{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "aabfc9b7",
      "metadata": {
        "id": "aabfc9b7"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install numpy==1.22.0 pandas==1.5.3\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFDDgqRzxfTB",
        "outputId": "a652677b-ae2d-4052-d971-3990bc6b0186"
      },
      "id": "SFDDgqRzxfTB",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy==1.22.0 in /usr/local/lib/python3.10/dist-packages (1.22.0)\n",
            "Requirement already satisfied: pandas==1.5.3 in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas==1.5.3) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show numpy\n",
        "!pip show pandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGPxBWkrxkct",
        "outputId": "d01503b5-51fa-43c5-c674-2f20340ee904"
      },
      "id": "FGPxBWkrxkct",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: numpy\n",
            "Version: 1.22.0\n",
            "Summary: NumPy is the fundamental package for array computing with Python.\n",
            "Home-page: https://www.numpy.org\n",
            "Author: Travis E. Oliphant et al.\n",
            "Author-email: \n",
            "License: BSD\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: \n",
            "Required-by: albumentations, altair, arviz, astropy, autograd, blis, bokeh, bqplot, category-encoders, chex, cmdstanpy, contourpy, cudf-cu12, cufflinks, cupy-cuda12x, cvxpy, datascience, db-dtypes, dopamine_rl, ecos, flax, folium, geemap, gensim, gym, h5py, holoviews, hyperopt, ibis-framework, imageio, imbalanced-learn, imgaug, jax, jaxlib, librosa, lightgbm, matplotlib, matplotlib-venn, missingno, mizani, ml-dtypes, mlxtend, moviepy, music21, nibabel, numba, numexpr, opencv-contrib-python, opencv-python, opencv-python-headless, opt-einsum, optax, orbax-checkpoint, osqp, pandas, pandas-gbq, pandas-stubs, patsy, plotnine, prophet, pyarrow, pycocotools, pyerfa, pymc, pytensor, python-louvain, PyWavelets, qdldl, qudida, rmm-cu12, scikit-image, scikit-learn, scipy, scs, seaborn, shapely, sklearn-pandas, soxr, spacy, stanio, statsmodels, tables, tensorboard, tensorflow, tensorflow-datasets, tensorflow-hub, tensorflow-probability, tensorstore, thinc, tifffile, torchtext, torchvision, transformers, wordcloud, xarray, xarray-einstats, xgboost, yellowbrick, yfinance\n",
            "Name: pandas\n",
            "Version: 1.5.3\n",
            "Summary: Powerful data structures for data analysis, time series, and statistics\n",
            "Home-page: https://pandas.pydata.org\n",
            "Author: The Pandas Development Team\n",
            "Author-email: pandas-dev@python.org\n",
            "License: BSD-3-Clause\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: numpy, python-dateutil, pytz\n",
            "Required-by: altair, arviz, bigframes, bokeh, bqplot, category-encoders, cmdstanpy, cudf-cu12, cufflinks, datascience, db-dtypes, dopamine_rl, fastai, geemap, geopandas, google-colab, gspread-dataframe, holoviews, ibis-framework, mizani, mlxtend, pandas-datareader, pandas-gbq, panel, plotnine, prophet, pymc, seaborn, sklearn-pandas, statsmodels, vega-datasets, xarray, yfinance\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "079ed1b4",
      "metadata": {
        "id": "079ed1b4"
      },
      "outputs": [],
      "source": [
        "match = pd.read_csv('/content/matches.csv')\n",
        "delivery = pd.read_csv('/content/deliveries.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "bfadbf7d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "bfadbf7d",
        "outputId": "d1a2bb15-e98c-44bb-a21a-ffabac7b4aab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id    Season       city        date                        team1  \\\n",
              "0   1  IPL-2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
              "1   2  IPL-2017       Pune  06-04-2017               Mumbai Indians   \n",
              "2   3  IPL-2017     Rajkot  07-04-2017                Gujarat Lions   \n",
              "3   4  IPL-2017     Indore  08-04-2017       Rising Pune Supergiant   \n",
              "4   5  IPL-2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
              "\n",
              "                         team2                  toss_winner toss_decision  \\\n",
              "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
              "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
              "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
              "3              Kings XI Punjab              Kings XI Punjab         field   \n",
              "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
              "\n",
              "   result  dl_applied                       winner  win_by_runs  \\\n",
              "0  normal           0          Sunrisers Hyderabad           35   \n",
              "1  normal           0       Rising Pune Supergiant            0   \n",
              "2  normal           0        Kolkata Knight Riders            0   \n",
              "3  normal           0              Kings XI Punjab            0   \n",
              "4  normal           0  Royal Challengers Bangalore           15   \n",
              "\n",
              "   win_by_wickets player_of_match                                      venue  \\\n",
              "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
              "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
              "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
              "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
              "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
              "\n",
              "          umpire1        umpire2 umpire3  \n",
              "0     AY Dandekar       NJ Llong     NaN  \n",
              "1  A Nand Kishore         S Ravi     NaN  \n",
              "2     Nitin Menon      CK Nandan     NaN  \n",
              "3    AK Chaudhary  C Shamshuddin     NaN  \n",
              "4             NaN            NaN     NaN  "
            ],
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              "      <td>IPL-2017</td>\n",
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              "      <td>08-04-2017</td>\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b0377e22-749c-4ec5-8a99-f9057b4d7adb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "match",
              "summary": "{\n  \"name\": \"match\",\n  \"rows\": 756,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3464,\n        \"min\": 1,\n        \"max\": 11415,\n        \"num_unique_values\": 756,\n        \"samples\": [\n          409,\n          98,\n          425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Season\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"IPL-2018\",\n          \"IPL-2016\",\n          \"IPL-2017\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"Sharjah\",\n          \"Centurion\",\n          \"Kochi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 546,\n        \"samples\": [\n          \"26-05-2013\",\n          \"20-05-2008\",\n          \"10-04-2015\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"team1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Rajasthan Royals\",\n          \"Kochi Tuskers Kerala\",\n          \"Sunrisers Hyderabad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"team2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Chennai Super Kings\",\n          \"Pune Warriors\",\n          \"Royal Challengers Bangalore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"toss_winner\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Rajasthan Royals\",\n          \"Kochi Tuskers Kerala\",\n          \"Royal Challengers Bangalore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"toss_decision\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bat\",\n          \"field\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"result\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"normal\",\n          \"tie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dl_applied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"winner\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Rajasthan Royals\",\n          \"Pune Warriors\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"win_by_runs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 0,\n        \"max\": 146,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          53,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"win_by_wickets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"player_of_match\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 226,\n        \"samples\": [\n          \"JJ Bumrah\",\n          \"MA Agarwal\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"Barabati Stadium\",\n          \"Dr DY Patil Sports Academy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"umpire1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 61,\n        \"samples\": [\n          \"AY Dandekar\",\n          \"KN Ananthapadmanabhan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"umpire2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 65,\n        \"samples\": [\n          \"O Nandan\",\n          \"Nanda Kishore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"umpire3\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Chris Gaffaney\",\n          \"Marais Erasmus\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "match.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "d4616531",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4616531",
        "outputId": "5a409bd2-6f6a-440b-e7f7-93927fb7c8a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(756, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "match.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "b9576f6a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "b9576f6a",
        "outputId": "3ea808cf-236d-43a1-d013-c948f6e75ac2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   match_id  inning         batting_team                 bowling_team  over  \\\n",
              "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
              "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
              "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
              "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
              "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
              "\n",
              "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
              "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
              "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
              "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
              "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
              "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
              "\n",
              "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
              "0            0            0             0             0           0   \n",
              "1            0            0             0             0           0   \n",
              "2            0            0             0             4           0   \n",
              "3            0            0             0             0           0   \n",
              "4            0            0             0             0           2   \n",
              "\n",
              "   total_runs  player_dismissed dismissal_kind fielder  \n",
              "0           0               NaN            NaN     NaN  \n",
              "1           0               NaN            NaN     NaN  \n",
              "2           4               NaN            NaN     NaN  \n",
              "3           0               NaN            NaN     NaN  \n",
              "4           2               NaN            NaN     NaN  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2253e335-4363-4a0c-89e1-8ec669970293\" class=\"colab-df-container\">\n",
              "    <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",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>match_id</th>\n",
              "      <th>inning</th>\n",
              "      <th>batting_team</th>\n",
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              "      <th>over</th>\n",
              "      <th>ball</th>\n",
              "      <th>batsman</th>\n",
              "      <th>non_striker</th>\n",
              "      <th>bowler</th>\n",
              "      <th>is_super_over</th>\n",
              "      <th>...</th>\n",
              "      <th>bye_runs</th>\n",
              "      <th>legbye_runs</th>\n",
              "      <th>noball_runs</th>\n",
              "      <th>penalty_runs</th>\n",
              "      <th>batsman_runs</th>\n",
              "      <th>extra_runs</th>\n",
              "      <th>total_runs</th>\n",
              "      <th>player_dismissed</th>\n",
              "      <th>dismissal_kind</th>\n",
              "      <th>fielder</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>DA Warner</td>\n",
              "      <td>S Dhawan</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
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              "      <td>2</td>\n",
              "      <td>DA Warner</td>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>TS Mills</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>DA Warner</td>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>TS Mills</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>DA Warner</td>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>TS Mills</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>DA Warner</td>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>TS Mills</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2253e335-4363-4a0c-89e1-8ec669970293')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2253e335-4363-4a0c-89e1-8ec669970293 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2253e335-4363-4a0c-89e1-8ec669970293');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
              "<div id=\"df-43369dba-4637-47d0-ba45-522addb2f5df\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-43369dba-4637-47d0-ba45-522addb2f5df')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-43369dba-4637-47d0-ba45-522addb2f5df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "delivery"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "delivery.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "be21b391",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "be21b391",
        "outputId": "760b10fd-5c79-4b6b-8317-8b193061dab8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-40e5f6bc616a>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  total_score_df = delivery.groupby(['match_id','inning']).sum()['total_runs'].reset_index()\n"
          ]
        }
      ],
      "source": [
        "total_score_df = delivery.groupby(['match_id','inning']).sum()['total_runs'].reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "cbf8c553",
      "metadata": {
        "id": "cbf8c553"
      },
      "outputs": [],
      "source": [
        "total_score_df = total_score_df[total_score_df['inning'] == 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "0e59930d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "0e59930d",
        "outputId": "31c0886d-c4a5-41d2-8a9c-7e7c81afffe4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      match_id  inning  total_runs\n",
              "0            1       1         207\n",
              "2            2       1         184\n",
              "4            3       1         183\n",
              "6            4       1         163\n",
              "8            5       1         157\n",
              "...        ...     ...         ...\n",
              "1518     11347       1         143\n",
              "1520     11412       1         136\n",
              "1522     11413       1         171\n",
              "1524     11414       1         155\n",
              "1526     11415       1         152\n",
              "\n",
              "[756 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-15829df6-995f-4312-a390-9d23938a3d03\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>match_id</th>\n",
              "      <th>inning</th>\n",
              "      <th>total_runs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>207</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>184</td>\n",
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              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1518</th>\n",
              "      <td>11347</td>\n",
              "      <td>1</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1520</th>\n",
              "      <td>11412</td>\n",
              "      <td>1</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1522</th>\n",
              "      <td>11413</td>\n",
              "      <td>1</td>\n",
              "      <td>171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1524</th>\n",
              "      <td>11414</td>\n",
              "      <td>1</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1526</th>\n",
              "      <td>11415</td>\n",
              "      <td>1</td>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>756 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-15829df6-995f-4312-a390-9d23938a3d03')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-15829df6-995f-4312-a390-9d23938a3d03 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-15829df6-995f-4312-a390-9d23938a3d03');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-310d1813-14e6-4285-891c-7e87449c9eaf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ba32f557-2327-4b35-abb3-96ff67445ce4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('total_score_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ba32f557-2327-4b35-abb3-96ff67445ce4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('total_score_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "total_score_df",
              "summary": "{\n  \"name\": \"total_score_df\",\n  \"rows\": 756,\n  \"fields\": [\n    {\n      \"column\": \"match_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3464,\n        \"min\": 1,\n        \"max\": 11415,\n        \"num_unique_values\": 756,\n        \"samples\": [\n          409,\n          98,\n          425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"inning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_runs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30,\n        \"min\": 56,\n        \"max\": 263,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "total_score_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "78c81c64",
      "metadata": {
        "id": "78c81c64"
      },
      "outputs": [],
      "source": [
        "match_df = match.merge(total_score_df[['match_id','total_runs']],left_on='id',right_on='match_id')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "6dad8a91",
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            "text/plain": [
              "        id    Season           city        date                        team1  \\\n",
              "0        1  IPL-2017      Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
              "1        2  IPL-2017           Pune  06-04-2017               Mumbai Indians   \n",
              "2        3  IPL-2017         Rajkot  07-04-2017                Gujarat Lions   \n",
              "3        4  IPL-2017         Indore  08-04-2017       Rising Pune Supergiant   \n",
              "4        5  IPL-2017      Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
              "..     ...       ...            ...         ...                          ...   \n",
              "751  11347  IPL-2019         Mumbai  05-05-2019        Kolkata Knight Riders   \n",
              "752  11412  IPL-2019        Chennai  07-05-2019          Chennai Super Kings   \n",
              "753  11413  IPL-2019  Visakhapatnam  08-05-2019          Sunrisers Hyderabad   \n",
              "754  11414  IPL-2019  Visakhapatnam  10-05-2019               Delhi Capitals   \n",
              "755  11415  IPL-2019      Hyderabad  12-05-2019               Mumbai Indians   \n",
              "\n",
              "                           team2                  toss_winner toss_decision  \\\n",
              "0    Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
              "1         Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
              "2          Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
              "3                Kings XI Punjab              Kings XI Punjab         field   \n",
              "4               Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
              "..                           ...                          ...           ...   \n",
              "751               Mumbai Indians               Mumbai Indians         field   \n",
              "752               Mumbai Indians          Chennai Super Kings           bat   \n",
              "753               Delhi Capitals               Delhi Capitals         field   \n",
              "754          Chennai Super Kings          Chennai Super Kings         field   \n",
              "755          Chennai Super Kings               Mumbai Indians           bat   \n",
              "\n",
              "     result  dl_applied                       winner  win_by_runs  \\\n",
              "0    normal           0          Sunrisers Hyderabad           35   \n",
              "1    normal           0       Rising Pune Supergiant            0   \n",
              "2    normal           0        Kolkata Knight Riders            0   \n",
              "3    normal           0              Kings XI Punjab            0   \n",
              "4    normal           0  Royal Challengers Bangalore           15   \n",
              "..      ...         ...                          ...          ...   \n",
              "751  normal           0               Mumbai Indians            0   \n",
              "752  normal           0               Mumbai Indians            0   \n",
              "753  normal           0               Delhi Capitals            0   \n",
              "754  normal           0          Chennai Super Kings            0   \n",
              "755  normal           0               Mumbai Indians            1   \n",
              "\n",
              "     win_by_wickets player_of_match  \\\n",
              "0                 0    Yuvraj Singh   \n",
              "1                 7       SPD Smith   \n",
              "2                10         CA Lynn   \n",
              "3                 6      GJ Maxwell   \n",
              "4                 0       KM Jadhav   \n",
              "..              ...             ...   \n",
              "751               9       HH Pandya   \n",
              "752               6        AS Yadav   \n",
              "753               2         RR Pant   \n",
              "754               6    F du Plessis   \n",
              "755               0       JJ Bumrah   \n",
              "\n",
              "                                         venue         umpire1  \\\n",
              "0    Rajiv Gandhi International Stadium, Uppal     AY Dandekar   \n",
              "1      Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
              "2       Saurashtra Cricket Association Stadium     Nitin Menon   \n",
              "3                       Holkar Cricket Stadium    AK Chaudhary   \n",
              "4                        M Chinnaswamy Stadium             NaN   \n",
              "..                                         ...             ...   \n",
              "751                           Wankhede Stadium   Nanda Kishore   \n",
              "752                  M. A. Chidambaram Stadium     Nigel Llong   \n",
              "753                           ACA-VDCA Stadium             NaN   \n",
              "754                           ACA-VDCA Stadium   Sundaram Ravi   \n",
              "755         Rajiv Gandhi Intl. Cricket Stadium     Nitin Menon   \n",
              "\n",
              "            umpire2                  umpire3  match_id  total_runs  \n",
              "0          NJ Llong                      NaN         1         207  \n",
              "1            S Ravi                      NaN         2         184  \n",
              "2         CK Nandan                      NaN         3         183  \n",
              "3     C Shamshuddin                      NaN         4         163  \n",
              "4               NaN                      NaN         5         157  \n",
              "..              ...                      ...       ...         ...  \n",
              "751        O Nandan                   S Ravi     11347         143  \n",
              "752     Nitin Menon                Ian Gould     11412         136  \n",
              "753             NaN                      NaN     11413         171  \n",
              "754  Bruce Oxenford  Chettithody Shamshuddin     11414         155  \n",
              "755       Ian Gould              Nigel Llong     11415         152  \n",
              "\n",
              "[756 rows x 20 columns]"
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              "      <td>10</td>\n",
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              "      <td>Saurashtra Cricket Association Stadium</td>\n",
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              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8e8d2a05-119d-481e-aa52-4fdfebd80d7c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_597729b4-027e-4eb8-b45c-0b0ed567d5a9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('match_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_597729b4-027e-4eb8-b45c-0b0ed567d5a9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('match_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "match_df",
              "summary": "{\n  \"name\": \"match_df\",\n  \"rows\": 756,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3464,\n        \"min\": 1,\n        \"max\": 11415,\n        \"num_unique_values\": 756,\n        \"samples\": [\n          409,\n          98,\n          425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Season\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"IPL-2018\",\n          \"IPL-2016\",\n          \"IPL-2017\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"Sharjah\",\n          \"Centurion\",\n          \"Kochi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 546,\n        \"samples\": [\n          \"26-05-2013\",\n          \"20-05-2008\",\n          \"10-04-2015\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"team1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Rajasthan Royals\",\n          \"Kochi Tuskers Kerala\",\n          \"Sunrisers Hyderabad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"team2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Chennai Super Kings\",\n          \"Pune Warriors\",\n          \"Royal Challengers Bangalore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"toss_winner\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Rajasthan Royals\",\n          \"Kochi Tuskers Kerala\",\n          \"Royal Challengers Bangalore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"toss_decision\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bat\",\n          \"field\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"result\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"normal\",\n          \"tie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dl_applied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"winner\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Rajasthan Royals\",\n          \"Pune Warriors\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"win_by_runs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 0,\n        \"max\": 146,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          53,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"win_by_wickets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"player_of_match\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 226,\n        \"samples\": [\n          \"JJ Bumrah\",\n          \"MA Agarwal\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"Barabati Stadium\",\n          \"Dr DY Patil Sports Academy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"umpire1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 61,\n        \"samples\": [\n          \"AY Dandekar\",\n          \"KN Ananthapadmanabhan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"umpire2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 65,\n        \"samples\": [\n          \"O Nandan\",\n          \"Nanda Kishore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"umpire3\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Chris Gaffaney\",\n          \"Marais Erasmus\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"match_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3464,\n        \"min\": 1,\n        \"max\": 11415,\n        \"num_unique_values\": 756,\n        \"samples\": [\n          409,\n          98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_runs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30,\n        \"min\": 56,\n        \"max\": 263,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          175,\n          159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "match_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "46d110b1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46d110b1",
        "outputId": "a1a1e3a1-a75c-42f6-b686-23e51b1a656b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Sunrisers Hyderabad', 'Mumbai Indians', 'Gujarat Lions',\n",
              "       'Rising Pune Supergiant', 'Royal Challengers Bangalore',\n",
              "       'Kolkata Knight Riders', 'Delhi Daredevils', 'Kings XI Punjab',\n",
              "       'Chennai Super Kings', 'Rajasthan Royals', 'Deccan Chargers',\n",
              "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Rising Pune Supergiants',\n",
              "       'Delhi Capitals'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "match_df['team1'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "9f048dbf",
      "metadata": {
        "id": "9f048dbf"
      },
      "outputs": [],
      "source": [
        "teams = [\n",
        "    'Sunrisers Hyderabad',\n",
        "    'Mumbai Indians',\n",
        "    'Royal Challengers Bangalore',\n",
        "    'Kolkata Knight Riders',\n",
        "    'Kings XI Punjab',\n",
        "    'Chennai Super Kings',\n",
        "    'Rajasthan Royals',\n",
        "    'Delhi Capitals'\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "4ca212ee",
      "metadata": {
        "id": "4ca212ee"
      },
      "outputs": [],
      "source": [
        "match_df['team1'] = match_df['team1'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
        "match_df['team2'] = match_df['team2'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
        "\n",
        "match_df['team1'] = match_df['team1'].str.replace('Deccan Chargers','Sunrisers Hyderabad')\n",
        "match_df['team2'] = match_df['team2'].str.replace('Deccan Chargers','Sunrisers Hyderabad')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "ec3d2992",
      "metadata": {
        "id": "ec3d2992"
      },
      "outputs": [],
      "source": [
        "match_df = match_df[match_df['team1'].isin(teams)]\n",
        "match_df = match_df[match_df['team2'].isin(teams)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "456148f0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "456148f0",
        "outputId": "46a14d66-2a98-4124-d3e4-9a8f1466f640"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(641, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "match_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "82af99c7",
      "metadata": {
        "id": "82af99c7"
      },
      "outputs": [],
      "source": [
        "# Check if the column exists before trying to filter on it.\n",
        "if 'dl_applied' in match_df.columns:\n",
        "    match_df = match_df[match_df['dl_applied'] == 0]\n",
        "else:\n",
        "    print(\"Column 'dl_applied' not found in the DataFrame.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "bb7e68ce",
      "metadata": {
        "id": "bb7e68ce"
      },
      "outputs": [],
      "source": [
        "match_df = match_df[['match_id','city','winner','total_runs']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "cfa8b802",
      "metadata": {
        "id": "cfa8b802"
      },
      "outputs": [],
      "source": [
        "delivery_df = match_df.merge(delivery,on='match_id')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "bb9e3301",
      "metadata": {
        "id": "bb9e3301"
      },
      "outputs": [],
      "source": [
        "delivery_df = delivery_df[delivery_df['inning'] == 2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "ed062c89",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "ed062c89",
        "outputId": "dda09727-05b3-43bb-d849-a77f9c992733"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        match_id       city               winner  total_runs_x  inning  \\\n",
              "125            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "126            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "127            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "128            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "129            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "...          ...        ...                  ...           ...     ...   \n",
              "149573     11415  Hyderabad       Mumbai Indians           152       2   \n",
              "149574     11415  Hyderabad       Mumbai Indians           152       2   \n",
              "149575     11415  Hyderabad       Mumbai Indians           152       2   \n",
              "149576     11415  Hyderabad       Mumbai Indians           152       2   \n",
              "149577     11415  Hyderabad       Mumbai Indians           152       2   \n",
              "\n",
              "                       batting_team         bowling_team  over  ball  \\\n",
              "125     Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
              "126     Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
              "127     Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
              "128     Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
              "129     Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
              "...                             ...                  ...   ...   ...   \n",
              "149573          Chennai Super Kings       Mumbai Indians    20     2   \n",
              "149574          Chennai Super Kings       Mumbai Indians    20     3   \n",
              "149575          Chennai Super Kings       Mumbai Indians    20     4   \n",
              "149576          Chennai Super Kings       Mumbai Indians    20     5   \n",
              "149577          Chennai Super Kings       Mumbai Indians    20     6   \n",
              "\n",
              "              batsman  ... bye_runs legbye_runs  noball_runs  penalty_runs  \\\n",
              "125          CH Gayle  ...        0           0            0             0   \n",
              "126     Mandeep Singh  ...        0           0            0             0   \n",
              "127     Mandeep Singh  ...        0           0            0             0   \n",
              "128     Mandeep Singh  ...        0           0            0             0   \n",
              "129     Mandeep Singh  ...        0           0            0             0   \n",
              "...               ...  ...      ...         ...          ...           ...   \n",
              "149573      RA Jadeja  ...        0           0            0             0   \n",
              "149574      SR Watson  ...        0           0            0             0   \n",
              "149575      SR Watson  ...        0           0            0             0   \n",
              "149576      SN Thakur  ...        0           0            0             0   \n",
              "149577      SN Thakur  ...        0           0            0             0   \n",
              "\n",
              "        batsman_runs  extra_runs  total_runs_y  player_dismissed  \\\n",
              "125                1           0             1               NaN   \n",
              "126                0           0             0               NaN   \n",
              "127                0           0             0               NaN   \n",
              "128                2           0             2               NaN   \n",
              "129                4           0             4               NaN   \n",
              "...              ...         ...           ...               ...   \n",
              "149573             1           0             1               NaN   \n",
              "149574             2           0             2               NaN   \n",
              "149575             1           0             1         SR Watson   \n",
              "149576             2           0             2               NaN   \n",
              "149577             0           0             0         SN Thakur   \n",
              "\n",
              "        dismissal_kind    fielder  \n",
              "125                NaN        NaN  \n",
              "126                NaN        NaN  \n",
              "127                NaN        NaN  \n",
              "128                NaN        NaN  \n",
              "129                NaN        NaN  \n",
              "...                ...        ...  \n",
              "149573             NaN        NaN  \n",
              "149574             NaN        NaN  \n",
              "149575         run out  KH Pandya  \n",
              "149576             NaN        NaN  \n",
              "149577             lbw        NaN  \n",
              "\n",
              "[72413 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7614e2aa-f19a-4863-a6b4-11412568a965\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>match_id</th>\n",
              "      <th>city</th>\n",
              "      <th>winner</th>\n",
              "      <th>total_runs_x</th>\n",
              "      <th>inning</th>\n",
              "      <th>batting_team</th>\n",
              "      <th>bowling_team</th>\n",
              "      <th>over</th>\n",
              "      <th>ball</th>\n",
              "      <th>batsman</th>\n",
              "      <th>...</th>\n",
              "      <th>bye_runs</th>\n",
              "      <th>legbye_runs</th>\n",
              "      <th>noball_runs</th>\n",
              "      <th>penalty_runs</th>\n",
              "      <th>batsman_runs</th>\n",
              "      <th>extra_runs</th>\n",
              "      <th>total_runs_y</th>\n",
              "      <th>player_dismissed</th>\n",
              "      <th>dismissal_kind</th>\n",
              "      <th>fielder</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>207</td>\n",
              "      <td>2</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>CH Gayle</td>\n",
              "      <td>...</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>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>207</td>\n",
              "      <td>2</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Mandeep Singh</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>207</td>\n",
              "      <td>2</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Mandeep Singh</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>207</td>\n",
              "      <td>2</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Mandeep Singh</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>207</td>\n",
              "      <td>2</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Mandeep Singh</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149573</th>\n",
              "      <td>11415</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>152</td>\n",
              "      <td>2</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>RA Jadeja</td>\n",
              "      <td>...</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>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149574</th>\n",
              "      <td>11415</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>152</td>\n",
              "      <td>2</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>SR Watson</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149575</th>\n",
              "      <td>11415</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>152</td>\n",
              "      <td>2</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>SR Watson</td>\n",
              "      <td>...</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>1</td>\n",
              "      <td>SR Watson</td>\n",
              "      <td>run out</td>\n",
              "      <td>KH Pandya</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149576</th>\n",
              "      <td>11415</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>152</td>\n",
              "      <td>2</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>SN Thakur</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149577</th>\n",
              "      <td>11415</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>152</td>\n",
              "      <td>2</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>20</td>\n",
              "      <td>6</td>\n",
              "      <td>SN Thakur</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SN Thakur</td>\n",
              "      <td>lbw</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>72413 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7614e2aa-f19a-4863-a6b4-11412568a965')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7614e2aa-f19a-4863-a6b4-11412568a965 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7614e2aa-f19a-4863-a6b4-11412568a965');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c8d63335-1b70-4d25-ba05-f58056fb5193\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c8d63335-1b70-4d25-ba05-f58056fb5193')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c8d63335-1b70-4d25-ba05-f58056fb5193 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_556a4768-e0e3-4422-99df-37879844b8f3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        google.colab.notebook.generateWithVariable('delivery_df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "delivery_df"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "delivery_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "3a2aed14",
      "metadata": {
        "id": "3a2aed14"
      },
      "outputs": [],
      "source": [
        "# Convert 'total_runs_y' to a numeric type (e.g., integer) before applying cumsum\n",
        "delivery_df['total_runs_y'] = delivery_df['total_runs_y'].astype(int)  # Or float if needed\n",
        "\n",
        "# Now calculate the cumulative sum\n",
        "delivery_df['current_score'] = delivery_df.groupby('match_id')['total_runs_y'].cumsum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "a37ab264",
      "metadata": {
        "id": "a37ab264"
      },
      "outputs": [],
      "source": [
        "delivery_df['runs_left'] = delivery_df['total_runs_x'] - delivery_df['current_score']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "91142ecc",
      "metadata": {
        "id": "91142ecc"
      },
      "outputs": [],
      "source": [
        "delivery_df['balls_left'] = 126 - (delivery_df['over']*6 + delivery_df['ball'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "e49251b7",
      "metadata": {
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          "height": 704
        },
        "id": "e49251b7",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        match_id       city               winner  total_runs_x  inning  \\\n",
              "125            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "126            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "127            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "128            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "129            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "...          ...        ...                  ...           ...     ...   \n",
              "149573     11415  Hyderabad       Mumbai Indians           152       2   \n",
              "149574     11415  Hyderabad       Mumbai Indians           152       2   \n",
              "149575     11415  Hyderabad       Mumbai Indians           152       2   \n",
              "149576     11415  Hyderabad       Mumbai Indians           152       2   \n",
              "149577     11415  Hyderabad       Mumbai Indians           152       2   \n",
              "\n",
              "                       batting_team         bowling_team  over  ball  \\\n",
              "125     Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
              "126     Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
              "127     Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
              "128     Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
              "129     Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
              "...                             ...                  ...   ...   ...   \n",
              "149573          Chennai Super Kings       Mumbai Indians    20     2   \n",
              "149574          Chennai Super Kings       Mumbai Indians    20     3   \n",
              "149575          Chennai Super Kings       Mumbai Indians    20     4   \n",
              "149576          Chennai Super Kings       Mumbai Indians    20     5   \n",
              "149577          Chennai Super Kings       Mumbai Indians    20     6   \n",
              "\n",
              "              batsman  ... penalty_runs batsman_runs  extra_runs  \\\n",
              "125          CH Gayle  ...            0            1           0   \n",
              "126     Mandeep Singh  ...            0            0           0   \n",
              "127     Mandeep Singh  ...            0            0           0   \n",
              "128     Mandeep Singh  ...            0            2           0   \n",
              "129     Mandeep Singh  ...            0            4           0   \n",
              "...               ...  ...          ...          ...         ...   \n",
              "149573      RA Jadeja  ...            0            1           0   \n",
              "149574      SR Watson  ...            0            2           0   \n",
              "149575      SR Watson  ...            0            1           0   \n",
              "149576      SN Thakur  ...            0            2           0   \n",
              "149577      SN Thakur  ...            0            0           0   \n",
              "\n",
              "        total_runs_y  player_dismissed  dismissal_kind    fielder  \\\n",
              "125                1               NaN             NaN        NaN   \n",
              "126                0               NaN             NaN        NaN   \n",
              "127                0               NaN             NaN        NaN   \n",
              "128                2               NaN             NaN        NaN   \n",
              "129                4               NaN             NaN        NaN   \n",
              "...              ...               ...             ...        ...   \n",
              "149573             1               NaN             NaN        NaN   \n",
              "149574             2               NaN             NaN        NaN   \n",
              "149575             1         SR Watson         run out  KH Pandya   \n",
              "149576             2               NaN             NaN        NaN   \n",
              "149577             0         SN Thakur             lbw        NaN   \n",
              "\n",
              "        current_score  runs_left  balls_left  \n",
              "125                 1        206         119  \n",
              "126                 1        206         118  \n",
              "127                 1        206         117  \n",
              "128                 3        204         116  \n",
              "129                 7        200         115  \n",
              "...               ...        ...         ...  \n",
              "149573            152          0           4  \n",
              "149574            154         -2           3  \n",
              "149575            155         -3           2  \n",
              "149576            157         -5           1  \n",
              "149577            157         -5           0  \n",
              "\n",
              "[72413 rows x 27 columns]"
            ],
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              "<p>72413 rows × 27 columns</p>\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "delivery_df"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "delivery_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "5ee97c37",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "5ee97c37",
        "outputId": "5499b493-3e92-4453-beca-0ddd62f0e1f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        match_id  inning         batting_team                 bowling_team  \\\n",
              "0              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
              "1              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
              "2              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
              "3              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
              "4              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
              "...          ...     ...                  ...                          ...   \n",
              "179073     11415       2  Chennai Super Kings               Mumbai Indians   \n",
              "179074     11415       2  Chennai Super Kings               Mumbai Indians   \n",
              "179075     11415       2  Chennai Super Kings               Mumbai Indians   \n",
              "179076     11415       2  Chennai Super Kings               Mumbai Indians   \n",
              "179077     11415       2  Chennai Super Kings               Mumbai Indians   \n",
              "\n",
              "        over  ball    batsman non_striker      bowler  is_super_over  ...  \\\n",
              "0          1     1  DA Warner    S Dhawan    TS Mills              0  ...   \n",
              "1          1     2  DA Warner    S Dhawan    TS Mills              0  ...   \n",
              "2          1     3  DA Warner    S Dhawan    TS Mills              0  ...   \n",
              "3          1     4  DA Warner    S Dhawan    TS Mills              0  ...   \n",
              "4          1     5  DA Warner    S Dhawan    TS Mills              0  ...   \n",
              "...      ...   ...        ...         ...         ...            ...  ...   \n",
              "179073    20     2  RA Jadeja   SR Watson  SL Malinga              0  ...   \n",
              "179074    20     3  SR Watson   RA Jadeja  SL Malinga              0  ...   \n",
              "179075    20     4  SR Watson   RA Jadeja  SL Malinga              0  ...   \n",
              "179076    20     5  SN Thakur   RA Jadeja  SL Malinga              0  ...   \n",
              "179077    20     6  SN Thakur   RA Jadeja  SL Malinga              0  ...   \n",
              "\n",
              "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
              "0              0            0            0             0             0   \n",
              "1              0            0            0             0             0   \n",
              "2              0            0            0             0             4   \n",
              "3              0            0            0             0             0   \n",
              "4              0            0            0             0             0   \n",
              "...          ...          ...          ...           ...           ...   \n",
              "179073         0            0            0             0             1   \n",
              "179074         0            0            0             0             2   \n",
              "179075         0            0            0             0             1   \n",
              "179076         0            0            0             0             2   \n",
              "179077         0            0            0             0             0   \n",
              "\n",
              "        extra_runs  total_runs  player_dismissed dismissal_kind    fielder  \n",
              "0                0           0               NaN            NaN        NaN  \n",
              "1                0           0               NaN            NaN        NaN  \n",
              "2                0           4               NaN            NaN        NaN  \n",
              "3                0           0               NaN            NaN        NaN  \n",
              "4                2           2               NaN            NaN        NaN  \n",
              "...            ...         ...               ...            ...        ...  \n",
              "179073           0           1               NaN            NaN        NaN  \n",
              "179074           0           2               NaN            NaN        NaN  \n",
              "179075           0           1         SR Watson        run out  KH Pandya  \n",
              "179076           0           2               NaN            NaN        NaN  \n",
              "179077           0           0         SN Thakur            lbw        NaN  \n",
              "\n",
              "[179078 rows x 21 columns]"
            ],
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179074</th>\n",
              "      <td>11415</td>\n",
              "      <td>2</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>SR Watson</td>\n",
              "      <td>RA Jadeja</td>\n",
              "      <td>SL Malinga</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179075</th>\n",
              "      <td>11415</td>\n",
              "      <td>2</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>SR Watson</td>\n",
              "      <td>RA Jadeja</td>\n",
              "      <td>SL Malinga</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>SR Watson</td>\n",
              "      <td>run out</td>\n",
              "      <td>KH Pandya</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179076</th>\n",
              "      <td>11415</td>\n",
              "      <td>2</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>SN Thakur</td>\n",
              "      <td>RA Jadeja</td>\n",
              "      <td>SL Malinga</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179077</th>\n",
              "      <td>11415</td>\n",
              "      <td>2</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>20</td>\n",
              "      <td>6</td>\n",
              "      <td>SN Thakur</td>\n",
              "      <td>RA Jadeja</td>\n",
              "      <td>SL Malinga</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SN Thakur</td>\n",
              "      <td>lbw</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>179078 rows × 21 columns</p>\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        width: 32px;\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('delivery')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('delivery');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "delivery"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "delivery_df['player_dismissed'] = delivery_df['player_dismissed'].fillna(\"0\")\n",
        "delivery_df['player_dismissed'] = delivery_df['player_dismissed'].apply(lambda x:x if x == \"0\" else \"1\")\n",
        "delivery_df['player_dismissed'] = delivery_df['player_dismissed'].astype('int')  # Ensure conversion to integer\n",
        "wickets = delivery_df.groupby('match_id')['player_dismissed'].cumsum() # Remove .values, cumsum already returns a Series\n",
        "delivery"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "030b9c43",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "030b9c43",
        "outputId": "6bc62058-c398-43cb-aca4-b15850608469"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     match_id       city               winner  total_runs_x  inning  \\\n",
              "125         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "126         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "127         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "128         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "129         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
              "\n",
              "                    batting_team         bowling_team  over  ball  \\\n",
              "125  Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
              "126  Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
              "127  Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
              "128  Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
              "129  Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
              "\n",
              "           batsman  ... penalty_runs batsman_runs  extra_runs  total_runs_y  \\\n",
              "125       CH Gayle  ...            0            1           0             1   \n",
              "126  Mandeep Singh  ...            0            0           0             0   \n",
              "127  Mandeep Singh  ...            0            0           0             0   \n",
              "128  Mandeep Singh  ...            0            2           0             2   \n",
              "129  Mandeep Singh  ...            0            4           0             4   \n",
              "\n",
              "     player_dismissed  dismissal_kind  fielder  current_score  runs_left  \\\n",
              "125                 0             NaN      NaN              1        206   \n",
              "126                 0             NaN      NaN              1        206   \n",
              "127                 0             NaN      NaN              1        206   \n",
              "128                 0             NaN      NaN              3        204   \n",
              "129                 0             NaN      NaN              7        200   \n",
              "\n",
              "     balls_left  \n",
              "125         119  \n",
              "126         118  \n",
              "127         117  \n",
              "128         116  \n",
              "129         115  \n",
              "\n",
              "[5 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "      <th>total_runs_y</th>\n",
              "      <th>player_dismissed</th>\n",
              "      <th>dismissal_kind</th>\n",
              "      <th>fielder</th>\n",
              "      <th>current_score</th>\n",
              "      <th>runs_left</th>\n",
              "      <th>balls_left</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>125</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
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              "      <td>CH Gayle</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>206</td>\n",
              "      <td>119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>207</td>\n",
              "      <td>2</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Mandeep Singh</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>206</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>207</td>\n",
              "      <td>2</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Mandeep Singh</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>206</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>207</td>\n",
              "      <td>2</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Mandeep Singh</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>204</td>\n",
              "      <td>116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>207</td>\n",
              "      <td>2</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Mandeep Singh</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>200</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab0688ac-d9b7-45f5-9bb3-c37890ffd13e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ab0688ac-d9b7-45f5-9bb3-c37890ffd13e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ab0688ac-d9b7-45f5-9bb3-c37890ffd13e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ca151c85-a11e-4908-8ff2-1ec00613f452\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ca151c85-a11e-4908-8ff2-1ec00613f452')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ca151c85-a11e-4908-8ff2-1ec00613f452 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "delivery_df"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "delivery_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "f9fe60c7",
      "metadata": {
        "id": "f9fe60c7"
      },
      "outputs": [],
      "source": [
        "# crr = runs/overs\n",
        "delivery_df['crr'] = (delivery_df['current_score']*6)/(120 - delivery_df['balls_left'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "7d484dea",
      "metadata": {
        "id": "7d484dea"
      },
      "outputs": [],
      "source": [
        "delivery_df['rrr'] = (delivery_df['runs_left']*6)/delivery_df['balls_left']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "730c19d4",
      "metadata": {
        "id": "730c19d4"
      },
      "outputs": [],
      "source": [
        "def result(row):\n",
        "    return 1 if row['batting_team'] == row['winner'] else 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "a49caf70",
      "metadata": {
        "id": "a49caf70"
      },
      "outputs": [],
      "source": [
        "delivery_df['result'] = delivery_df.apply(result,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "2999909b",
      "metadata": {
        "id": "2999909b"
      },
      "outputs": [],
      "source": [
        "# Add the 'wickets' Series to the delivery_df DataFrame\n",
        "delivery_df['wickets'] = wickets\n",
        "\n",
        "# Now create the final DataFrame\n",
        "final_df = delivery_df[['batting_team','bowling_team','city','runs_left','balls_left','wickets','total_runs_x','crr','rrr','result']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "fb242ffd",
      "metadata": {
        "id": "fb242ffd"
      },
      "outputs": [],
      "source": [
        "final_df = final_df.sample(final_df.shape[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "3dc0b91d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "3dc0b91d",
        "outputId": "908b84ce-4ebe-414a-fef9-f4bf10f007df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 batting_team         bowling_team     city  runs_left  \\\n",
              "135691  Kolkata Knight Riders  Sunrisers Hyderabad  Kolkata         51   \n",
              "\n",
              "        balls_left  wickets  total_runs_x       crr   rrr  result  \n",
              "135691          17        4           189  8.038835  18.0       1  "
            ],
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>batting_team</th>\n",
              "      <th>bowling_team</th>\n",
              "      <th>city</th>\n",
              "      <th>runs_left</th>\n",
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              "      <th>total_runs_x</th>\n",
              "      <th>crr</th>\n",
              "      <th>rrr</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>135691</th>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>51</td>\n",
              "      <td>17</td>\n",
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              "      <td>8.038835</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc386dea-a3d0-42ab-bc4b-9589819be0e3')\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fc386dea-a3d0-42ab-bc4b-9589819be0e3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fc386dea-a3d0-42ab-bc4b-9589819be0e3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"final_df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"batting_team\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Kolkata Knight Riders\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bowling_team\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Sunrisers Hyderabad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Kolkata\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runs_left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 51,\n        \"max\": 51,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balls_left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 17,\n        \"max\": 17,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wickets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 4,\n        \"max\": 4,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_runs_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 189,\n        \"max\": 189,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          189\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 8.03883495145631,\n        \"max\": 8.03883495145631,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8.03883495145631\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rrr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 18.0,\n        \"max\": 18.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"result\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "final_df.sample()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "dfec0834",
      "metadata": {
        "id": "dfec0834"
      },
      "outputs": [],
      "source": [
        "final_df.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "bafcba9c",
      "metadata": {
        "id": "bafcba9c"
      },
      "outputs": [],
      "source": [
        "final_df = final_df[final_df['balls_left'] != 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "54edf23b",
      "metadata": {
        "id": "54edf23b"
      },
      "outputs": [],
      "source": [
        "X = final_df.iloc[:,:-1]\n",
        "y = final_df.iloc[:,-1]\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "3aa219a5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "3aa219a5",
        "outputId": "699970f0-9c51-465c-c3fe-a3b57cbed3af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               batting_team                 bowling_team       city  \\\n",
              "64488        Mumbai Indians             Delhi Daredevils      Delhi   \n",
              "85786        Mumbai Indians          Chennai Super Kings      Delhi   \n",
              "82269   Sunrisers Hyderabad          Chennai Super Kings  Hyderabad   \n",
              "65470        Mumbai Indians              Deccan Chargers     Mumbai   \n",
              "135896       Mumbai Indians               Delhi Capitals     Mumbai   \n",
              "...                     ...                          ...        ...   \n",
              "41310       Deccan Chargers               Mumbai Indians     Mumbai   \n",
              "43512       Deccan Chargers          Chennai Super Kings     Nagpur   \n",
              "126242     Delhi Daredevils              Kings XI Punjab      Delhi   \n",
              "73409   Sunrisers Hyderabad  Royal Challengers Bangalore  Hyderabad   \n",
              "101346      Kings XI Punjab             Rajasthan Royals       Pune   \n",
              "\n",
              "        runs_left  balls_left  wickets  total_runs_x       crr        rrr  \n",
              "64488          92          45        3           207  9.200000  12.266667  \n",
              "85786         103          64        2           192  9.535714   9.656250  \n",
              "82269         199         101        1           223  7.578947  11.821782  \n",
              "65470          37          48        3           100  5.250000   4.625000  \n",
              "135896        121          56        3           219  9.187500  12.964286  \n",
              "...           ...         ...      ...           ...       ...        ...  \n",
              "41310          96          48        5           178  6.833333  12.000000  \n",
              "43512          48          36        3           138  6.428571   8.000000  \n",
              "126242        111          89        2           157  8.903226   7.483146  \n",
              "73409         107          94        2           130  5.307692   6.829787  \n",
              "101346        158         117        1           162  8.000000   8.102564  \n",
              "\n",
              "[57073 rows x 9 columns]"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>batting_team</th>\n",
              "      <th>bowling_team</th>\n",
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              "      <td>Mumbai Indians</td>\n",
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              "      <td>92</td>\n",
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              "      <td>9.200000</td>\n",
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              "      <td>Mumbai Indians</td>\n",
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              "      <th>82269</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>199</td>\n",
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              "      <td>7.578947</td>\n",
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              "    <tr>\n",
              "      <th>65470</th>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Deccan Chargers</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>37</td>\n",
              "      <td>48</td>\n",
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              "      <td>100</td>\n",
              "      <td>5.250000</td>\n",
              "      <td>4.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135896</th>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Delhi Capitals</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>121</td>\n",
              "      <td>56</td>\n",
              "      <td>3</td>\n",
              "      <td>219</td>\n",
              "      <td>9.187500</td>\n",
              "      <td>12.964286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41310</th>\n",
              "      <td>Deccan Chargers</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>96</td>\n",
              "      <td>48</td>\n",
              "      <td>5</td>\n",
              "      <td>178</td>\n",
              "      <td>6.833333</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43512</th>\n",
              "      <td>Deccan Chargers</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Nagpur</td>\n",
              "      <td>48</td>\n",
              "      <td>36</td>\n",
              "      <td>3</td>\n",
              "      <td>138</td>\n",
              "      <td>6.428571</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126242</th>\n",
              "      <td>Delhi Daredevils</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>111</td>\n",
              "      <td>89</td>\n",
              "      <td>2</td>\n",
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              "      <td>8.903226</td>\n",
              "      <td>7.483146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73409</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>107</td>\n",
              "      <td>94</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>5.307692</td>\n",
              "      <td>6.829787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101346</th>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>Rajasthan Royals</td>\n",
              "      <td>Pune</td>\n",
              "      <td>158</td>\n",
              "      <td>117</td>\n",
              "      <td>1</td>\n",
              "      <td>162</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.102564</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>57073 rows × 9 columns</p>\n",
              "</div>\n",
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              "        document.querySelector('#df-7ce3a408-02e9-487f-89da-064b1ee79f09 button.colab-df-convert');\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_fd6c832a-2952-4e7a-90b3-470a75cf8226\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_fd6c832a-2952-4e7a-90b3-470a75cf8226 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 57073,\n  \"fields\": [\n    {\n      \"column\": \"batting_team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Kolkata Knight Riders\",\n          \"Sunrisers Hyderabad\",\n          \"Rajasthan Royals\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bowling_team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Royal Challengers Bangalore\",\n          \"Chennai Super Kings\",\n          \"Mumbai Indians\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"Indore\",\n          \"Ahmedabad\",\n          \"Mohali\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runs_left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50,\n        \"min\": -16,\n        \"max\": 248,\n        \"num_unique_values\": 254,\n        \"samples\": [\n          206,\n          117,\n          153\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balls_left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": -2,\n        \"max\": 119,\n        \"num_unique_values\": 121,\n        \"samples\": [\n          95,\n          16,\n          56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wickets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0,\n          3,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_runs_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 65,\n        \"max\": 250,\n        \"num_unique_values\": 142,\n        \"samples\": [\n          232,\n          170,\n          95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"crr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2750805067157467,\n        \"min\": 0.0,\n        \"max\": 42.0,\n        \"num_unique_values\": 5313,\n        \"samples\": [\n          8.225806451612904,\n          10.88659793814433,\n          4.645161290322581\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rrr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.14860217304409,\n        \"min\": -510.0,\n        \"max\": 678.0,\n        \"num_unique_values\": 8548,\n        \"samples\": [\n          11.493975903614459,\n          29.47826086956522,\n          11.866666666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "45c6fffa",
      "metadata": {
        "id": "45c6fffa"
      },
      "outputs": [],
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "trf = ColumnTransformer([\n",
        "    ('trf',OneHotEncoder(sparse=False,drop='first'),['batting_team','bowling_team','city'])\n",
        "]\n",
        ",remainder='passthrough')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "9be108ac",
      "metadata": {
        "id": "9be108ac"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "92dfbfcb",
      "metadata": {
        "id": "92dfbfcb"
      },
      "outputs": [],
      "source": [
        "pipe = Pipeline(steps=[\n",
        "    ('step1',trf),\n",
        "    ('step2',LogisticRegression(solver='liblinear'))\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "12679868",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "12679868",
        "outputId": "826f4fa5-fa60-4e0a-e2cb-f3079e381635"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('step1',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('trf',\n",
              "                                                  OneHotEncoder(drop='first',\n",
              "                                                                sparse=False),\n",
              "                                                  ['batting_team',\n",
              "                                                   'bowling_team', 'city'])])),\n",
              "                ('step2', LogisticRegression(solver='liblinear'))])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;trf&#x27;,\n",
              "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
              "                                                                sparse=False),\n",
              "                                                  [&#x27;batting_team&#x27;,\n",
              "                                                   &#x27;bowling_team&#x27;, &#x27;city&#x27;])])),\n",
              "                (&#x27;step2&#x27;, LogisticRegression(solver=&#x27;liblinear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;trf&#x27;,\n",
              "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
              "                                                                sparse=False),\n",
              "                                                  [&#x27;batting_team&#x27;,\n",
              "                                                   &#x27;bowling_team&#x27;, &#x27;city&#x27;])])),\n",
              "                (&#x27;step2&#x27;, LogisticRegression(solver=&#x27;liblinear&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">step1: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;trf&#x27;,\n",
              "                                 OneHotEncoder(drop=&#x27;first&#x27;, sparse=False),\n",
              "                                 [&#x27;batting_team&#x27;, &#x27;bowling_team&#x27;, &#x27;city&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">trf</label><div class=\"sk-toggleable__content\"><pre>[&#x27;batting_team&#x27;, &#x27;bowling_team&#x27;, &#x27;city&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;runs_left&#x27;, &#x27;balls_left&#x27;, &#x27;wickets&#x27;, &#x27;total_runs_x&#x27;, &#x27;crr&#x27;, &#x27;rrr&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "pipe.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "cf3fde3b",
      "metadata": {
        "id": "cf3fde3b"
      },
      "outputs": [],
      "source": [
        "y_pred = pipe.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "b43ea121",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b43ea121",
        "outputId": "e60f3bba-4636-4914-b251-00f35e1a41e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7987245076739785"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "01205f46",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01205f46",
        "outputId": "ed532931-5c10-407b-d56c-4d77829869eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.11028987, 0.88971013])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "pipe.predict_proba(X_test)[10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "cf6fbd69",
      "metadata": {
        "id": "cf6fbd69"
      },
      "outputs": [],
      "source": [
        "def match_summary(row):\n",
        "    print(\"Batting Team-\" + row['batting_team'] + \" | Bowling Team-\" + row['bowling_team'] + \" | Target- \" + str(row['total_runs_x']))\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "41c62b45",
      "metadata": {
        "id": "41c62b45"
      },
      "outputs": [],
      "source": [
        "def match_progression(x_df,match_id,pipe):\n",
        "    match = x_df[x_df['match_id'] == match_id]\n",
        "    match = match[(match['ball'] == 6)]\n",
        "    temp_df = match[['batting_team','bowling_team','city','runs_left','balls_left','wickets','total_runs_x','crr','rrr']].dropna()\n",
        "    temp_df = temp_df[temp_df['balls_left'] != 0]\n",
        "    result = pipe.predict_proba(temp_df)\n",
        "    temp_df['lose'] = np.round(result.T[0]*100,1)\n",
        "    temp_df['win'] = np.round(result.T[1]*100,1)\n",
        "    temp_df['end_of_over'] = range(1,temp_df.shape[0]+1)\n",
        "\n",
        "    target = temp_df['total_runs_x'].values[0]\n",
        "    runs = list(temp_df['runs_left'].values)\n",
        "    new_runs = runs[:]\n",
        "    runs.insert(0,target)\n",
        "    temp_df['runs_after_over'] = np.array(runs)[:-1] - np.array(new_runs)\n",
        "    wickets = list(temp_df['wickets'].values)\n",
        "    new_wickets = wickets[:]\n",
        "    new_wickets.insert(0,10)\n",
        "    wickets.append(0)\n",
        "    w = np.array(wickets)\n",
        "    nw = np.array(new_wickets)\n",
        "    temp_df['wickets_in_over'] = (nw - w)[0:temp_df.shape[0]]\n",
        "\n",
        "    print(\"Target-\",target)\n",
        "    temp_df = temp_df[['end_of_over','runs_after_over','wickets_in_over','lose','win']]\n",
        "    return temp_df,target\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "d3238e65",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "d3238e65",
        "outputId": "3556213a-5988-4b94-e3e7-8a1fb26eec0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Target- 178\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       end_of_over  runs_after_over  wickets_in_over  lose   win\n",
              "10459            1                4               10  57.4  42.6\n",
              "10467            2                8                0  52.3  47.7\n",
              "10473            3                1                0  59.2  40.8\n",
              "10479            4                7               -1  70.7  29.3\n",
              "10485            5               12                0  60.8  39.2\n",
              "10491            6               13                0  48.5  51.5\n",
              "10497            7                9                0  42.5  57.5\n",
              "10505            8               15                0  28.4  71.6\n",
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              "10518           10               17                0  14.3  85.7\n",
              "10524           11                9               -1  19.9  80.1\n",
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              "10536           13                8                0  14.1  85.9\n",
              "10542           14                8                0  12.1  87.9\n",
              "10548           15                5               -1  21.2  78.8\n",
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              "10567           18                6               -1  71.3  28.7\n",
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              "      <th></th>\n",
              "      <th>end_of_over</th>\n",
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              "type": "dataframe",
              "variable_name": "temp_df",
              "summary": "{\n  \"name\": \"temp_df\",\n  \"rows\": 19,\n  \"fields\": [\n    {\n      \"column\": \"end_of_over\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1,\n        \"max\": 19,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          1,\n          6,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runs_after_over\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 17,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          13,\n          4,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wickets_in_over\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": -2,\n        \"max\": 10,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          -2,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.312713947092202,\n        \"min\": 12.1,\n        \"max\": 89.6,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          57.4,\n          48.5,\n          16.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"win\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.312713947092202,\n        \"min\": 10.4,\n        \"max\": 87.9,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          42.6,\n          51.5,\n          83.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "temp_df,target = match_progression(delivery_df,74,pipe)\n",
        "temp_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "256b9c2d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "256b9c2d",
        "outputId": "3831dfb2-e24c-4f7c-81bc-25406b42a8cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Target-178')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(18,8))\n",
        "plt.plot(temp_df['end_of_over'],temp_df['wickets_in_over'],color='yellow',linewidth=3)\n",
        "plt.plot(temp_df['end_of_over'],temp_df['win'],color='#00a65a',linewidth=4)\n",
        "plt.plot(temp_df['end_of_over'],temp_df['lose'],color='red',linewidth=4)\n",
        "plt.bar(temp_df['end_of_over'],temp_df['runs_after_over'])\n",
        "plt.title('Target-' + str(target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "5731378e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5731378e",
        "outputId": "60891b1a-b758-4485-b7f9-82e014c18491"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Sunrisers Hyderabad',\n",
              " 'Mumbai Indians',\n",
              " 'Royal Challengers Bangalore',\n",
              " 'Kolkata Knight Riders',\n",
              " 'Kings XI Punjab',\n",
              " 'Chennai Super Kings',\n",
              " 'Rajasthan Royals',\n",
              " 'Delhi Capitals']"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "teams"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "fb7e305d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fb7e305d",
        "outputId": "b9540df9-3c1d-4746-83d3-60a0d3403299"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Hyderabad', 'Bangalore', 'Mumbai', 'Indore', 'Kolkata', 'Delhi',\n",
              "       'Chandigarh', 'Jaipur', 'Chennai', 'Cape Town', 'Port Elizabeth',\n",
              "       'Durban', 'Centurion', 'East London', 'Johannesburg', 'Kimberley',\n",
              "       'Bloemfontein', 'Ahmedabad', 'Cuttack', 'Nagpur', 'Dharamsala',\n",
              "       'Visakhapatnam', 'Pune', 'Raipur', 'Ranchi', 'Abu Dhabi',\n",
              "       'Sharjah', nan, 'Mohali', 'Bengaluru'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "delivery_df['city'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install category_encoders\n",
        "import category_encoders as ce\n",
        "\n",
        "def transform_new_data(new_data):\n",
        "    # Assuming you want to use a specific encoder like OneHotEncoder\n",
        "    encoder = ce.OneHotEncoder(cols=['city']) # Replace 'city' with the actual column name\n",
        "    encoder.fit(new_data) # Fit the encoder to your data\n",
        "    transformed_data = encoder.transform(new_data)\n",
        "    return transformed_data\n",
        "\n",
        "# Example of transforming new data\n",
        "new_data = pd.DataFrame({'city': ['Abu Dhabi', 'New York']})\n",
        "transformed_new_data = transform_new_data(new_data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7nDHbpX3qugB",
        "outputId": "eecf14d8-d853-4cf8-cceb-3cfed53a5b15"
      },
      "id": "7nDHbpX3qugB",
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: category_encoders in /usr/local/lib/python3.10/dist-packages (2.6.3)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.22.0)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.11.4)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (0.14.2)\n",
            "Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.5.3)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (0.5.6)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2023.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.1->category_encoders) (1.16.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.5.0)\n",
            "Collecting numpy>=1.14.0 (from category_encoders)\n",
            "  Downloading numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m33.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.9.0->category_encoders) (24.1)\n",
            "Installing collected packages: numpy\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.22.0\n",
            "    Uninstalling numpy-1.22.0:\n",
            "      Successfully uninstalled numpy-1.22.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pandas<2.2.2dev0,>=2.0, but you have pandas 1.5.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed numpy-1.26.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "handle_unknown='ignore'"
      ],
      "metadata": {
        "id": "on3bGFY1re6z"
      },
      "id": "on3bGFY1re6z",
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# Sample data\n",
        "data = pd.DataFrame({'City': ['New York', 'Los Angeles', 'San Francisco', 'Chicago', 'Houston']})\n",
        "\n",
        "# Define all possible categories\n",
        "all_possible_categories = ['New York', 'Los Angeles', 'San Francisco', 'Chicago', 'Houston', 'Abu Dhabi']\n",
        "\n",
        "# Initialize the encoder\n",
        "encoder = OneHotEncoder(categories=[all_possible_categories], handle_unknown='ignore')\n",
        "\n",
        "# Fit the encoder\n",
        "encoder.fit(data[['City']])\n",
        "\n",
        "# Function to transform new data\n",
        "def transform_new_data(new_data):\n",
        "    transformed_data = encoder.transform(new_data[['City']])\n",
        "    return transformed_data\n",
        "\n",
        "# Example of transforming new data\n",
        "new_data = pd.DataFrame({'City': ['Abu Dhabi', 'New York']})\n",
        "transformed_new_data = transform_new_data(new_data)\n",
        "print(transformed_new_data.toarray())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yi1uqXWUuNva",
        "outputId": "3479abdd-83a6-428a-b8ec-ec0a134932a8"
      },
      "id": "yi1uqXWUuNva",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. 0. 0. 1.]\n",
            " [1. 0. 0. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "99e08b54",
      "metadata": {
        "id": "99e08b54"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "pickle.dump(pipe,open('pipe.pkl','wb'))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}