Predicting_the_Football_(Soccer)_World_Cup_Winner.ipynb

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    "colab": {
      "name": "Predicting the Football (Soccer) World Cup Winner.ipynb",
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      "authorship_tag": "ABX9TyPFMR5ZKVVMLcmWdYqDIpDH",
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      "name": "python3",
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  "cells": [
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/saima8/Data-Science-from-scratch/blob/main/Predicting_the_Football_(Soccer)_World_Cup_Winner.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
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        "id": "9ptY4tRqSCvu",
        "outputId": "bf82c799-5eb7-4524-c6bd-336f4c23a776"
      },
      "source": [
        "rankings.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>country_full</th>\n",
              "      <th>country_abrv</th>\n",
              "      <th>cur_year_avg_weighted</th>\n",
              "      <th>rank_date</th>\n",
              "      <th>two_year_ago_weighted</th>\n",
              "      <th>three_year_ago_weighted</th>\n",
              "      <th>weighted_points</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Germany</td>\n",
              "      <td>GER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Italy</td>\n",
              "      <td>ITA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>SUI</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>SWE</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>ARG</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   rank country_full  ... three_year_ago_weighted  weighted_points\n",
              "0     1      Germany  ...                     0.0              0.0\n",
              "1     2        Italy  ...                     0.0              0.0\n",
              "2     3  Switzerland  ...                     0.0              0.0\n",
              "3     4       Sweden  ...                     0.0              0.0\n",
              "4     5    Argentina  ...                     0.0              0.0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NL2HR--hHkEX"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "from matplotlib import pyplot as plt\r\n",
        "\r\n",
        "rankings = pd.read_csv(\"https://raw.githubusercontent.com/rajeevratan84/data-analyst-bootcamp/master/fifa_ranking.csv\")\r\n",
        "rankings = rankings.loc[:,['rank','country_full','country_abrv','cur_year_avg_weighted', 'rank_date', \r\n",
        "                           'two_year_ago_weighted', 'three_year_ago_weighted']]\r\n",
        "\r\n",
        "rankings = rankings.replace({'IR Iran': \"Irah\"})\r\n",
        "rankings['weighted_points'] = rankings['cur_year_avg_weighted']+ rankings['two_year_ago_weighted']+rankings['three_year_ago_weighted']\r\n",
        "rankings['rank_date'] = pd.to_datetime(rankings['rank_date'])\r\n",
        "\r\n",
        "matches = pd.read_csv(\"https://raw.githubusercontent.com/rajeevratan84/data-analyst-bootcamp/master/results.csv\")\r\n",
        "matches = matches.replace({'Germany DR': 'Germany', 'China':'China PR'})\r\n",
        "matches['date'] = pd.to_datetime(matches['date'])\r\n",
        "\r\n",
        "# Get the world cup fixture data\r\n",
        "world_cup = pd.read_csv(\"https://raw.githubusercontent.com/rajeevratan84/data-analyst-bootcamp/master/WorldCup2018Dataset.csv\")\r\n",
        "\r\n",
        "# extract relavent columns\r\n",
        "world_cup = world_cup.loc[:, ['Team', 'Group', 'First match \\nagainst', 'Second match\\n against', 'Third match\\n against']]\r\n",
        "\r\n",
        "# Drop NAs\r\n",
        "world_cup = world_cup.dropna(how='all')\r\n",
        "\r\n",
        "# Fix some country names\r\n",
        "world_cup = world_cup.replace({\"IRAN\": \"Iran\", \r\n",
        "                               \"Costarica\": \"Costa Rica\", \r\n",
        "                               \"Porugal\": \"Portugal\", \r\n",
        "                               \"Columbia\": \"Colombia\", \r\n",
        "                               \"Korea\" : \"Korea Republic\"})\r\n",
        "# set index to team \r\n",
        "world_cup = world_cup.set_index('Team')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "TXxKotunSTJZ",
        "outputId": "b59436bc-a03e-464d-e7ed-d9933487f96e"
      },
      "source": [
        "matches.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>tournament</th>\n",
              "      <th>city</th>\n",
              "      <th>country</th>\n",
              "      <th>neutral</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1872-11-30</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>England</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Glasgow</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1873-03-08</td>\n",
              "      <td>England</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>London</td>\n",
              "      <td>England</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1874-03-07</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>England</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Glasgow</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1875-03-06</td>\n",
              "      <td>England</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>London</td>\n",
              "      <td>England</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1876-03-04</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>England</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Glasgow</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date home_team away_team  ...     city   country neutral\n",
              "0 1872-11-30  Scotland   England  ...  Glasgow  Scotland   False\n",
              "1 1873-03-08   England  Scotland  ...   London   England   False\n",
              "2 1874-03-07  Scotland   England  ...  Glasgow  Scotland   False\n",
              "3 1875-03-06   England  Scotland  ...   London   England   False\n",
              "4 1876-03-04  Scotland   England  ...  Glasgow  Scotland   False\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "YMU-VlUsSYB8",
        "outputId": "d237f307-eaf4-4607-f1bf-8dd60f860c3b"
      },
      "source": [
        "world_cup.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Group</th>\n",
              "      <th>First match \\nagainst</th>\n",
              "      <th>Second match\\n against</th>\n",
              "      <th>Third match\\n against</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Team</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Russia</th>\n",
              "      <td>A</td>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>Uruguay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Saudi Arabia</th>\n",
              "      <td>A</td>\n",
              "      <td>Russia</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>Egypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Egypt</th>\n",
              "      <td>A</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>Russia</td>\n",
              "      <td>Saudi Arabia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uruguay</th>\n",
              "      <td>A</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>Russia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portugal</th>\n",
              "      <td>B</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Morocco</td>\n",
              "      <td>Iran</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Group  ... Third match\\n against\n",
              "Team                ...                      \n",
              "Russia           A  ...               Uruguay\n",
              "Saudi Arabia     A  ...                 Egypt\n",
              "Egypt            A  ...          Saudi Arabia\n",
              "Uruguay          A  ...                Russia\n",
              "Portugal         B  ...                  Iran\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 204
        },
        "id": "46JyG_A5S_Mb",
        "outputId": "812f82ac-a97e-48a9-9346-4450847583c1"
      },
      "source": [
        "#Feature extraction\r\n",
        "\r\n",
        "rankings = rankings.set_index(['rank_date'])\\\r\n",
        "              .groupby(['country_full'], group_keys=False)\\\r\n",
        "              .resample('D').first()\\\r\n",
        "              .fillna(method='ffill')\\\r\n",
        "              .reset_index()\r\n",
        "rankings.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank_date</th>\n",
              "      <th>rank</th>\n",
              "      <th>country_full</th>\n",
              "      <th>country_abrv</th>\n",
              "      <th>cur_year_avg_weighted</th>\n",
              "      <th>two_year_ago_weighted</th>\n",
              "      <th>three_year_ago_weighted</th>\n",
              "      <th>weighted_points</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2003-01-15</td>\n",
              "      <td>204.0</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2003-01-16</td>\n",
              "      <td>204.0</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2003-01-17</td>\n",
              "      <td>204.0</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2003-01-18</td>\n",
              "      <td>204.0</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2003-01-19</td>\n",
              "      <td>204.0</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   rank_date   rank  ... three_year_ago_weighted weighted_points\n",
              "0 2003-01-15  204.0  ...                     0.0             0.0\n",
              "1 2003-01-16  204.0  ...                     0.0             0.0\n",
              "2 2003-01-17  204.0  ...                     0.0             0.0\n",
              "3 2003-01-18  204.0  ...                     0.0             0.0\n",
              "4 2003-01-19  204.0  ...                     0.0             0.0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 309
        },
        "id": "4apvKwt-UOnB",
        "outputId": "e965f776-b5ab-4c7b-c585-4480dd55f9c1"
      },
      "source": [
        "matches.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>tournament</th>\n",
              "      <th>city</th>\n",
              "      <th>country</th>\n",
              "      <th>neutral</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1872-11-30</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>England</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Glasgow</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1873-03-08</td>\n",
              "      <td>England</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>London</td>\n",
              "      <td>England</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1874-03-07</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>England</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Glasgow</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1875-03-06</td>\n",
              "      <td>England</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>London</td>\n",
              "      <td>England</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1876-03-04</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>England</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Glasgow</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date home_team away_team  ...     city   country neutral\n",
              "0 1872-11-30  Scotland   England  ...  Glasgow  Scotland   False\n",
              "1 1873-03-08   England  Scotland  ...   London   England   False\n",
              "2 1874-03-07  Scotland   England  ...  Glasgow  Scotland   False\n",
              "3 1875-03-06   England  Scotland  ...   London   England   False\n",
              "4 1876-03-04  Scotland   England  ...  Glasgow  Scotland   False\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "rZWKndJEUV9s",
        "outputId": "fb14caf4-298e-451b-93b5-dac73e97fa3a"
      },
      "source": [
        "matches = matches.merge(rankings,\r\n",
        "                        left_on=['date','home_team'],\r\n",
        "                        right_on=['rank_date', 'country_full'])\r\n",
        "\r\n",
        "matches.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>tournament</th>\n",
              "      <th>city</th>\n",
              "      <th>country</th>\n",
              "      <th>neutral</th>\n",
              "      <th>rank_date</th>\n",
              "      <th>rank</th>\n",
              "      <th>country_full</th>\n",
              "      <th>country_abrv</th>\n",
              "      <th>cur_year_avg_weighted</th>\n",
              "      <th>two_year_ago_weighted</th>\n",
              "      <th>three_year_ago_weighted</th>\n",
              "      <th>weighted_points</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>La Paz</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>59.0</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>BOL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Maceió</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BRA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>Venezuela</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>Quito</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>35.0</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>ECU</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Conakry</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>65.0</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>GUI</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Paraguay</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>Asunción</td>\n",
              "      <td>Paraguay</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>67.0</td>\n",
              "      <td>Paraguay</td>\n",
              "      <td>PAR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date home_team  ... three_year_ago_weighted  weighted_points\n",
              "0 1993-08-08   Bolivia  ...                     0.0              0.0\n",
              "1 1993-08-08    Brazil  ...                     0.0              0.0\n",
              "2 1993-08-08   Ecuador  ...                     0.0              0.0\n",
              "3 1993-08-08    Guinea  ...                     0.0              0.0\n",
              "4 1993-08-08  Paraguay  ...                     0.0              0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "Ly4WxZHLUv_S",
        "outputId": "569034af-d3e8-4f83-d00e-dff1b5c5c20e"
      },
      "source": [
        "matches = matches.merge(rankings,\r\n",
        "                        left_on=['date','away_team'],\r\n",
        "                        right_on=['rank_date','country_full'],\r\n",
        "                        suffixes=('_home','_away'))\r\n",
        "matches.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
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              "      <th>rank_date_home</th>\n",
              "      <th>rank_home</th>\n",
              "      <th>country_full_home</th>\n",
              "      <th>country_abrv_home</th>\n",
              "      <th>cur_year_avg_weighted_home</th>\n",
              "      <th>two_year_ago_weighted_home</th>\n",
              "      <th>three_year_ago_weighted_home</th>\n",
              "      <th>weighted_points_home</th>\n",
              "      <th>rank_date_away</th>\n",
              "      <th>rank_away</th>\n",
              "      <th>country_full_away</th>\n",
              "      <th>country_abrv_away</th>\n",
              "      <th>cur_year_avg_weighted_away</th>\n",
              "      <th>two_year_ago_weighted_away</th>\n",
              "      <th>three_year_ago_weighted_away</th>\n",
              "      <th>weighted_points_away</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>La Paz</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>59.0</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>BOL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>22.0</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>URU</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Maceió</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BRA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>14.0</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>MEX</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>Venezuela</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>Quito</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>35.0</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>ECU</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>94.0</td>\n",
              "      <td>Venezuela</td>\n",
              "      <td>VEN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Conakry</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>65.0</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>GUI</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>86.0</td>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>SLE</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Paraguay</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>Asunción</td>\n",
              "      <td>Paraguay</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>67.0</td>\n",
              "      <td>Paraguay</td>\n",
              "      <td>PAR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>ARG</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date home_team  ... three_year_ago_weighted_away  weighted_points_away\n",
              "0 1993-08-08   Bolivia  ...                          0.0                   0.0\n",
              "1 1993-08-08    Brazil  ...                          0.0                   0.0\n",
              "2 1993-08-08   Ecuador  ...                          0.0                   0.0\n",
              "3 1993-08-08    Guinea  ...                          0.0                   0.0\n",
              "4 1993-08-08  Paraguay  ...                          0.0                   0.0\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "5dDRVD4RVXyN",
        "outputId": "09e11919-0afa-4b6b-b6c9-2983005b4681"
      },
      "source": [
        "matches['rank_difference'] = matches[\"rank_home\"] - matches['rank_away']\r\n",
        "matches[\"average_rank\"] = (matches['rank_home'] + matches['rank_away'])/2\r\n",
        "matches['point_difference'] = matches ['weighted_points_home'] - matches['weighted_points_away']\r\n",
        "matches['score_difference'] = matches['home_score'] - matches['away_score']\r\n",
        "matches['is_won'] = matches['score_difference'] > 0 # take draw as lost\r\n",
        "matches['is_stake'] = matches['tournament'] != 'Friendly'\r\n",
        "\r\n",
        "matches.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
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              "      <th>cur_year_avg_weighted_home</th>\n",
              "      <th>two_year_ago_weighted_home</th>\n",
              "      <th>three_year_ago_weighted_home</th>\n",
              "      <th>weighted_points_home</th>\n",
              "      <th>rank_date_away</th>\n",
              "      <th>rank_away</th>\n",
              "      <th>country_full_away</th>\n",
              "      <th>country_abrv_away</th>\n",
              "      <th>cur_year_avg_weighted_away</th>\n",
              "      <th>two_year_ago_weighted_away</th>\n",
              "      <th>three_year_ago_weighted_away</th>\n",
              "      <th>weighted_points_away</th>\n",
              "      <th>rank_difference</th>\n",
              "      <th>average_rank</th>\n",
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              "      <th>score_difference</th>\n",
              "      <th>is_won</th>\n",
              "      <th>is_stake</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>La Paz</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>59.0</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>BOL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>22.0</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>URU</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>40.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Maceió</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>BRA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>14.0</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>MEX</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>Venezuela</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>Quito</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>35.0</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>ECU</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>94.0</td>\n",
              "      <td>Venezuela</td>\n",
              "      <td>VEN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-59.0</td>\n",
              "      <td>64.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Conakry</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>65.0</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>GUI</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>86.0</td>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>SLE</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-21.0</td>\n",
              "      <td>75.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>Paraguay</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>Asunción</td>\n",
              "      <td>Paraguay</td>\n",
              "      <td>False</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>67.0</td>\n",
              "      <td>Paraguay</td>\n",
              "      <td>PAR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1993-08-08</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>ARG</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date home_team     away_team  ...  score_difference  is_won is_stake\n",
              "0 1993-08-08   Bolivia       Uruguay  ...                 2    True     True\n",
              "1 1993-08-08    Brazil        Mexico  ...                 0   False    False\n",
              "2 1993-08-08   Ecuador     Venezuela  ...                 5    True     True\n",
              "3 1993-08-08    Guinea  Sierra Leone  ...                 1    True    False\n",
              "4 1993-08-08  Paraguay     Argentina  ...                -2   False     True\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "mfmFZczoZRUV",
        "outputId": "31ed8cac-bef7-415e-91a4-eac9d24a26a3"
      },
      "source": [
        "from sklearn import linear_model\r\n",
        "from sklearn import ensemble\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score\r\n",
        "from sklearn.pipeline import Pipeline\r\n",
        "from sklearn.preprocessing import PolynomialFeatures\r\n",
        "from sklearn.metrics import classification_report\r\n",
        "\r\n",
        "X,y = matches.loc[:,['average_rank','rank_difference','point_difference', 'is_stake']], matches['is_won']\r\n",
        "\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(\r\n",
        "    X, y, test_size=0.2,random_state=42\r\n",
        ")\r\n",
        "\r\n",
        "logreg = linear_model.LogisticRegression(C=1e-5)\r\n",
        "features = PolynomialFeatures(degree=2)\r\n",
        "model= Pipeline([\r\n",
        "                 ('Polynomial_features', features),\r\n",
        "                 ('logistic_regression', logreg)\r\n",
        "])\r\n",
        "model=model.fit(X_train, y_train)\r\n",
        "\r\n",
        "#plot our results\r\n",
        "\r\n",
        "fpr, tpr, _ = roc_curve(y_test, model.predict_proba(X_test)[:,1])\r\n",
        "plt.figure(figsize=(15,5))\r\n",
        "ax = plt.subplot(1,3,1)\r\n",
        "ax.plot([0, 1], [0, 1], 'k--')\r\n",
        "ax.plot(fpr, tpr)\r\n",
        "ax.set_title('AUC score is {0:0.2}'.format(roc_auc_score(y_test, model.predict_proba(X_test)[:,1])))\r\n",
        "\r\n",
        "predicted = model.predict(X_test)\r\n",
        "print(classification_report(y_test, predicted))\r\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.67      0.69      0.68      1815\n",
            "        True       0.68      0.66      0.67      1819\n",
            "\n",
            "    accuracy                           0.67      3634\n",
            "   macro avg       0.67      0.67      0.67      3634\n",
            "weighted avg       0.67      0.67      0.67      3634\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "id": "L7khyKJuFfK2",
        "outputId": "d9bed3de-910f-4c7c-ffa9-1d34b1f6a06f"
      },
      "source": [
        "features = ['average_rank', 'rank_difference', 'point_difference']\r\n",
        "\r\n",
        "# Get the wrongly predicted matches\r\n",
        "wrongs = y_test != model.predict(X_test)\r\n",
        "\r\n",
        "for feature in features:\r\n",
        "    plt.figure()\r\n",
        "    plt.title(feature)\r\n",
        "    X_test.loc[wrongs, feature].plot.kde()\r\n",
        "    X.loc[:, feature].plot.kde()\r\n",
        "    plt.legend(['wrongs', 'all'])\r\n",
        "    \r\n",
        "print(\"Stakes distribution in the wrong predictions\")\r\n",
        "print(X_test.loc[wrongs, 'is_stake'].value_counts() / wrongs.sum())\r\n",
        "print(\"Stakes distribution overall\")\r\n",
        "print(X['is_stake'].value_counts() / X.shape[0])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stakes distribution in the wrong predictions\n",
            "True     0.57877\n",
            "False    0.42123\n",
            "Name: is_stake, dtype: float64\n",
            "Stakes distribution overall\n",
            "True     0.615643\n",
            "False    0.384357\n",
            "Name: is_stake, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "FJT4XcySGMtk",
        "outputId": "5d62bc63-c70b-4775-bc1e-57bcc845c0a4"
      },
      "source": [
        "# let's define a small margin that we add, so that we predict draws when probabilies lie between 0.475 and 0.525\r\n",
        "margin = 0.025\r\n",
        "\r\n",
        "# let's define the rankings at the time of the World Cup (so we sort on most recent rankings)\r\n",
        "world_cup_rankings = rankings.loc[(rankings['rank_date'] == rankings['rank_date'].max()) & \r\n",
        "                                    rankings['country_full'].isin(world_cup.index.unique())]\r\n",
        "world_cup_rankings = world_cup_rankings.set_index(['country_full'])\r\n",
        "world_cup_rankings.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank_date</th>\n",
              "      <th>rank</th>\n",
              "      <th>country_abrv</th>\n",
              "      <th>cur_year_avg_weighted</th>\n",
              "      <th>two_year_ago_weighted</th>\n",
              "      <th>three_year_ago_weighted</th>\n",
              "      <th>weighted_points</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country_full</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>2018-06-07</td>\n",
              "      <td>5.0</td>\n",
              "      <td>ARG</td>\n",
              "      <td>404.07</td>\n",
              "      <td>248.99</td>\n",
              "      <td>183.59</td>\n",
              "      <td>836.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>2018-06-07</td>\n",
              "      <td>36.0</td>\n",
              "      <td>AUS</td>\n",
              "      <td>366.60</td>\n",
              "      <td>98.16</td>\n",
              "      <td>59.79</td>\n",
              "      <td>524.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belgium</th>\n",
              "      <td>2018-06-07</td>\n",
              "      <td>3.0</td>\n",
              "      <td>BEL</td>\n",
              "      <td>629.98</td>\n",
              "      <td>158.94</td>\n",
              "      <td>186.58</td>\n",
              "      <td>975.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>2018-06-07</td>\n",
              "      <td>2.0</td>\n",
              "      <td>BRA</td>\n",
              "      <td>558.95</td>\n",
              "      <td>168.06</td>\n",
              "      <td>162.38</td>\n",
              "      <td>889.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colombia</th>\n",
              "      <td>2018-06-07</td>\n",
              "      <td>16.0</td>\n",
              "      <td>COL</td>\n",
              "      <td>292.09</td>\n",
              "      <td>199.73</td>\n",
              "      <td>166.38</td>\n",
              "      <td>658.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              rank_date  rank  ... three_year_ago_weighted  weighted_points\n",
              "country_full                   ...                                         \n",
              "Argentina    2018-06-07   5.0  ...                  183.59           836.65\n",
              "Australia    2018-06-07  36.0  ...                   59.79           524.55\n",
              "Belgium      2018-06-07   3.0  ...                  186.58           975.50\n",
              "Brazil       2018-06-07   2.0  ...                  162.38           889.39\n",
              "Colombia     2018-06-07  16.0  ...                  166.38           658.20\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYHJjmN9GSJT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}