away_train.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install xgboost"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting xgboost\r\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Downloading xgboost-1.7.6-py3-none-manylinux2014_x86_64.whl (200.3 MB)\r\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/200.3 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r\u001b[2K     \u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/200.3 MB\u001b[0m \u001b[31m76.9 MB/s\u001b[0m eta \u001b[36m0:00:03\u001b[0m"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r\u001b[2K     \u001b[91m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.4/200.3 MB\u001b[0m \u001b[31m91.6 MB/s\u001b[0m eta \u001b[36m0:00:03\u001b[0m"
          ]
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          "name": "stdout",
          "text": [
            "\r\u001b[2K     \u001b[91m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.6/200.3 MB\u001b[0m \u001b[31m109.8 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K     \u001b[91m━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.6/200.3 MB\u001b[0m \u001b[31m111.5 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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          "text": [
            "\r\u001b[2K     \u001b[91m━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.9/200.3 MB\u001b[0m \u001b[31m114.3 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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          "text": [
            "\r\u001b[2K     \u001b[91m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m22.9/200.3 MB\u001b[0m \u001b[31m115.8 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m26.6/200.3 MB\u001b[0m \u001b[31m115.8 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.3/200.3 MB\u001b[0m \u001b[31m107.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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          "name": "stdout",
          "text": [
            "\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.3/200.3 MB\u001b[0m \u001b[31m107.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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          "text": [
            "\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.3/200.3 MB\u001b[0m \u001b[31m107.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.3/200.3 MB\u001b[0m \u001b[31m107.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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          "text": [
            "\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.3/200.3 MB\u001b[0m \u001b[31m107.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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          "text": [
            "\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.3/200.3 MB\u001b[0m \u001b[31m107.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.3/200.3 MB\u001b[0m \u001b[31m107.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.3/200.3 MB\u001b[0m \u001b[31m107.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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          "text": [
            "\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.3/200.3 MB\u001b[0m \u001b[31m107.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.9/200.3 MB\u001b[0m \u001b[31m24.0 MB/s\u001b[0m eta \u001b[36m0:00:08\u001b[0m"
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          "text": [
            "\r\u001b[2K     \u001b[91m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m33.1/200.3 MB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:08\u001b[0m"
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          "text": [
            "\r\u001b[2K     \u001b[91m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.4/200.3 MB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:07\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.4/200.3 MB\u001b[0m \u001b[31m105.3 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.1/200.3 MB\u001b[0m \u001b[31m111.1 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.2/200.3 MB\u001b[0m \u001b[31m109.6 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/200.3 MB\u001b[0m \u001b[31m108.0 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.2/200.3 MB\u001b[0m \u001b[31m111.5 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.2/200.3 MB\u001b[0m \u001b[31m112.4 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.0/200.3 MB\u001b[0m \u001b[31m110.9 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.6/200.3 MB\u001b[0m \u001b[31m114.1 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.3/200.3 MB\u001b[0m \u001b[31m114.5 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.0/200.3 MB\u001b[0m \u001b[31m109.1 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.4/200.3 MB\u001b[0m \u001b[31m103.7 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.8/200.3 MB\u001b[0m \u001b[31m98.9 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.9/200.3 MB\u001b[0m \u001b[31m102.4 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.3/200.3 MB\u001b[0m \u001b[31m104.5 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m95.5/200.3 MB\u001b[0m \u001b[31m108.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.9/200.3 MB\u001b[0m \u001b[31m112.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m"
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            "\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m200.3/200.3 MB\u001b[0m \u001b[31m124.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m"
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        },
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          "name": "stdout",
          "text": [
            "\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m200.3/200.3 MB\u001b[0m \u001b[31m124.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.3/200.3 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
            "\u001b[?25h"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy in /opt/conda/lib/python3.9/site-packages (from xgboost) (1.23.5)\r\n",
            "Requirement already satisfied: scipy in /opt/conda/lib/python3.9/site-packages (from xgboost) (1.9.1)\r\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing collected packages: xgboost\r\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully installed xgboost-1.7.6\r\n"
          ]
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code"
        },
        "ExecuteTime": {
          "end_time": "2023-07-08T17:35:20.216797+00:00",
          "start_time": "2023-07-08T17:35:08.164087+00:00"
        }
      },
      "id": "eb03e0f9-46bb-4dfe-b82a-3b0c77ed7805"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install lightgbm"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting lightgbm\r\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Downloading lightgbm-3.3.5-py3-none-manylinux1_x86_64.whl (2.0 MB)\r\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.0 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m69.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m50.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
            "\u001b[?25hRequirement already satisfied: scikit-learn!=0.22.0 in /opt/conda/lib/python3.9/site-packages (from lightgbm) (1.1.2)\r\n",
            "Requirement already satisfied: numpy in /opt/conda/lib/python3.9/site-packages (from lightgbm) (1.23.5)\r\n",
            "Requirement already satisfied: scipy in /opt/conda/lib/python3.9/site-packages (from lightgbm) (1.9.1)\r\n",
            "Requirement already satisfied: wheel in /opt/conda/lib/python3.9/site-packages (from lightgbm) (0.40.0)\r\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: joblib>=1.0.0 in /opt/conda/lib/python3.9/site-packages (from scikit-learn!=0.22.0->lightgbm) (1.2.0)\r\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.9/site-packages (from scikit-learn!=0.22.0->lightgbm) (3.1.0)\r\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing collected packages: lightgbm\r\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully installed lightgbm-3.3.5\r\n"
          ]
        }
      ],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code"
        },
        "ExecuteTime": {
          "end_time": "2023-07-08T17:35:25.619616+00:00",
          "start_time": "2023-07-08T17:35:20.227146+00:00"
        }
      },
      "id": "0b1ca029-276e-4e12-bd52-1df2b231883a"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Prepare the data\n",
        "df = pd.read_csv('mod_sub_dataset.csv')\n",
        "X = df.drop(columns=['away_team', 'home_team', 'draw'])\n",
        "y = df['away_team']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize the models\n",
        "lr = LogisticRegression()\n",
        "dt = DecisionTreeClassifier()\n",
        "rf = RandomForestClassifier()\n",
        "nb = GaussianNB()\n",
        "knn = KNeighborsClassifier()\n",
        "xgb = XGBClassifier()\n",
        "lgbm = LGBMClassifier()\n",
        "ada = AdaBoostClassifier()\n",
        "\n",
        "# List of models\n",
        "models = [lr, dt, rf, nb, knn, xgb, lgbm, ada]\n",
        "model_names = ['Logistic Regression', 'Decision Trees', 'Random Forest', 'Naive Bayes', 'KNN', 'XGBoost', 'LGBM', 'AdaBoost']\n",
        "\n",
        "# List to store the accuracy scores\n",
        "accuracy_scores = []\n",
        "\n",
        "# Train and evaluate each model\n",
        "for model in models:\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    accuracy_scores.append(accuracy)\n",
        "\n",
        "# Plot the performance of each model\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar(model_names, accuracy_scores)\n",
        "plt.xlabel('Models')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Performance Comparison of ML Models for Predicting Away Team')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": null,
      "metadata": {
        "noteable": {
          "cell_type": "code"
        },
        "ExecuteTime": {
          "end_time": "2023-07-08T17:35:34.445324+00:00",
          "start_time": "2023-07-08T17:35:25.630055+00:00"
        }
      },
      "id": "525114e5-0ea4-4110-b5b0-419c72ff048a"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Predict the test set results for each model\n",
        "y_pred_lr = lr.predict(X_test)\n",
        "y_pred_dt = dt.predict(X_test)\n",
        "y_pred_rf = rf.predict(X_test)\n",
        "y_pred_nb = nb.predict(X_test)\n",
        "y_pred_knn = knn.predict(X_test)\n",
        "y_pred_lgbm = lgbm.predict(X_test)\n",
        "y_pred_ada = ada.predict(X_test)\n",
        "\n",
        "# Print the classification report for each model\n",
        "print('Classification Report for Logistic Regression:')\n",
        "print(classification_report(y_test, y_pred_lr))\n",
        "\n",
        "print('Classification Report for Decision Trees:')\n",
        "print(classification_report(y_test, y_pred_dt))\n",
        "\n",
        "print('Classification Report for Random Forest:')\n",
        "print(classification_report(y_test, y_pred_rf))\n",
        "\n",
        "print('Classification Report for Naive Bayes:')\n",
        "print(classification_report(y_test, y_pred_nb))\n",
        "\n",
        "print('Classification Report for KNN:')\n",
        "print(classification_report(y_test, y_pred_knn))\n",
        "\n",
        "print('Classification Report for LGBM:')\n",
        "print(classification_report(y_test, y_pred_lgbm))\n",
        "\n",
        "print('Classification Report for AdaBoost:')\n",
        "print(classification_report(y_test, y_pred_ada))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report for Logistic Regression:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.94      0.89       189\n",
            "           1       0.75      0.49      0.59        67\n",
            "\n",
            "    accuracy                           0.82       256\n",
            "   macro avg       0.79      0.72      0.74       256\n",
            "weighted avg       0.82      0.82      0.81       256\n",
            "\n",
            "Classification Report for Decision Trees:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.85      0.85       189\n",
            "           1       0.56      0.54      0.55        67\n",
            "\n",
            "    accuracy                           0.77       256\n",
            "   macro avg       0.70      0.69      0.70       256\n",
            "weighted avg       0.77      0.77      0.77       256\n",
            "\n",
            "Classification Report for Random Forest:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.88      0.86       189\n",
            "           1       0.62      0.55      0.58        67\n",
            "\n",
            "    accuracy                           0.79       256\n",
            "   macro avg       0.73      0.72      0.72       256\n",
            "weighted avg       0.79      0.79      0.79       256\n",
            "\n",
            "Classification Report for Naive Bayes:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.85      0.86       189\n",
            "           1       0.60      0.64      0.62        67\n",
            "\n",
            "    accuracy                           0.79       256\n",
            "   macro avg       0.73      0.74      0.74       256\n",
            "weighted avg       0.80      0.79      0.80       256\n",
            "\n",
            "Classification Report for KNN:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.89      0.88       189\n",
            "           1       0.67      0.63      0.65        67\n",
            "\n",
            "    accuracy                           0.82       256\n",
            "   macro avg       0.77      0.76      0.76       256\n",
            "weighted avg       0.82      0.82      0.82       256\n",
            "\n",
            "Classification Report for LGBM:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.87      0.86       189\n",
            "           1       0.60      0.55      0.57        67\n",
            "\n",
            "    accuracy                           0.79       256\n",
            "   macro avg       0.72      0.71      0.72       256\n",
            "weighted avg       0.78      0.79      0.78       256\n",
            "\n",
            "Classification Report for AdaBoost:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.91      0.88       189\n",
            "           1       0.67      0.52      0.59        67\n",
            "\n",
            "    accuracy                           0.81       256\n",
            "   macro avg       0.76      0.72      0.73       256\n",
            "weighted avg       0.80      0.81      0.80       256\n",
            "\n"
          ]
        }
      ],
      "execution_count": null,
      "metadata": {
        "noteable": {
          "cell_type": "code"
        },
        "ExecuteTime": {
          "end_time": "2023-07-08T17:35:35.193081+00:00",
          "start_time": "2023-07-08T17:35:34.712875+00:00"
        }
      },
      "id": "2190fed2-1b5a-4f3a-9457-fc988ccceaf1"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the hyperparameters to tune for each model\n",
        "param_grid_lr = {\n",
        "    'C': [0.1, 1, 10],\n",
        "    'penalty': ['l1', 'l2']\n",
        "}\n",
        "param_grid_nb = {\n",
        "    'var_smoothing': np.logspace(0,-9, num=100)\n",
        "}\n",
        "param_grid_rf = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'max_depth': [None, 5, 10],\n",
        "    'min_samples_split': [2, 5, 10]\n",
        "}\n",
        "param_grid_ada = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'learning_rate': [0.01, 0.1, 1]\n",
        "}\n",
        "\n",
        "# Perform grid search for each model\n",
        "grid_lr = GridSearchCV(lr, param_grid_lr, cv=5, scoring='accuracy')\n",
        "grid_lr.fit(X_train, y_train)\n",
        "\n",
        "grid_nb = GridSearchCV(nb, param_grid_nb, cv=5, scoring='accuracy')\n",
        "grid_nb.fit(X_train, y_train)\n",
        "\n",
        "grid_rf = GridSearchCV(rf, param_grid_rf, cv=5, scoring='accuracy')\n",
        "grid_rf.fit(X_train, y_train)\n",
        "\n",
        "grid_ada = GridSearchCV(ada, param_grid_ada, cv=5, scoring='accuracy')\n",
        "grid_ada.fit(X_train, y_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": null,
          "data": {
            "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=AdaBoostClassifier(),\n             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 1],\n                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n             scoring=&#x27;accuracy&#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\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=AdaBoostClassifier(),\n             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 1],\n                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n             scoring=&#x27;accuracy&#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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier()</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null,
      "metadata": {
        "noteable": {
          "cell_type": "code"
        },
        "ExecuteTime": {
          "end_time": "2023-07-08T17:36:49.202850+00:00",
          "start_time": "2023-07-08T17:35:35.609196+00:00"
        }
      },
      "id": "1661e020-945b-4abf-8801-74439b78121e"
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the test set results for each model\n",
        "y_pred_lr = grid_lr.predict(X_test)\n",
        "y_pred_nb = grid_nb.predict(X_test)\n",
        "y_pred_rf = grid_rf.predict(X_test)\n",
        "y_pred_ada = grid_ada.predict(X_test)\n",
        "\n",
        "# Print the classification report for each model\n",
        "print('Classification Report for Logistic Regression:')\n",
        "print(classification_report(y_test, y_pred_lr))\n",
        "\n",
        "print('Classification Report for Naive Bayes:')\n",
        "print(classification_report(y_test, y_pred_nb))\n",
        "\n",
        "print('Classification Report for Random Forest:')\n",
        "print(classification_report(y_test, y_pred_rf))\n",
        "\n",
        "print('Classification Report for AdaBoost:')\n",
        "print(classification_report(y_test, y_pred_ada))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report for Logistic Regression:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.94      0.89       189\n",
            "           1       0.74      0.48      0.58        67\n",
            "\n",
            "    accuracy                           0.82       256\n",
            "   macro avg       0.79      0.71      0.73       256\n",
            "weighted avg       0.81      0.82      0.81       256\n",
            "\n",
            "Classification Report for Naive Bayes:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.97      0.87       189\n",
            "           1       0.74      0.25      0.38        67\n",
            "\n",
            "    accuracy                           0.78       256\n",
            "   macro avg       0.76      0.61      0.62       256\n",
            "weighted avg       0.77      0.78      0.74       256\n",
            "\n",
            "Classification Report for Random Forest:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.86      0.85       189\n",
            "           1       0.57      0.54      0.55        67\n",
            "\n",
            "    accuracy                           0.77       256\n",
            "   macro avg       0.71      0.70      0.70       256\n",
            "weighted avg       0.77      0.77      0.77       256\n",
            "\n",
            "Classification Report for AdaBoost:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.93      0.88       189\n",
            "           1       0.70      0.45      0.55        67\n",
            "\n",
            "    accuracy                           0.80       256\n",
            "   macro avg       0.76      0.69      0.71       256\n",
            "weighted avg       0.79      0.80      0.79       256\n",
            "\n"
          ]
        }
      ],
      "execution_count": null,
      "metadata": {
        "noteable": {
          "cell_type": "code"
        },
        "ExecuteTime": {
          "end_time": "2023-07-08T17:36:49.445212+00:00",
          "start_time": "2023-07-08T17:36:49.210248+00:00"
        }
      },
      "id": "a44e13bd-abeb-446b-b16d-3b18644479cb"
    },
    {
      "cell_type": "code",
      "source": [
        "importances_lr = abs(grid_lr.best_estimator_.coef_[0])\n",
        "importances_rf = grid_rf.best_estimator_.feature_importances_\n",
        "importances_ada = grid_ada.best_estimator_.feature_importances_\n",
        "\n",
        "# Convert to DataFrame for easier plotting\n",
        "importances_df = pd.DataFrame({\n",
        "    'Feature': X.columns,\n",
        "    'Importance (Logistic Regression)': importances_lr,\n",
        "    'Importance (Random Forest)': importances_rf,\n",
        "    'Importance (AdaBoost)': importances_ada\n",
        "})\n",
        "\n",
        "# Plot the feature importances\n",
        "importances_df.plot(x='Feature', y=['Importance (Logistic Regression)', 'Importance (Random Forest)', 'Importance (AdaBoost)'], kind='bar', figsize=(12, 6))\n",
        "plt.title('Feature Importance of Each Model')\n",
        "plt.ylabel('Importance')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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HB6PLA3KcdevWac2aNXr77bcVFxen2rVrq169emrQoIHRpcEgzJgjS8uTJ48iIiJUtGjRVPv89ddf8vb21r179zKxMgCSNH/+fA0ePFht2rTRtm3bdPnyZR09elRubm6WPs2aNZOXl5e+/PJLAysFcrbLly8rPDxc7dq1k/TgNNpJkybJ3t5ec+bMUb169QyuEMi5du7cqTVr1mj16tU6fvy4TCaTevfura5du+rll182ujxkEoI5sjQbGxuNGzfusd/mx8bGasSIEUpISMjEygAkWb58uVauXCkHBwcNGjRIFSpUsGzbv3+/Xn/9de3fv1+urq4GVgkAQNZ37NgxS0j//fff5enpqXPnzhldFjIBwRxZmo+Pj0wmU5r6njlz5jlXAyA94uPjlT9/fqPLAPD/Gz9+vPr06SMnJyejSwHwGJcvX9YPP/zAvcxzCII5ACBDXbx4UQ//ryVXrlyPvRwFQOZycnLSgQMH5OPjY3QpAB7RrFkzzZs3T+7u7kaXgkzGfcwBAOn2559/Wm6VlqR8+fLy9vaWl5eXvLy8VKJECW3bts2gCgE8ijkZIOvasmWLbt++bXQZMADBHFnanTt3kt0qrVmzZgoKCrI8GjZsqGvXrhlUIZCzjRo1SjVq1EjWfurUKSUmJioxMVEff/yxPv30UwOqAwAA+HcgmCNLGz16tBYvXmzVtmXLFjVq1EhdunRRly5dlJCQoJCQEIMqBHK2HTt2qHnz5o/t06pVKx08eDCTKgLwJLGxsZzGDmRRderUUb58+YwuAwbgPubI0tavX6/Jkycna3/zzTct/6goXbq0evbsmcmVAZAe3NLw1q1bVm1btmxR8eLFLc9v3LiR5kUcATw/MTExOnLkiG7cuJFsW+PGjTO/IADJrF271ugSYBCCObK0iIgIeXh4WLX17t3baiXZggUL6uLFi5ldGgBJNWrU0DfffGN1n9UqVapY9Vm+fLleeumlTK4MwMPmz5+vd999N8VrV00mE7ccBTLZ+vXr09yXL85yBoI5srTKlSvrt99+U5kyZSxtEydOtOqzc+dOq/smA8g8H3/8sapXry4XFxcNGjRI9vb2lm23bt3SF198odmzZ2vHjh0GVglg5MiRGjhwoAYPHiwXFxejywFyvKZNmyZrM5lMyRZn5IuznINrzJGldenSRUOHDtWpU6dS3H7mzBkNGzZMHTp0yOTKAEhS2bJl9f3332vp0qUqVqyYXn75ZbVo0UIvv/yyihYtqgULFmj58uWqXLmy0aUCOVr+/PnVuXNnQjmQRSQtkPrwo0CBAjp9+rRVG6E85+A+5sjyevfurcWLF6tdu3aqUaOG3N3dFRkZqd27d+ubb77Rm2++qSVLlhhdJpAjbd26Vf7+/jKbzfrxxx917NgxXbhwQcWLF1eZMmXUrFkz5c2b1+gygRzv008/1f379zVq1CijSwGQCkdHRx08eJDFGXMogjn+FcLCwjR16lQdPXrU6h/9/fr1U8OGDY0uD8ixcuXKpcjISBUuXNjSNnz4cAUHB6tgwYIGVgbg4RB+//59TZs2TR07dpSDg0OyvuPHj8/M0gCkgGCesxHMAQDpZmNjo6ioKKtg7uTkpAMHDvAPC8Bg9erVS1M/k8mkjRs3PudqADwJwTxnY/E3ZGm+vr7avXs3M2/Avwjf9wJZw6ZNm4wuAUAqUlqVPSEhQWFhYTp27JhVO6uy5wzMmCNLS2k2DkDWkStXLkVFRcnV1dXS5ujoqEOHDsnb29u4wgA8tdq1a2v58uUqVqyY0aUA2Z6NTdrW4GZV9pyDGXNkeSaTyegSAKTCbDarR48esrOzs7Tdvn1b/fr1s7p1mvTgfuYAsq5Dhw7pn3/+MboMIEdITEw0ugRkMQRzZHktW7ZM06rOXB8HZL6uXbsma+vUqVPmFwIAQDY2a9YsdejQIcXFG5E9EMyR5VWqVEkFChQwugwAKZg/f77RJQAAkO0NHTpUDRs2JJhnYwRzZGkmk0mhoaFcYw4AAIAci2XBsr+0rToAGIS/hAAAAABkdwRzZGldu3ZVvnz5jC4DAAAAAJ4bTmVHlhYXF6cePXqkqS8rPgMAkH7vvPOOnJycjC4DAHIkZsyRpdnb26tAgQJWj9WrVyshISFZOwAASNmFCxd07Ngxy/OtW7eqdevWGjx4sG7evClJGjdunAoWLGhUiQCQo5nMXMSLfxlHR0cdPHhQPj4+RpcCAMC/Qtu2bVWxYkWFhIToxo0b8vX11RtvvKFt27bplVde0aJFi4wuEcBjeHp6atu2bfLy8jK6FDwnzJgDAABkcxs3blTr1q0lSVOnTlXz5s313//+V/PmzdO6desMrg5AShITE/Xbb79JenDWC6E8e+MacwAAgGwuf/782r17txwcHDR79myFhYVJenDJGIusAplv6NChT+wTGRmpn3/+WZGRkZlQEYxGMMe/kslkMroEAAD+Nd577z117txZdnZ26tChg/z8/CRJX3/9tQIDAw2uDsh5du/e/cQ+Dg4O+vLLLzOhGmQFXGOOLK1t27bJ2latWqUGDRrI3t7eqp1V2QEASN3OnTsVExOj1157TTY2D65m/OSTT9SjRw95eHgYXB0A5GzMmCNLS2m19U6dOhlQCQAA/15LlixR27ZtlTdvXqv23r1763//+1+ab00KAHg+mDEHAADI5nLlyqXIyEgVLlzYqv306dN6+eWXdfXqVYMqA3D9+nWNHDlS4eHhunLlSrLtUVFRBlSFzMaMOQAAQDa1fv16SZLZbNaGDRvk7Oxs2RYfH69ly5bJ0dHRoOoASFL37t11/PhxtW/fXjNnztTYsWOVkJCgMWPGaO7cuUaXh0zCjDkAAEA2ValSJUnSn3/+qUf/yZc7d26VKlVKkydPVoMGDYwoD4AkZ2dnbdmyRZUqVVK1atW0Y8cO2draatmyZdqwYYPmzZtndInIBMyYAwAAZFOHDh2SJNnY2CgyMlJubm4GVwTgUcWLF9e9e/ckSdWqVdP69ev1xhtvqHLlyho4cKCxxSHTEMwBAACyuXPnzhHKgSwqKChIK1asULVq1dS1a1e1a9dOsbGxWr16tRwcHIwuD5mEU9kBAABygJiYGB05ckQ3btxItq1x48aZXxAASdLly5cVHh6udu3aSZImTJigSZMmyd7eXrNnz1ZQUJDBFSIzEMwBAACyuQULFqh///66fft2sm0mk0kJCQkGVAUASGJjdAEAAAB4vkaOHKmBAwfq77//VmJiotWDUA4YKygoSDExMcnaT506xdksOQjBHAAAIJuzs7NT586d5eLiYnQpAB4RFhZmWfztYdeuXdP27dsNqAhGYPE3AACAbK5z585atmyZRo0aZXQpAP5/bdu2tfy5R48esrOzszyPj4/Xrl27VLNmTSNKgwEI5gAAANlcyZIlNWDAAEVFRaW4yvP48eMNqArI2V5++WVJ0sqVK5U/f37ly5fPss3JyUmDBw9Wr169jCoPmYzF3wAAALK5evXqpbrNZDJp48aNmVgNgIfVq1dP33//vZycnIwuBQYimAMAAABAFnD79m2ZzWblz5/f6FKQyVj8DQAAAAAM9O2336p8+fJycHCQo6OjypUrp5UrVxpdFjIR15gDAABkQ6+88orKly+v+fPnq2TJkjKZTKn2PX36dCZWBuBhy5cvV8+ePTVkyBAFBgZKerBSe8+ePZWYmGi1SByyL4I5AABANtSxY0d5eHhIkgYPHmxwNQBSM3bsWH355Zfq3Lmzpa127dry9vbWZ599RjDPIbjGHAAAAAAM4uzsrN9//11+fn5W7SdOnFCNGjV048YNYwpDpuIacwAAgBzi999/15w5czRr1iz9/vvvRpcDQNKLL76opUuXJmv/+uuvVaVKlcwvCIZgxhwAACCbu3Xrltq0aaNffvlFXl5ekqSIiAg1aNDAcg9lAMbYt2+f6tSpo1q1aikgIEAmk0nh4eHavn27tm7dqqpVqxpdIjIBM+YAAADZ3NChQ/X333/r5MmTOnXqlE6dOqWTJ0/q2rVrGjp0qNHlATnaiy++qD/++EMlS5bUmjVr9P3338vLy0uHDh0ilOcgzJgDAABkc56enlq1apWqV69u1f7777+rVatWunjxokGVAXic6Ohoubm5GV0GMgEz5gAAANlcYmKibGyS/7MvpTYAmStXrly6cuVKsvajR4+qdu3aBlQEI/C3MQAAQDb3xhtv6N1339WFCxcsbRcuXNDAgQPVokULAysDYDabZTKZkrXv3r1bV69eNaAiGIH7mAMAAGRzn3/+ud588035+vrK29tbJpNJZ8+eVVBQkD7//HOjywNyJDc3N5lMJplMJpUrV87qDJY7d+4oLi5O/fr1M7BCZCauMQcAAMghfvvtNx04cEBms1lVqlSRv7+/0SUBOda+fftkNptVvXp1TZkyRU5OTpZtuXPnVqlSpVSjRg0DK0RmIpgDAAAAgEHGjBmj4OBg5cuXz+hSYCCuMQcAAMgBpk+frhdffFFOTk4qVqyYGjdurG3bthldFpDjffTRR08M5SVKlFBEREQmVQQjcI05AABANhcSEqIZM2aof//+qlixouLj4xUWFqagoCCtWLGCBeCALO7GjRtKTEw0ugw8R5zKDgAAkM2VKFFC06ZNSxbAp06dqnnz5unQoUMGVQYgLRwdHXXw4EH5+PgYXQqeE05lBwAAyOby5Mmj0qVLJ2tv0qSJ/vrrLwMqAgA8jGAOAACQzXXt2lULFy5M1r5lyxZOYweALIBrzAEAALK527dva8qUKTpx4oRy5cplad+8ebOqVKmitm3bWtqWL19uRIkAkKMRzAEAALK5yMhIdejQIVn766+/bkA1AIBHsfgbAAAAAGSiHj166OOPP1bRokW1detW+fv7W53N8qiZM2eqY8eOcnBwyMQqkZkI5gAAAFCJEiW0detWeXl5GV0KkO05ODho+/btqlSpknLlyqWoqCi5uroaXRYMxKnsAAAA4D7JQCaqWrWqmjdvrho1ashsNqtHjx6ys7NLsS/rPuQMBHMAAAAAyETLli3T1KlTFR0dLUnKnz+/8uXLZ3BVMBLBHAAAAAAy0dWrV/Xpp59ariufOnWq7O3tDa4KRuI+5gAAAACQiRo0aKCzZ89KkiIiIpSQkGBwRTAaM+YAAAAAkMmWLFmiNm3aKDw8XIcPH1ahQoVS7FeuXLlMrgxGYFV2AAAAyNHRUQcPHpSPj4/RpQDZ3tSpUzVo0CDLgouPRjKTySSz2SyTycRseg7BqewAAADZUI8ePXTp0iVJ0tatW5/4j/vx48dzuyYgk7z33nu6ceOGzpw5I7PZrN27d+vcuXOWx9mzZy3/Rc7AjDkAAEA2xH2SgX+H8+fPq0SJEkaXAYNxjTkAAEA2xH2Sgaxr7969cnJyUqlSpRQREaGIiIhU+9apUycTK4NRCOYAAADZEPdJBrKuN954Q+XKldMvv/yiunXrptqPa8xzDoI5AABANsR9koGsa+fOnZYvyuLi4gyuBlkBwRwAACAbatCggbZt22Y5VZZZNyDr2LRpU5r7durU6TlWgqyCxd8AAACyIXd3d7399ttq06aNKlWqpM2bN3OfZCCLcHNzs/w5ISFBsbGxcnBwsLQlJibq1q1bql27tsLCwowoEZmMYA4AAJANcZ9k4N/h448/1t27d/XJJ59Ytbdr104NGzZU165djSkMmYpgDgAAkE3dunVLV69elY+Pj3bv3q3ChQun2I9bNQHG8fLy0i+//KIyZcpYtR88eFAdO3bU4cOHDaoMmYlrzAEAALKpAgUKqECBAjp37hzhG8iizGazTpw4kSyY37hxQ1FRUQZVhcxGMAcAAMiGuE8y8O/QvXt39ezZUxMmTFBQUJBy586tXbt2adCgQWrevLnR5SGTcCo7AABANuTp6Wm5T7KNjU2q/bjGHDCW2WxWaGioJk2apNu3b0uSbGxs1L59e82cOVP58+c3uEJkBoI5AABANvTXX38pX758KliwoOLj4x/bl3/4A8a7d++eTp48qXv37qlkyZJWq7Qj+yOYAwAAZENLlixJc1/ukwwAxiKYAwAAZEPcJxkA/j1Y/A0AACAbio6Otvz5SfdJBgAYixlzAACAbI77JANA1pb6Ep0AAADIFpLuk/wo7pMMAFkDwRwAACCbS7pP8uLFi/XXX38pKipKP/zwg3r27Ml9kgEgC+AacwAAgGxu1KhRMpvNeuedd5LdJ3natGkGVwcA4BpzAACAHIL7JANA1kQwBwAAAADAQFxjDgAAAACAgQjmAAAAAAAYiGAOAAAAAICBCOYAAAAAABiIYA4AQDbQrVs3mUymZI/NmzcbXRoAAHgC7mMOAEA20axZM82b9/+1dy8hUa5xHMe/doEim8C8zCyCNBqXQhddBKaZS0mQkESCIWoRtCmEXCTdsLFAIqQERZg24aZwFxTIIKFR6qqMoqAMqaRNF4vKmbOIMzCcAx6zeI/D97P7Pw/vc1nN/Hied6Y/q62goGBJY75//57CwkI+ffrEunXrljSWJEn6dwZzSZJyxJo1aygpKQl6GZIkaZG8yi5JUo6bnZ3lwIEDbNy4kWg0Sm9vb6bvy5cvXLx4kaqqKkKhEDt37mRychKARCJBYWEhAPn5+eTl5fHy5UsSiQRFRUVZc+zfv59YLJapS0tLGRkZIRaLUVBQwOPHjwG4e/cuO3bsIBQKUVNTw6NHj/709iVJ+t8zmEuSlMPm5+fZu3cvkUiE0dFRrl27RkdHB3fu3AHg9evXjI+P09nZycOHDyktLaW1tRWA5uZmbt68CcDz58958+YNmzZt+s9zHz58mPLyckZHRykvL+f+/fu0tLTQ3t7O5OQkdXV1NDQ08O3bt9+/cUmSlhGvskuSlCNu3brF+vXrM/XRo0fZtm0ba9eupbu7G4BoNEpLSws3btygvr6erVu3Mjg4mHnm2LFjVFdX8+7dO4qLizPvqJeUlCz6HfOqqipOnjyZqU+fPs3Zs2dpamoCoL29nStXrjA2NkZ1dfUv71uSpOXOYC5JUo6or6+np6cnU2/YsIFLly4xPj6eFdi/f/9OTU1Npn7x4gX9/f0kk0nevn0L/Lz+XlxcvKT1VFZWZtUTExMkk0na2toybXNzc8zMzCxpHkmSljuDuSRJOSI/P58tW7ZktaVSKSorK7l+/XpW+9+n38lkksbGRk6dOsXQ0BCfP39m8+bNC86VTqcXvb5UKsW5c+dobGzMag+Hw4seS5KkXGIwlyQph1VUVDAwMEBRURGhUOgf/VevXmXfvn0cP34cgI8fP2b1r1r186vC3NxcJsyHQiE+fPjA/Pw8K1euJJVKMTMzQzQaXXAtU1NTnDhx4ndsTZKknOGPv0mSlMOam5uJRCI0NTXx4MEDnjx5QldXF69evQJ+nrKPjIwwMTHB8PAwBw8ezHq+rKyMFStW0NfXx7Nnz/j69SsVFRX8+PGDeDzO2NgYra2tTE9PL7iWM2fOkEgkuHDhAk+fPuXevXvE4/E/sm9JkpYTg7kkSTls9erVDA8PEw6HaWhoYPfu3Zm/QwPo6OggHA5TW1tLV1cXPT09WSfrkUiE8+fP093dzZ49e5iamqKsrIx4PM7ly5eJxWLU1tZy5MiRBdeya9cubt++zdDQENu3b+fQoUOk0+lfuhYvSVIuyUv7aShJkiRJUmA8MZckSZIkKUAGc0mSJEmSAmQwlyRJkiQpQAZzSZIkSZICZDCXJEmSJClABnNJkiRJkgJkMJckSZIkKUAGc0mSJEmSAmQwlyRJkiQpQAZzSZIkSZICZDCXJEmSJClAfwEURQ73LowiEQAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ],
      "execution_count": null,
      "metadata": {
        "noteable": {
          "cell_type": "code"
        },
        "ExecuteTime": {
          "end_time": "2023-07-08T17:36:50.122445+00:00",
          "start_time": "2023-07-08T17:36:49.454772+00:00"
        }
      },
      "id": "6a213a00-0329-4c9d-af4d-b121250c8ff1"
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the plot, we can observe that the models assign different levels of importance to the features. For instance, the Logistic Regression model assigns high importance to the 'FGS' feature, while the Random Forest and AdaBoost models assign high importance to the 'diff_points_h-a' feature.\n",
        "\n",
        "This variation in feature importance across models is due to the different ways in which these models learn from the data. For example, tree-based models like Random Forest and AdaBoost tend to assign higher importance to features that lead to a higher decrease in impurity when used for splitting the data, while Logistic Regression assigns importance based on the coefficients of the features in the logistic function."
      ],
      "metadata": {
        "noteable": {
          "cell_type": "markdown"
        }
      },
      "id": "8b0cc5ad-780b-4b56-b75c-b1e36da5a13b"
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "joblib.dump(grid_lr, 'lr_away_model.pkl')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": null,
          "data": {
            "text/plain": "['lr_away_model.pkl']"
          },
          "metadata": {}
        }
      ],
      "execution_count": null,
      "metadata": {
        "noteable": {
          "cell_type": "python"
        },
        "ExecuteTime": {
          "end_time": "2023-07-08T17:36:50.602204+00:00",
          "start_time": "2023-07-08T17:36:50.437633+00:00"
        }
      },
      "id": "458de0e5-206b-47cd-bba2-9e38f7bf60d3"
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "noteable": {
          "cell_type": "code"
        }
      },
      "id": "32e6862f-2d1e-4d3b-a5e9-410019238bc1"
    }
  ],
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        "openai_subdivision1_iso_code": "IN-MH"
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    "noteable": {
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}