IPL_Analysis.ipynb

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    "colab": {
      "name": "IPL Analysis.ipynb",
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      "authorship_tag": "ABX9TyM7bsg3lPiv+Bzq3berZ8VU",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/smasterparth/Colab-Project/blob/master/IPL_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rl4caGe-eM2H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ao2H07HffFvs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/anujvyas/IPL-First-Innings-Score-Prediction-Deployment/master/ipl.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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          "height": 306
        },
        "outputId": "94c98bd5-7ccc-437d-9073-d21ae5a59343"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8135E3hJfF48",
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          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6ba6a520-f9b3-4c88-951e-ae3078b08780"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(76014, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgB-kHMnfF3l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1fe17661-10c2-484a-ae08-c0969e1446d9"
      },
      "source": [
        "df.isnull().values.any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDh_pmfYlCgD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "columns_to_remove = ['mid' , 'venue' , 'batsman' , 'bowler', 'striker','non-striker']\n",
        "df.drop(labels=columns_to_remove, axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcKbY9WJlCsa",
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        },
        "outputId": "124e11ab-4cc7-4a6a-ca10-836b253d88a7"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lO-vIx8lCvZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c957ae8-a5d9-4940-859a-9d3d89599eef"
      },
      "source": [
        "df['bat_team'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZt-t5UnlCeE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "5cbb6f22-1789-438f-ba07-51a389631f7a"
      },
      "source": [
        "list(df['bat_team'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Kolkata Knight Riders',\n",
              " 'Chennai Super Kings',\n",
              " 'Rajasthan Royals',\n",
              " 'Mumbai Indians',\n",
              " 'Deccan Chargers',\n",
              " 'Kings XI Punjab',\n",
              " 'Royal Challengers Bangalore',\n",
              " 'Delhi Daredevils',\n",
              " 'Kochi Tuskers Kerala',\n",
              " 'Pune Warriors',\n",
              " 'Sunrisers Hyderabad',\n",
              " 'Rising Pune Supergiants',\n",
              " 'Gujarat Lions',\n",
              " 'Rising Pune Supergiant']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnwInIbslCbj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "consistent_teams = ['Kolkata Knight Riders','Chennai Super Kings','Rajasthan Royals','Mumbai Indians','Kings XI Punjab','Royal Challengers Bangalore','Delhi Daredevils', 'Sunrisers Hyderabad']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVaIlxYNlCXw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df[(df['bat_team'].isin(consistent_teams)) & (df['bowl_team'].isin(consistent_teams))]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hp9WbxnVlCVQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5483f93d-1ed6-4a96-cedc-0655c94757ea"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <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",
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              "      <th>wickets_last_5</th>\n",
              "      <th>total</th>\n",
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              "  </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": 12
        }
      ]
    },
    {
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        "outputId": "c1a8e694-d570-40ad-b86e-8aa5653e50b7"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(53811, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfdrURtHfFzL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b8767405-2739-44b3-c6cd-74853f4223cc"
      },
      "source": [
        "df['bat_team'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9680EMW8mxzk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c943155f-c059-4401-fc1d-e7016f2d6fab"
      },
      "source": [
        "df['bowl_team'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
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        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b1185d6b-b177-4c56-ce02-e7e9ac22f7ee"
      },
      "source": [
        "df['total'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "246"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OvJtxgsemyyA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b69a9e7a-8b79-4263-84af-9dc1fd15ee86"
      },
      "source": [
        "df['total'].min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "67"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "8D0PJVJ1myvE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a7923bf4-76dc-4589-8388-36efd3c2c812"
      },
      "source": [
        "df['runs_last_5'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "94"
            ]
          },
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            "tags": []
          },
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    {
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        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f301a7c9-8974-4811-a1f4-d9264c24eed6"
      },
      "source": [
        "df['runs_last_5'].min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCiA-7OKmynk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "627b1fe5-b852-49d8-f9de-9a878d57ecf2"
      },
      "source": [
        "df.isnull().values.any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8NY_j2PmykT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df= df[df['overs']>=5.0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHKNxA8RnGnq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3edcbff4-6fc6-46e1-a119-3e874f4264ab"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40108, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "50lrqW_rnHci",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "98cba966-19f1-4427-9885-cc380b3dbea4"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>bat_team</th>\n",
              "      <th>bowl_team</th>\n",
              "      <th>runs</th>\n",
              "      <th>wickets</th>\n",
              "      <th>overs</th>\n",
              "      <th>runs_last_5</th>\n",
              "      <th>wickets_last_5</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>59</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>5.2</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>5.3</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>5.5</td>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          date               bat_team  ... wickets_last_5  total\n",
              "32  2008-04-18  Kolkata Knight Riders  ...              0    222\n",
              "33  2008-04-18  Kolkata Knight Riders  ...              1    222\n",
              "34  2008-04-18  Kolkata Knight Riders  ...              1    222\n",
              "35  2008-04-18  Kolkata Knight Riders  ...              1    222\n",
              "36  2008-04-18  Kolkata Knight Riders  ...              1    222\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yV6lO7hrnIpC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "76cc3293-e446-4b9b-c44c-80a06cbe5e65"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date               object\n",
              "bat_team           object\n",
              "bowl_team          object\n",
              "runs                int64\n",
              "wickets             int64\n",
              "overs             float64\n",
              "runs_last_5         int64\n",
              "wickets_last_5      int64\n",
              "total               int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mQi7sgXnIld",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from datetime import datetime"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-mMfUHyZnHT3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['date'] = df['date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8hfJem8OnHQ-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5cd5eb38-250f-45c5-9d9d-4da28450894f"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>bat_team</th>\n",
              "      <th>bowl_team</th>\n",
              "      <th>runs</th>\n",
              "      <th>wickets</th>\n",
              "      <th>overs</th>\n",
              "      <th>runs_last_5</th>\n",
              "      <th>wickets_last_5</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>59</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>5.2</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>5.3</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>5.5</td>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date               bat_team  ... wickets_last_5  total\n",
              "32 2008-04-18  Kolkata Knight Riders  ...              0    222\n",
              "33 2008-04-18  Kolkata Knight Riders  ...              1    222\n",
              "34 2008-04-18  Kolkata Knight Riders  ...              1    222\n",
              "35 2008-04-18  Kolkata Knight Riders  ...              1    222\n",
              "36 2008-04-18  Kolkata Knight Riders  ...              1    222\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKWnPE_InHOR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Converting categorical features into Numerical features using OneHotEncoding "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJue6NEanHLf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_encoded = pd.get_dummies(data = df, columns = ['bat_team', 'bowl_team'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgNr9NbgnHI4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "e4aaf35a-cb47-439d-9470-f5089d68b7b8"
      },
      "source": [
        "df_encoded.head()"
      ],
      "execution_count": null,
      "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",
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              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <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",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "  </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]"
            ]
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      "execution_count": null,
      "outputs": [
        {
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          "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')"
            ]
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            "tags": []
          },
          "execution_count": 31
        }
      ]
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    {
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      "metadata": {
        "id": "uhoR2aghnHGO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_encoded = df_encoded[['date','bat_team_Chennai Super Kings', 'bat_team_Delhi Daredevils','bat_team_Kings XI Punjab', 'bat_team_Kolkata Knight Riders','bat_team_Mumbai Indians', 'bat_team_Rajasthan Royals','bat_team_Royal Challengers Bangalore', 'bat_team_Sunrisers Hyderabad','bowl_team_Chennai Super Kings', 'bowl_team_Delhi Daredevils','bowl_team_Kings XI Punjab', 'bowl_team_Kolkata Knight Riders','bowl_team_Mumbai Indians', 'bowl_team_Rajasthan Royals','bowl_team_Royal Challengers Bangalore','bowl_team_Sunrisers Hyderabad', 'runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5','total']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wunocc41nHDW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "fedf5f9c-3f5a-4718-c2b0-df56fd399e68"
      },
      "source": [
        "df_encoded.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "      <th>bowl_team_Delhi Daredevils</th>\n",
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              "      <th>bowl_team_Royal Challengers Bangalore</th>\n",
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              "      <th>runs</th>\n",
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              "      <th>runs_last_5</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>32</th>\n",
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              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <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",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>5.3</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>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",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2008-04-18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>222</td>\n",
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            "text/plain": [
              "         date  bat_team_Chennai Super Kings  ...  wickets_last_5  total\n",
              "32 2008-04-18                             0  ...               0    222\n",
              "33 2008-04-18                             0  ...               1    222\n",
              "34 2008-04-18                             0  ...               1    222\n",
              "35 2008-04-18                             0  ...               1    222\n",
              "36 2008-04-18                             0  ...               1    222\n",
              "\n",
              "[5 rows x 23 columns]"
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      "metadata": {
        "id": "v0xWvUf6nG_W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = df_encoded.drop('total' , axis = 1)[df_encoded['date'].dt.year <= 2016]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNxDsF7TnG7E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = df_encoded.drop('total', axis=1)[df_encoded['date'].dt.year >= 2017]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGiO-HNznG4o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train = df_encoded[df_encoded['date'].dt.year <= 2016]['total']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5U6GzJJnG2U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_test = df_encoded[df_encoded['date'].dt.year >= 2017]['total']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8Vb1k7vnGy6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "2baf15e3-cef3-45f1-c54d-7f72fc2500cd"
      },
      "source": [
        "X_train.tail()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>68857</th>\n",
              "      <td>2016-05-29</td>\n",
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              "      <td>2016-05-29</td>\n",
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              "      <td>19.3</td>\n",
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              "      <td>2016-05-29</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>201</td>\n",
              "      <td>7</td>\n",
              "      <td>19.4</td>\n",
              "      <td>56</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>68860</th>\n",
              "      <td>2016-05-29</td>\n",
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              "      <td>2016-05-29</td>\n",
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              "      <td>62</td>\n",
              "      <td>4</td>\n",
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            ],
            "text/plain": [
              "            date  bat_team_Chennai Super Kings  ...  runs_last_5  wickets_last_5\n",
              "68857 2016-05-29                             0  ...           54               4\n",
              "68858 2016-05-29                             0  ...           56               4\n",
              "68859 2016-05-29                             0  ...           56               4\n",
              "68860 2016-05-29                             0  ...           57               4\n",
              "68861 2016-05-29                             0  ...           62               4\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VX3gF2ZxL1cP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "10707d26-56f7-4203-aa50-8bf492d4d54e"
      },
      "source": [
        "y_train.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "68857    208\n",
              "68858    208\n",
              "68859    208\n",
              "68860    208\n",
              "68861    208\n",
              "Name: total, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltLQgJuFL1YJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train.drop('date',axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EI-18LyL1UA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test.drop('date',axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuPH-HaGLd5Y",
        "colab_type": "text"
      },
      "source": [
        "# Model Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tuYRasXnGt0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mQ_wejuLcbH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_1 = LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22dw1mTZLcWp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "554e32ba-3c8a-44fc-ad47-a1684a6e5610"
      },
      "source": [
        "model_1.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GU98J0B9MZFO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWi-ADHoLcRF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "filename = 'first-innings-score-lr-model.pkl'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6g0aSPuJnGsR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pickle.dump(model_1, open(filename,'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3NBz770MhEn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_1 = model_1.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgB_0wENMmrU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fbcff2a1-24bc-4463-e202-808f1e06b86b"
      },
      "source": [
        "print(model_1.score(X_train,y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6502133812161469\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COtCGrj_MxZP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TiBLNDYRNMrZ",
        "colab_type": "text"
      },
      "source": [
        "# Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAvh-AAbNTLz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import Ridge"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkDusJn-NTG_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPx0Aj4cNTCF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "params = {'alpha' : [1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40]}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iGE9mHkNS9L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grid = GridSearchCV(estimator=Ridge(), param_grid=params, scoring='neg_mean_squared_error' , cv=5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBR10H0YNS3-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "674a4eb6-61b4-4499-f76b-1cf389fa5446"
      },
      "source": [
        "grid.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.61566e-20): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.41749e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.45442e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.40095e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.62242e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.57111e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                             max_iter=None, normalize=False, random_state=None,\n",
              "                             solver='auto', tol=0.001),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'alpha': [1e-15, 1e-10, 1e-08, 0.001, 0.01, 1, 5, 10,\n",
              "                                   20, 30, 35, 40]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='neg_mean_squared_error', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JMhmyKG3NSyy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "913b6aca-c598-4c13-9997-e7cac25c8838"
      },
      "source": [
        "print(grid.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ridge(alpha=40, copy_X=True, fit_intercept=True, max_iter=None, normalize=False,\n",
            "      random_state=None, solver='auto', tol=0.001)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nmJXmXhNSuF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "59eb464a-a001-4394-e91e-d819a2e81e42"
      },
      "source": [
        "print(grid.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'alpha': 40}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpGWeXFLNSpD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "812bc2df-230b-4f2e-87fd-9bbcfc98e76f"
      },
      "source": [
        "print(grid.best_index_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apvMK-uWNShy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ef701b91-479f-4c04-bffb-93c7a8607052"
      },
      "source": [
        "print(grid.best_score_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-328.41527924879244\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQXmJqyjNP5L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 10^-15 = 1e-15"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjLEYAumOgN5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#GridSearchCV is a Hyperparameter tuning technique\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyVc8ThWOhXE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bgc7w6BmOhRm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "random = RandomizedSearchCV(estimator=Ridge(), param_distributions=params, cv=5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQ0aZDVfOhMD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "d3cedc81-e7d1-4cd6-9f0f-41053589fb76"
      },
      "source": [
        "random.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.41749e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.45442e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.40095e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.62242e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=1.57111e-18): result may not be accurate.\n",
            "  overwrite_a=True).T\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:148: LinAlgWarning: Ill-conditioned matrix (rcond=5.61566e-20): result may not be accurate.\n",
            "  overwrite_a=True).T\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score=nan,\n",
              "                   estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                                   max_iter=None, normalize=False,\n",
              "                                   random_state=None, solver='auto',\n",
              "                                   tol=0.001),\n",
              "                   iid='deprecated', n_iter=10, n_jobs=None,\n",
              "                   param_distributions={'alpha': [1e-15, 1e-10, 1e-08, 0.001,\n",
              "                                                  0.01, 1, 5, 10, 20, 30, 35,\n",
              "                                                  40]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uhq305RIOhJw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e2143198-504c-4fdf-abf4-ab498a7c6384"
      },
      "source": [
        "print(random.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ridge(alpha=40, copy_X=True, fit_intercept=True, max_iter=None, normalize=False,\n",
            "      random_state=None, solver='auto', tol=0.001)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xE5_j8okRW_h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c679c3af-fb3b-4888-c131-5a092cfe63ee"
      },
      "source": [
        "print(random.best_score_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6139794729368739\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6p0afSjRZOa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_2 = grid.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFHqTcs1Tb31",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3e0f9f6a-80a4-4d5c-c596-3283162826aa"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": null,
      "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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQoQJnLGTdli",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "fe459100-c0e2-45d8-8189-11902c8a0994"
      },
      "source": [
        "sns.distplot(y_test - pred_2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2079e9c748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ieJYs4mFTdd3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYF-pWg-TdUx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "11eba238-6e1d-4ba2-86f8-b282bb520ff7"
      },
      "source": [
        "print('MSE: ' , metrics.mean_squared_error(y_test,pred_2))\n",
        "print('MAE: ' , metrics.mean_absolute_error(y_test,pred_2))\n",
        "print('RMSE: ', np.sqrt(metrics.mean_squared_error(y_test,pred_2)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE:  251.03172964112716\n",
            "MAE:  12.117294527005024\n",
            "RMSE:  15.843980864704651\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JidIINemTbw0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ifw4yU1kUxZw",
        "colab_type": "text"
      },
      "source": [
        "# Lasso Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUWRMi3kTbp1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import Lasso"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scCpYNR9Tbiv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_3 = Lasso()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6Ew5Pc8Tbbq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grid_lasso = GridSearchCV(estimator=Lasso(), param_grid=params, cv=5, scoring='neg_mean_squared_error')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WIQ2SMGvTbU3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "outputId": "30f138e0-ecab-4eb1-f52e-2c6b680bf1cf"
      },
      "source": [
        "grid_lasso.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4538752.411627855, tolerance: 2529.9556965945594\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4480012.438691147, tolerance: 2547.038071028663\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4636937.876042261, tolerance: 2667.8126904366486\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4373381.3930394035, tolerance: 2712.3488913976626\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4560416.926436814, tolerance: 2646.143766019281\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 589203.6057024151, tolerance: 2529.9556965945594\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 482835.1628650855, tolerance: 2547.038071028663\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 253600.62487523444, tolerance: 2667.8126904366486\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273554.61997329723, tolerance: 2712.3488913976626\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 328227.2046945626, tolerance: 2646.143766019281\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 589202.6230358146, tolerance: 2529.9556965945594\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 482835.82693805266, tolerance: 2547.038071028663\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 253599.47215746436, tolerance: 2667.8126904366486\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273552.75216391217, tolerance: 2712.3488913976626\n",
            "  positive)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 328228.201526002, tolerance: 2646.143766019281\n",
            "  positive)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=Lasso(alpha=1.0, copy_X=True, fit_intercept=True,\n",
              "                             max_iter=1000, normalize=False, positive=False,\n",
              "                             precompute=False, random_state=None,\n",
              "                             selection='cyclic', tol=0.0001, warm_start=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'alpha': [1e-15, 1e-10, 1e-08, 0.001, 0.01, 1, 5, 10,\n",
              "                                   20, 30, 35, 40]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='neg_mean_squared_error', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3h8Lzsp6TbOS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "8fb169a8-475a-44f2-84e9-ffa3ba6ff5c8"
      },
      "source": [
        "print(grid_lasso.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Lasso(alpha=1, copy_X=True, fit_intercept=True, max_iter=1000, normalize=False,\n",
            "      positive=False, precompute=False, random_state=None, selection='cyclic',\n",
            "      tol=0.0001, warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnOrnOvOTbHr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d5fbf7e5-b273-4e49-ed6f-0ad89d37d65d"
      },
      "source": [
        "print(grid_lasso.best_score_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-320.8221491835716\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVDbzbbFTbBG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_lasso = grid_lasso.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4N-hILhTa6j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0171af3f-8262-4f9e-907b-1aaae94c9d61"
      },
      "source": [
        "print('MSE: ' , metrics.mean_squared_error(y_test,pred_lasso))\n",
        "print('MAE: ' , metrics.mean_absolute_error(y_test,pred_lasso))\n",
        "print('RMSE: ', np.sqrt(metrics.mean_squared_error(y_test,pred_lasso)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE:  262.3653827960697\n",
            "MAE:  12.213583996827493\n",
            "RMSE:  16.197696836157593\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFmiihNKTazy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# lasso works bitslower than Ridge Regression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJqaSPGCTatX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "55c93a99-48af-41aa-b25b-193a59b9bb09"
      },
      "source": [
        "sns.distplot(y_test - pred_lasso)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f20783592e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "re6vxbG1Tam5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5PwHeE-BTagL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqeSuUEpTaYb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2_aVohSTaSH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWx0rbzFTaPq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}