First Innings Score Prediction - IPL.ipynb

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    "colab": {
      "name": "First Innings Score Predictor - IPL.ipynb",
      "provenance": [],
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      "toc_visible": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Ye3oEMGgcWhp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing essential libraries\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8utzElrdPub",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loading the dataset\n",
        "df = pd.read_csv('ipl.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vWE2y4ldZC4",
        "colab_type": "text"
      },
      "source": [
        "## **Exploring the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEkrx9IWdYHh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "71a453fa-df59-40b6-81b5-3407a240b3f0"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "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')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "3OZDpVGvddx6",
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          "height": 34
        },
        "outputId": "14bdb7d8-4de8-42d5-820e-a4151ff3533d"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(76014, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNodK4n7dpep",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "55a278d8-cbc8-467a-892f-c33ed14fd8c1"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8VJefH9drgh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "1bd26e10-6d71-4e6d-c747-9a4d014f1adc"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mid</th>\n",
              "      <th>date</th>\n",
              "      <th>venue</th>\n",
              "      <th>bat_team</th>\n",
              "      <th>bowl_team</th>\n",
              "      <th>batsman</th>\n",
              "      <th>bowler</th>\n",
              "      <th>runs</th>\n",
              "      <th>wickets</th>\n",
              "      <th>overs</th>\n",
              "      <th>runs_last_5</th>\n",
              "      <th>wickets_last_5</th>\n",
              "      <th>striker</th>\n",
              "      <th>non-striker</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>SC Ganguly</td>\n",
              "      <td>P Kumar</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>BB McCullum</td>\n",
              "      <td>P Kumar</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>BB McCullum</td>\n",
              "      <td>P Kumar</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>BB McCullum</td>\n",
              "      <td>P Kumar</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>BB McCullum</td>\n",
              "      <td>P Kumar</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mid        date                  venue  ... striker non-striker total\n",
              "0    1  2008-04-18  M Chinnaswamy Stadium  ...       0           0   222\n",
              "1    1  2008-04-18  M Chinnaswamy Stadium  ...       0           0   222\n",
              "2    1  2008-04-18  M Chinnaswamy Stadium  ...       0           0   222\n",
              "3    1  2008-04-18  M Chinnaswamy Stadium  ...       0           0   222\n",
              "4    1  2008-04-18  M Chinnaswamy Stadium  ...       0           0   222\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
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          "execution_count": 6
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "srvJazxud7BB",
        "colab_type": "text"
      },
      "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/>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t3w2hRVbekfq",
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        "outputId": "dbc88fd6-19ed-43b1-94b2-8c19bb9bbd6a"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "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')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "bOyVrvsSdyFx",
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          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "4a70f21e-7f2d-439a-bfb1-d0f7037cca49"
      },
      "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))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before removing unwanted columns: (76014, 15)\n",
            "After removing unwanted columns: (76014, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VeYhEaB6fQdC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "93c091c8-16ce-47be-a79b-46e5dbe1f691"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'bat_team', 'bowl_team', 'runs', 'wickets', 'overs',\n",
              "       'runs_last_5', 'wickets_last_5', 'total'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCfuyal8hvXt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "b128f1c8-dd1d-4283-bd84-f371fc50982b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>bat_team</th>\n",
              "      <th>bowl_team</th>\n",
              "      <th>runs</th>\n",
              "      <th>wickets</th>\n",
              "      <th>overs</th>\n",
              "      <th>runs_last_5</th>\n",
              "      <th>wickets_last_5</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date               bat_team  ... wickets_last_5  total\n",
              "0  2008-04-18  Kolkata Knight Riders  ...              0    222\n",
              "1  2008-04-18  Kolkata Knight Riders  ...              0    222\n",
              "2  2008-04-18  Kolkata Knight Riders  ...              0    222\n",
              "3  2008-04-18  Kolkata Knight Riders  ...              0    222\n",
              "4  2008-04-18  Kolkata Knight Riders  ...              0    222\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2jQTWJhmIrt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "23463ddc-6c24-43f7-d60d-f43f64ad1a6b"
      },
      "source": [
        "df.index"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=76014, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HD-7eP27iWab",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "2477db91-0a4e-41e3-9689-2abf870a2d4d"
      },
      "source": [
        "df['bat_team'].unique()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',\n",
              "       'Mumbai Indians', 'Deccan Chargers', 'Kings XI Punjab',\n",
              "       'Royal Challengers Bangalore', 'Delhi Daredevils',\n",
              "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Sunrisers Hyderabad',\n",
              "       'Rising Pune Supergiants', 'Gujarat Lions',\n",
              "       'Rising Pune Supergiant'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcC_Crmhih1r",
        "colab_type": "code",
        "colab": {}
      },
      "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']"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXmAt3Colmdu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "9874be5e-a71f-418a-8d90-45268c6c634f"
      },
      "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))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before removing inconsistent teams: (76014, 9)\n",
            "After removing inconsistent teams: (53811, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AB8uOLpHoKtB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "89633380-9611-4552-e964-2d7fdbe8839c"
      },
      "source": [
        "df['bat_team'].unique()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVouIS8QqFwX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "5cf9861c-e3e4-41c4-937e-bf1de77c6556"
      },
      "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))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before removing first 5 overs data: (53811, 9)\n",
            "After removing first 5 overs data: (40108, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_R7UNqkf3Mt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e2383f98-4771-41d9-c6a4-ebd96a7aa575"
      },
      "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])))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "try0O4SSxjBE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "outputId": "3bde5d22-653f-49a4-965b-f992b2a6374d"
      },
      "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')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9Qt4bOGpKh3",
        "colab_type": "text"
      },
      "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",
      "metadata": {
        "id": "ZpJLe1YVfrr6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "ced0b921-0d9e-4f51-f3ce-841d16fcff0f"
      },
      "source": [
        "# Converting categorical features using OneHotEncoding method\n",
        "encoded_df = pd.get_dummies(data=df, columns=['bat_team', 'bowl_team'])\n",
        "encoded_df.columns"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5',\n",
              "       'total', 'bat_team_Chennai Super Kings', 'bat_team_Delhi Daredevils',\n",
              "       'bat_team_Kings XI Punjab', 'bat_team_Kolkata Knight Riders',\n",
              "       'bat_team_Mumbai Indians', 'bat_team_Rajasthan Royals',\n",
              "       'bat_team_Royal Challengers Bangalore', 'bat_team_Sunrisers Hyderabad',\n",
              "       'bowl_team_Chennai Super Kings', 'bowl_team_Delhi Daredevils',\n",
              "       'bowl_team_Kings XI Punjab', 'bowl_team_Kolkata Knight Riders',\n",
              "       'bowl_team_Mumbai Indians', 'bowl_team_Rajasthan Royals',\n",
              "       'bowl_team_Royal Challengers Bangalore',\n",
              "       'bowl_team_Sunrisers Hyderabad'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtrtRO7AiLPz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "925d1844-a1b0-49ac-bced-79940195d950"
      },
      "source": [
        "encoded_df.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>bat_team_Kolkata Knight Riders</th>\n",
              "      <th>bat_team_Mumbai Indians</th>\n",
              "      <th>bat_team_Rajasthan Royals</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>1</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>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>1</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>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>1</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>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>1</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>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>1</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...  bowl_team_Sunrisers Hyderabad\n",
              "32 2008-04-18  ...                              0\n",
              "33 2008-04-18  ...                              0\n",
              "34 2008-04-18  ...                              0\n",
              "35 2008-04-18  ...                              0\n",
              "36 2008-04-18  ...                              0\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVj9eyGJj5-J",
        "colab_type": "code",
        "colab": {}
      },
      "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']]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8raq2VVXeHyr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c89b9399-6397-438f-cd3c-3e7ca0ae63ee"
      },
      "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))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training set: (37330, 21) and Test set: (2778, 21)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRZoptrOlsT5",
        "colab_type": "text"
      },
      "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": {
        "id": "w5NJZyB8oFEw",
        "colab_type": "text"
      },
      "source": [
        "### *Linear Regression*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAbGSLrVln6Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d33e0b5f-511c-42dd-f4e3-da8f8b6b2610"
      },
      "source": [
        "# Linear Regression Model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "linear_regressor = LinearRegression()\n",
        "linear_regressor.fit(X_train,y_train)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeKomR6-nfaX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predicting results\n",
        "y_pred_lr = linear_regressor.predict(X_test)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xRPc6nsmmlbo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "24f2c7ee-a0dd-43c3-9efc-ba1d0856cad7"
      },
      "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))))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---- Linear Regression - Model Evaluation ----\n",
            "Mean Absolute Error (MAE): 12.11861754619329\n",
            "Mean Squared Error (MSE): 251.00792310417438\n",
            "Root Mean Squared Error (RMSE): 15.843229566732106\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fuPztrQQoLNg"
      },
      "source": [
        "### *Decision Tree*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drFWmrvBoC6x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "83a98e88-4307-4bbd-f941-74bb30dc5170"
      },
      "source": [
        "# Decision Tree Regression Model\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "decision_regressor = DecisionTreeRegressor()\n",
        "decision_regressor.fit(X_train,y_train)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCl-LXmpofeq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predicting results\n",
        "y_pred_dt = decision_regressor.predict(X_test)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fxQBkteHoj2Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "bc4f87bf-0ce4-44fb-c957-11f85d3526e0"
      },
      "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))))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---- Decision Tree Regression - Model Evaluation ----\n",
            "Mean Absolute Error (MAE): 17.08963282937365\n",
            "Mean Squared Error (MSE): 531.0550755939524\n",
            "Root Mean Squared Error (RMSE): 23.044632251219642\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "scpqVJxCpFyB"
      },
      "source": [
        "### *Random Forest*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNNGZMlRpKnq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "8d67934e-f177-4270-b2ba-93feba78fef6"
      },
      "source": [
        "# Random Forest Regression Model\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "random_regressor = RandomForestRegressor()\n",
        "random_regressor.fit(X_train,y_train)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UpqtMy1jpYTd",
        "colab": {}
      },
      "source": [
        "# Predicting results\n",
        "y_pred_rf = random_regressor.predict(X_test)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPsdbFk_pdrH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "ad050bf7-70e5-4df5-a7ca-7fc0700cf7d5"
      },
      "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))))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---- Random Forest Regression - Model Evaluation ----\n",
            "Mean Absolute Error (MAE): 13.76117836573074\n",
            "Mean Squared Error (MSE): 330.21283012058035\n",
            "Root Mean Squared Error (RMSE): 18.171759136654337\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMd5-w5Tpv-Y",
        "colab_type": "text"
      },
      "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",
      "metadata": {
        "colab_type": "code",
        "id": "eua4hjEus0W2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "2290f73b-67f2-4551-a77a-7f2942e465c4"
      },
      "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)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostRegressor(base_estimator=LinearRegression(copy_X=True,\n",
              "                                                  fit_intercept=True,\n",
              "                                                  n_jobs=None,\n",
              "                                                  normalize=False),\n",
              "                  learning_rate=1.0, loss='linear', n_estimators=100,\n",
              "                  random_state=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBRmYqGvtdaJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predicting results\n",
        "y_pred_adb = adb_regressor.predict(X_test)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67pZWWEKtgiF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "709cb77d-a0eb-43f5-aded-3c16ff2d0c98"
      },
      "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))))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---- AdaBoost Regression - Model Evaluation ----\n",
            "Mean Absolute Error (MAE): 12.217362349360771\n",
            "Mean Squared Error (MSE): 249.6020058588232\n",
            "Root Mean Squared Error (RMSE): 15.798797608008757\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbvk3I7ZvVG4",
        "colab_type": "text"
      },
      "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": {
        "id": "2Xl7f4FQybFg",
        "colab_type": "text"
      },
      "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",
      "metadata": {
        "id": "-C4QKIa-yhYB",
        "colab_type": "code",
        "colab": {}
      },
      "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])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lRZQW-YGS141",
        "colab_type": "text"
      },
      "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",
      "metadata": {
        "id": "fhgissiE10Bx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "94bfaafb-6305-4187-c813-9c2c9af0a3d9"
      },
      "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))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The final predicted score (range): 159 to 174\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "eVtlLk3afeDT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "62562997-ce49-41c5-8341-430965293e35"
      },
      "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))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The final predicted score (range): 138 to 153\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "CVgb01MY29NQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a1a3dc25-0ba3-489d-ff65-a4a3c4829c22"
      },
      "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))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The final predicted score (range): 180 to 195\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "FUWG9gsviudD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eaf6b5c4-d1ef-4f13-9ba4-8457fbe6dd5d"
      },
      "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))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The final predicted score (range): 179 to 194\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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",
      "metadata": {
        "colab_type": "code",
        "id": "hByMrV6l29YV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1cff54c2-0e7b-46f9-dd02-4437bd0d4028"
      },
      "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))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The final predicted score (range): 128 to 143\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "dAmNR2WLhD2F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f6d18cd0-5eeb-4999-a6d9-9b303324fa2a"
      },
      "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))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The final predicted score (range): 157 to 172\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "UR6QNPK_aSSj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1afc6b5b-d1bc-4f55-9c61-362e31cb4e80"
      },
      "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))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The final predicted score (range): 137 to 152\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZaEK5rEmjp8K",
        "colab_type": "text"
      },
      "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"
      ]
    }
  ]
}