "source": [
    "def predict_score(batting_team='Chennai Super Kings', bowling_team='Mumbai Indians', overs=5.1, runs=50, wickets=0, runs_in_prev_5=50, wickets_in_prev_5=0):\n",
    "  temp_array = list()\n",
    "\n",
    "  # Batting Team\n",
    "  if batting_team == 'Chennai Super Kings':\n",
    "    temp_array = temp_array + [1,0,0,0,0,0,0,0]\n",
    "  elif batting_team == 'Delhi Daredevils':\n",
    "    temp_array = temp_array + [0,1,0,0,0,0,0,0]\n",
    "  elif batting_team == 'Kings XI Punjab':\n",
    "    temp_array = temp_array + [0,0,1,0,0,0,0,0]\n",
    "  elif batting_team == 'Kolkata Knight Riders':\n",
    "    temp_array = temp_array + [0,0,0,1,0,0,0,0]\n",
    "  elif batting_team == 'Mumbai Indians':\n",
    "    temp_array = temp_array + [0,0,0,0,1,0,0,0]\n",
    "  elif batting_team == 'Rajasthan Royals':\n",
    "    temp_array = temp_array + [0,0,0,0,0,1,0,0]\n",
    "  elif batting_team == 'Royal Challengers Bangalore':\n",
    "    temp_array = temp_array + [0,0,0,0,0,0,1,0]\n",
    "  elif batting_team == 'Sunrisers Hyderabad':\n",
    "    temp_array = temp_array + [0,0,0,0,0,0,0,1]\n",
    "\n",
    "  # Bowling Team\n",
    "  if bowling_team == 'Chennai Super Kings':\n",
    "    temp_array = temp_array + [1,0,0,0,0,0,0,0]\n",
    "  elif bowling_team == 'Delhi Daredevils':\n",
    "    temp_array = temp_array + [0,1,0,0,0,0,0,0]\n",
    "  elif bowling_team == 'Kings XI Punjab':\n",
    "    temp_array = temp_array + [0,0,1,0,0,0,0,0]\n",
    "  elif bowling_team == 'Kolkata Knight Riders':\n",
    "    temp_array = temp_array + [0,0,0,1,0,0,0,0]\n",
    "  elif bowling_team == 'Mumbai Indians':\n",
    "    temp_array = temp_array + [0,0,0,0,1,0,0,0]\n",
    "  elif bowling_team == 'Rajasthan Royals':\n",
    "    temp_array = temp_array + [0,0,0,0,0,1,0,0]\n",
    "  elif bowling_team == 'Royal Challengers Bangalore':\n",
    "    temp_array = temp_array + [0,0,0,0,0,0,1,0]\n",
    "  elif bowling_team == 'Sunrisers Hyderabad':\n",
    "    temp_array = temp_array + [0,0,0,0,0,0,0,1]\n",
    "\n",
    "  # Overs, Runs, Wickets, Runs_in_prev_5, Wickets_in_prev_5\n",
    "  temp_array = temp_array + [overs, runs, wickets, runs_in_prev_5, wickets_in_prev_5]\n",
    "\n",
    "  # Converting into numpy array\n",
    "  temp_array = np.array([temp_array])\n",
    "\n",
    "  # Prediction\n",
    "  return int(linear_regressor.predict(temp_array)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"linear_regression.pkl\",'wb') as f:\n",
    "    pickle.dump(linear_regressor,f)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"proj_data.json\",'w') as f:\n",
    "    json.data = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lRZQW-YGS141"
   },
   "source": [
    "### **Prediction 1**\n",
    "• Date: 16th April 2018<br/>\n",
    "• IPL : Season 11<br/>\n",
    "• Match number: 13<br/>\n",
    "• Teams: Kolkata Knight Riders vs. Delhi Daredevils<br/>\n",
    "• First Innings final score: 200/9\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "fhgissiE10Bx",
    "outputId": "94bfaafb-6305-4187-c813-9c2c9af0a3d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final predicted score (range): 159 to 174\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "final_score = predict_score(batting_team='Kolkata Knight Riders', bowling_team='Delhi Daredevils', overs=9.2, runs=79, wickets=2, runs_in_prev_5=60, wickets_in_prev_5=1)\n",
    "print(\"The final predicted score (range): {} to {}\".format(final_score-10, final_score+5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IMAYg53PfbLm"
   },
   "source": [
    "### **Prediction 2**\n",
    "• Date: 7th May 2018<br/>\n",
    "• IPL : Season 11<br/>\n",
    "• Match number: 39<br/>\n",
    "• Teams: Sunrisers Hyderabad vs. Royal Challengers Bangalore<br/>\n",
    "• First Innings final score: 146/10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "eVtlLk3afeDT",
    "outputId": "62562997-ce49-41c5-8341-430965293e35"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final predicted score (range): 138 to 153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "final_score = predict_score(batting_team='Sunrisers Hyderabad', bowling_team='Royal Challengers Bangalore', overs=10.5, runs=67, wickets=3, runs_in_prev_5=29, wickets_in_prev_5=1)\n",
    "print(\"The final predicted score (range): {} to {}\".format(final_score-10, final_score+5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M-3FC7VhUzdK"
   },
   "source": [
    "### **Prediction 3**\n",
    "• Date: 17th May 2018<br/>\n",
    "• IPL : Season 11<br/>\n",
    "• Match number: 50<br/>\n",
    "• Teams: Mumbai Indians vs. Kings XI Punjab<br/>\n",
    "• First Innings final score: 186/8<br/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "CVgb01MY29NQ",
    "outputId": "a1a3dc25-0ba3-489d-ff65-a4a3c4829c22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final predicted score (range): 180 to 195\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "final_score = predict_score(batting_team='Mumbai Indians', bowling_team='Kings XI Punjab', overs=14.1, runs=136, wickets=4, runs_in_prev_5=50, wickets_in_prev_5=0)\n",
    "print(\"The final predicted score (range): {} to {}\".format(final_score-10, final_score+5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ix3XW3y2iPgc"
   },
   "source": [
    "### **Prediction 4**\n",
    "• Date: 30th March 2019<br/>\n",
    "• IPL : Season 12<br/>\n",
    "• Match number: 9<br/>\n",
    "• Teams: Mumbai Indians vs. Kings XI Punjab<br/>\n",
    "• First Innings final score: 176/7\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "FUWG9gsviudD",
    "outputId": "eaf6b5c4-d1ef-4f13-9ba4-8457fbe6dd5d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final predicted score (range): 179 to 194\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "final_score = predict_score(batting_team='Mumbai Indians', bowling_team='Kings XI Punjab', overs=12.3, runs=113, wickets=2, runs_in_prev_5=55, wickets_in_prev_5=0)\n",
    "print(\"The final predicted score (range): {} to {}\".format(final_score-10, final_score+5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pNs2zIxlW6Ou"
   },
   "source": [
    "### **Prediction 5**\n",
    "• Date: 11th April 2019<br/>\n",
    "• IPL : Season 12<br/>\n",
    "• Match number: 25<br/>\n",
    "• Teams: Rajasthan Royals vs. Chennai Super Kings<br/>\n",
    "• First Innings final score: 151/7\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "hByMrV6l29YV",
    "outputId": "1cff54c2-0e7b-46f9-dd02-4437bd0d4028"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final predicted score (range): 128 to 143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "final_score = predict_score(batting_team='Rajasthan Royals', bowling_team='Chennai Super Kings', overs=13.3, runs=92, wickets=5, runs_in_prev_5=27, wickets_in_prev_5=2)\n",
    "print(\"The final predicted score (range): {} to {}\".format(final_score-10, final_score+5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hYLKJMHShBn8"
   },
   "source": [
    "### **Prediction 6**\n",
    "• Date: 14th April 2019<br/>\n",
    "• IPL : Season 12<br/>\n",
    "• Match number: 30<br/>\n",
    "• Teams: Sunrisers Hyderabad vs. Delhi Daredevils<br/>\n",
    "• First Innings final score: 155/7\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "dAmNR2WLhD2F",
    "outputId": "f6d18cd0-5eeb-4999-a6d9-9b303324fa2a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final predicted score (range): 158 to 173\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "final_score = predict_score(batting_team='Delhi Daredevils', bowling_team='Sunrisers Hyderabad', overs=11.5, runs=98, wickets=3, runs_in_prev_5=41, wickets_in_prev_5=1)\n",
    "print(\"The final predicted score (range): {} to {}\".format(final_score-10, final_score+5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zxjq482uaQpc"
   },
   "source": [
    "### **Prediction 7**\n",
    "• Date: 10th May 2019<br/>\n",
    "• IPL : Season 12<br/>\n",
    "• Match number: 59 (Eliminator)<br/>\n",
    "• Teams: Delhi Daredevils vs. Chennai Super Kings<br/>\n",
    "• First Innings final score: 147/9\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "UR6QNPK_aSSj",
    "outputId": "1afc6b5b-d1bc-4f55-9c61-362e31cb4e80"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final predicted score (range): 138 to 153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "final_score = predict_score(batting_team='Delhi Daredevils', bowling_team='Chennai Super Kings', overs=10.2, runs=68, wickets=3, runs_in_prev_5=29, wickets_in_prev_5=1)\n",
    "print(\"The final predicted score (range): {} to {}\".format(final_score-10, final_score+5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZaEK5rEmjp8K"
   },
   "source": [
    "*Note: In IPL, it is very difficult to predict the actual score because in a moment of time the game can completely turn upside down!*\n"
   ]
  }
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