FIFA_2022.ipynb

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      "source": [
        "# **FIFA 2022 Predictions**\n",
        "   Project by : P & P Ranasinghe"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "R8MZR0POfGbW"
      }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ls2oHWlbI1R"
      },
      "source": [
        "## Analyzing the Available Data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dataset Link**\n",
        "\n",
        "This dataset includes 44,152 results of international football matches starting from the very first official match in 1872 up to 2022.\n",
        "The matches range from FIFA World Cup to regular friendly matches. The matches are strictly men's full internationals.\n",
        "\n",
        "[Dataset Link](https://drive.google.com/file/d/1vhG-TY2Nihi-jnw9IK681qUyDWMpjNAS/view?usp=sharing)"
      ],
      "metadata": {
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      "execution_count": 52,
      "metadata": {
        "id": "Fs5Zi3tSbI1S"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "import random\n",
        "from IPython.display import Image\n",
        "random_num = 85\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "if Error due to Matplotlib try to install \n",
        "\n",
        "\n",
        "```\n",
        "!pip install matplotlib==3.5.3\n",
        "```\n"
      ],
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        "outputId": "1827bf8c-4448-475a-f5bc-441049bc5346"
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          "data": {
            "text/plain": [
              "         date home_team away_team  home_score  away_score tournament     city  \\\n",
              "0  1872-11-30  Scotland   England           0           0   Friendly  Glasgow   \n",
              "1  1873-03-08   England  Scotland           4           2   Friendly   London   \n",
              "2  1874-03-07  Scotland   England           2           1   Friendly  Glasgow   \n",
              "3  1875-03-06   England  Scotland           2           2   Friendly   London   \n",
              "4  1876-03-04  Scotland   England           3           0   Friendly  Glasgow   \n",
              "5  1876-03-25  Scotland     Wales           4           0   Friendly  Glasgow   \n",
              "6  1877-03-03   England  Scotland           1           3   Friendly   London   \n",
              "7  1877-03-05     Wales  Scotland           0           2   Friendly  Wrexham   \n",
              "8  1878-03-02  Scotland   England           7           2   Friendly  Glasgow   \n",
              "9  1878-03-23  Scotland     Wales           9           0   Friendly  Glasgow   \n",
              "\n",
              "    country  neutral  \n",
              "0  Scotland    False  \n",
              "1   England    False  \n",
              "2  Scotland    False  \n",
              "3   England    False  \n",
              "4  Scotland    False  \n",
              "5  Scotland    False  \n",
              "6   England    False  \n",
              "7     Wales    False  \n",
              "8  Scotland    False  \n",
              "9  Scotland    False  "
            ],
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              "      <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",
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              "      <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",
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              "    <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",
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              "    <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",
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              "    <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",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1876-03-25</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>Wales</td>\n",
              "      <td>4</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>6</th>\n",
              "      <td>1877-03-03</td>\n",
              "      <td>England</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>London</td>\n",
              "      <td>England</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1877-03-05</td>\n",
              "      <td>Wales</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Wrexham</td>\n",
              "      <td>Wales</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1878-03-02</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>England</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Glasgow</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1878-03-23</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>Wales</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Glasgow</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>False</td>\n",
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      ],
      "source": [
        "#Reading the dataset\n",
        "df=pd.read_csv('https://raw.githubusercontent.com/pamudu123/FIFA-2022-Predictions/main/results.csv',encoding=\"unicode_escape\")\n",
        "df.head(10)"
      ]
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Size : (42082, 9)\n"
          ]
        },
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          "data": {
            "text/plain": [
              "         home_score    away_score\n",
              "count  42082.000000  42082.000000\n",
              "mean       1.743691      1.186541\n",
              "std        1.752459      1.403957\n",
              "min        0.000000      0.000000\n",
              "25%        1.000000      0.000000\n",
              "50%        1.000000      1.000000\n",
              "75%        2.000000      2.000000\n",
              "max       31.000000     21.000000"
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              "      <td>42082.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>1.743691</td>\n",
              "      <td>1.186541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.752459</td>\n",
              "      <td>1.403957</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <th>max</th>\n",
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          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "print(f'Dataset Size : {df.shape}')\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preprocessing\n",
        "Finding outmissing values\n",
        "\n"
      ],
      "metadata": {
        "id": "BwdLn_vNhuLd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "460EQZZubI1W",
        "outputId": "b1ac1bbf-6268-4e92-fdba-29d231ba820b",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date          0\n",
              "home_team     0\n",
              "away_team     0\n",
              "home_score    0\n",
              "away_score    0\n",
              "tournament    0\n",
              "city          0\n",
              "country       0\n",
              "neutral       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "> No any missing values\n",
        "\n",
        "> **We are good to go ‼**\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Zzo4Zc9OiEeB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Finding Outliers in the numerical data columns"
      ],
      "metadata": {
        "id": "JiBHsCseioML"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "L0EFxtObbI1X",
        "outputId": "bdee3058-308b-4095-e0ed-7c85a3581432"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig = plt.figure(figsize =(8, 4));\n",
        "plt.boxplot([df[\"home_score\"],df[\"away_score\"]]);\n",
        "plt.xticks([1,2], [\"Home Score\", \"Away Score\"]);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Boxplot says there are many outliers.\n",
        "\n",
        "Let's remove those outlier,It will make our Machine Learning model more accurate.\n",
        "\n",
        "Asume maximum goals that one team can score is 15\n",
        "\n",
        "> In reality, some matches score more than 15 goals by one team. For that scenario, we have little data. To increase model accuracy and avoid wrong predictions, we assume the maximum number of goals as 15\n",
        "\n"
      ],
      "metadata": {
        "id": "6-nrUODfjU89"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YRw97ejibI1X",
        "outputId": "b58014a6-26b8-480d-80aa-ac4311b4ef9b"
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              "         date home_team away_team  home_score  away_score tournament     city  \\\n",
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              "\n",
              "    country  neutral  \n",
              "0  Scotland    False  \n",
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      "source": [
        "higher_home=15\n",
        "higher_away=15\n",
        "df=df[(df[\"home_score\"]<higher_home) & (df[\"away_score\"]<higher_away)]\n",
        "df.head(5)\n",
        "# Dataset after removing outliers"
      ]
    },
    {
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      "execution_count": 58,
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              "         date home_team away_team  home_score  away_score tournament     city  \\\n",
              "0  1872-11-30  Scotland   England           0           0   Friendly  Glasgow   \n",
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              "4  1876-03-04  Scotland   England           3           0   Friendly  Glasgow   \n",
              "\n",
              "    country  neutral Win_Statues  \n",
              "0  Scotland    False        Draw  \n",
              "1   England    False         Win  \n",
              "2  Scotland    False         Win  \n",
              "3   England    False        Draw  \n",
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      ],
      "source": [
        "# Making a new colum named \"Win_Statues\" to store the result(Win,Lost,Draw) of the home team\n",
        "\n",
        "conditions  = [df[\"home_score\"] ==df[\"away_score\"], df[\"home_score\"] > df[\"away_score\"] , df[\"home_score\"] < df[\"away_score\"]]\n",
        "choices     = [ \"Draw\", 'Win', 'Lost' ]\n",
        "df[\"Win_Statues\"] = np.select(conditions, choices)\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 306 Countries in the home_team Column\n",
            "\n",
            "Countries-['Scotland' 'England' 'Wales' 'Northern Ireland' 'United States' 'Uruguay'\n",
            " 'Austria' 'Hungary' 'Argentina' 'Belgium' 'France' 'Netherlands'\n",
            " 'Czechoslovakia' 'Switzerland' 'Sweden' 'Germany' 'Italy' 'Chile'\n",
            " 'Norway' 'Finland' 'Luxembourg' 'Russia' 'Denmark' 'Catalonia'\n",
            " 'Basque Country' 'Brazil' 'Paraguay' 'Japan' 'Canada' 'Estonia'\n",
            " 'Costa Rica' 'Guatemala' 'Spain' 'Brittany' 'Poland' 'Yugoslavia'\n",
            " 'New Zealand' 'Romania' 'Latvia' 'Galicia' 'Portugal' 'Andalusia'\n",
            " 'China PR' 'Australia' 'Lithuania' 'Turkey' 'Central Spain' 'Mexico'\n",
            " 'Aruba' 'Egypt' 'Haiti' 'Philippines' 'Bulgaria' 'Jamaica' 'Kenya'\n",
            " 'Bolivia' 'Peru' 'Honduras' 'Guyana' 'Uganda' 'Belarus' 'El Salvador'\n",
            " 'Barbados' 'Republic of Ireland' 'Trinidad and Tobago' 'Greece'\n",
            " 'CuraÃ§ao' 'Dominica' 'Silesia' 'Guadeloupe' 'Israel' 'Suriname'\n",
            " 'French Guiana' 'Cuba' 'Colombia' 'Ecuador' 'Saint Kitts and Nevis'\n",
            " 'Panama' 'Slovakia' 'Manchukuo' 'Croatia' 'Nicaragua' 'Afghanistan'\n",
            " 'India' 'Martinique' 'Zimbabwe' 'Iceland' 'Albania' 'Madagascar' 'Zambia'\n",
            " 'Mauritius' 'Tanzania' 'Iran' 'Djibouti' 'DR Congo' 'Vietnam' 'Macau'\n",
            " 'Ethiopia' 'Puerto Rico' 'RÃ©union' 'Sierra Leone' 'Zanzibar'\n",
            " 'South Korea' 'Ghana' 'South Africa' 'New Caledonia' 'Fiji' 'Nigeria'\n",
            " 'Venezuela' 'Burma' 'Sri Lanka' 'Tahiti' 'Gambia' 'Hong Kong' 'Singapore'\n",
            " 'Malaysia' 'Indonesia' 'Guinea-Bissau' 'German DR' 'Vanuatu' 'Kernow'\n",
            " 'Saarland' 'Cambodia' 'Lebanon' 'Pakistan' 'Vietnam Republic'\n",
            " 'North Korea' 'Togo' 'Sudan' 'Malta' 'Syria' 'Tunisia' 'Malawi' 'Morocco'\n",
            " 'Benin' 'Cameroon' 'Central African Republic' 'Gabon' 'Ivory Coast'\n",
            " 'Congo' 'Mali' 'North Vietnam' 'Mongolia' 'Chinese Taipei' 'Cyprus'\n",
            " 'Iraq' 'Saint Lucia' 'Grenada' 'Thailand' 'Senegal' 'Libya' 'Guinea'\n",
            " 'Algeria' 'Kuwait' 'Jordan' 'Solomon Islands' 'Liberia' 'Laos'\n",
            " 'Saint Vincent and the Grenadines' 'Bermuda' 'Niger' 'Bahrain'\n",
            " 'Montenegro' 'Palestine' 'Papua New Guinea' 'Burkina Faso' 'Mauritania'\n",
            " 'Saudi Arabia' 'Eswatini' 'Somalia' 'Lesotho' 'Qatar'\n",
            " 'Antigua and Barbuda' 'Faroe Islands' 'Bangladesh' 'Oman' 'Yemen DPR'\n",
            " 'Burundi' 'Yemen' 'Mozambique' 'Guam' 'Chad' 'Angola'\n",
            " 'Dominican Republic' 'Seychelles' 'Rwanda' 'SÃ£o TomÃ© and PrÃ\\xadncipe'\n",
            " 'Botswana' 'Northern Cyprus' 'Cape Verde' 'Kyrgyzstan' 'Georgia'\n",
            " 'Azerbaijan' 'Kiribati' 'Tonga' 'Wallis Islands and Futuna'\n",
            " 'United Arab Emirates' 'Brunei' 'Equatorial Guinea' 'Liechtenstein'\n",
            " 'Nepal' 'Greenland' 'Niue' 'Samoa' 'American Samoa' 'Belize' 'Anguilla'\n",
            " 'Cayman Islands' 'Palau' 'Sint Maarten' 'Namibia' 'Ã\\x85land Islands'\n",
            " 'Ynys MÃ´n' 'Saint Martin' 'San Marino' 'Slovenia' 'Jersey' 'Shetland'\n",
            " 'Isle of Wight' 'Moldova' 'Ukraine' 'Kazakhstan' 'Tajikistan'\n",
            " 'Uzbekistan' 'Turkmenistan' 'Armenia' 'Czech Republic' 'Guernsey'\n",
            " 'Gibraltar' 'Isle of Man' 'North Macedonia' 'Montserrat' 'Serbia'\n",
            " 'Cook Islands' 'Canary Islands' 'Bosnia and Herzegovina' 'Maldives'\n",
            " 'Andorra' 'British Virgin Islands' 'FrÃ¸ya' 'Hitra' 'U.S. Virgin Islands'\n",
            " 'Corsica' 'Eritrea' 'Bahamas' 'Gotland' 'Saare County' 'Rhodes'\n",
            " 'Micronesia' 'Bhutan' 'Orkney' 'Monaco' 'Tuvalu' 'Alderney' 'Mayotte'\n",
            " 'Turks and Caicos Islands' 'East Timor' 'Western Isles'\n",
            " 'Falkland Islands' 'Kosovo' 'Republic of St. Pauli' 'GÄ\\x83gÄ\\x83uzia'\n",
            " 'Tibet' 'Crimea' 'Occitania' 'SÃ¡pmi' 'Northern Mariana Islands'\n",
            " 'Menorca' 'Comoros' 'Provence' 'Arameans Suryoye' 'Padania'\n",
            " 'Iraqi Kurdistan' 'Gozo' 'Bonaire' 'Western Sahara' 'Raetia'\n",
            " 'Tamil Eelam' 'South Sudan' 'Abkhazia' 'Artsakh' 'Madrid' 'Vatican City'\n",
            " 'Ellan Vannin' 'South Ossetia' 'Darfur' 'County of Nice' 'SzÃ©kely Land'\n",
            " 'Romani people' 'FelvidÃ©k' 'Chagos Islands' 'United Koreans in Japan'\n",
            " 'Somaliland' 'Western Armenia' 'Barawa' 'KÃ¡rpÃ¡talja' 'Yorkshire'\n",
            " 'Panjab' 'Matabeleland' 'Cascadia' 'Kabylia' 'Timor-Leste' 'Myanmar'\n",
            " 'Parishes of Jersey' 'Chameria' 'Saint Helena']\n"
          ]
        }
      ],
      "source": [
        "# see what are the Countries in the dataset\n",
        "\n",
        "countries=df.home_team.unique()\n",
        "print(f\"There are {len(countries)} Countries in the home_team Column\\n\")\n",
        "print(f\"Countries-{countries}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HUhKA819bI1a"
      },
      "source": [
        "**Type of the Matches**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "ZD5tDv37bI1a",
        "outputId": "423b482f-67f6-41d1-c9d0-402b7b907170"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "rank_bound = 10\n",
        "ax = df.tournament.value_counts()[:rank_bound].sort_values()\n",
        "value = ax.values\n",
        "label = ax.index\n",
        "\n",
        "plt.figure(figsize=(14,6))\n",
        "plt.barh(y=label, width=value, edgecolor=\"k\")\n",
        "for i in range(rank_bound):\n",
        "    plt.text(x=50,y=i-0.1,s=value[i],color=\"w\",fontsize=12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xYFU0QMMbI1b"
      },
      "source": [
        "**Teams played most Matches**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "0o967RHObI1b",
        "outputId": "9bdf36e8-b443-401e-f10e-e57b89f31478"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "rank_bound = 10\n",
        "ax = df.country.value_counts()[:rank_bound].sort_values()\n",
        "value = ax.values\n",
        "label = ax.index\n",
        "\n",
        "plt.figure(figsize=(14,6))\n",
        "plt.barh(y=label, width=value, edgecolor=\"k\")\n",
        "for i in range(rank_bound):\n",
        "    plt.text(x=10,y=i-0.1,s=value[i],color=\"w\",fontsize=12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Zrs8U0VbI1c"
      },
      "source": [
        "**Number of Matches played in equal intervals**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "7CQD9-xebI1c",
        "outputId": "1d7e3ab0-e855-42a9-aba7-c87821ace092"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "years = []\n",
        "for date in df.date:\n",
        "    years.append(int(str(date)[0:4]))\n",
        "plt.figure(figsize=(14,6))\n",
        "plt.hist(years, density=True, bins=12, edgecolor=\"k\")\n",
        "plt.title(\"Histogram of Years\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Most matches are played after 1960"
      ],
      "metadata": {
        "id": "YnS5vdLUmmjU"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YulnHXNZbI1d"
      },
      "source": [
        "### Finding out the impact hosting a major tournament helps a country's chances in winning matches?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now take the rows that home_team == Country \n",
        "\n",
        "And romove the data of friendly matches"
      ],
      "metadata": {
        "id": "iLTVad8dm4GP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7y4hrGmpbI1d",
        "outputId": "8a1eaae5-64c8-4041-d94e-0214711842d7",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          date         home_team         away_team  home_score  away_score  \\\n",
              "29  1884-01-26  Northern Ireland          Scotland           0           5   \n",
              "30  1884-02-09             Wales  Northern Ireland           6           0   \n",
              "31  1884-02-23  Northern Ireland           England           1           8   \n",
              "32  1884-03-15          Scotland           England           1           0   \n",
              "33  1884-03-17             Wales           England           0           4   \n",
              "\n",
              "              tournament     city              country  neutral Win_Statues  \n",
              "29  British Championship  Belfast  Republic of Ireland    False        Lost  \n",
              "30  British Championship  Wrexham                Wales    False         Win  \n",
              "31  British Championship  Belfast  Republic of Ireland    False        Lost  \n",
              "32  British Championship  Glasgow             Scotland    False         Win  \n",
              "33  British Championship  Wrexham                Wales    False        Lost  "
            ],
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              "      <th>date</th>\n",
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              "      <td>1884-01-26</td>\n",
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              "      <td>1884-03-15</td>\n",
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              "      <td>Glasgow</td>\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "data_home=df.loc[df[\"home_team\"]==df[\"country\"]]\n",
        "data_home=df.loc[df[\"tournament\"] != \"Friendly\"]\n",
        "data_home.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**How home team performs in tournament matches**"
      ],
      "metadata": {
        "id": "2Ihjngj3nkTS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
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          "height": 394
        },
        "id": "ozETBsUqbI1d",
        "outputId": "bcef10a2-3a2b-45fb-80e8-c301567df143"
      },
      "outputs": [
        {
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              "      <th class=\"index_name level0\" >Win_Statues</th>\n",
              "      <th class=\"col_heading level0 col0\" >Draw</th>\n",
              "      <th class=\"col_heading level0 col1\" >Lost</th>\n",
              "      <th class=\"col_heading level0 col2\" >Win</th>\n",
              "      <th class=\"col_heading level0 col3\" >All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >tournament</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_37ad6_level0_row0\" class=\"row_heading level0 row0\" >All</th>\n",
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              "      <td id=\"T_37ad6_row0_col3\" class=\"data row0 col3\" >24849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_37ad6_level0_row1\" class=\"row_heading level0 row1\" >FIFA World Cup qualification</th>\n",
              "      <td id=\"T_37ad6_row1_col0\" class=\"data row1 col0\" >1570</td>\n",
              "      <td id=\"T_37ad6_row1_col1\" class=\"data row1 col1\" >2000</td>\n",
              "      <td id=\"T_37ad6_row1_col2\" class=\"data row1 col2\" >3787</td>\n",
              "      <td id=\"T_37ad6_row1_col3\" class=\"data row1 col3\" >7357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_37ad6_level0_row2\" class=\"row_heading level0 row2\" >UEFA Euro qualification</th>\n",
              "      <td id=\"T_37ad6_row2_col0\" class=\"data row2 col0\" >506</td>\n",
              "      <td id=\"T_37ad6_row2_col1\" class=\"data row2 col1\" >815</td>\n",
              "      <td id=\"T_37ad6_row2_col2\" class=\"data row2 col2\" >1261</td>\n",
              "      <td id=\"T_37ad6_row2_col3\" class=\"data row2 col3\" >2582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_37ad6_level0_row3\" class=\"row_heading level0 row3\" >African Cup of Nations qualification</th>\n",
              "      <td id=\"T_37ad6_row3_col0\" class=\"data row3 col0\" >418</td>\n",
              "      <td id=\"T_37ad6_row3_col1\" class=\"data row3 col1\" >353</td>\n",
              "      <td id=\"T_37ad6_row3_col2\" class=\"data row3 col2\" >948</td>\n",
              "      <td id=\"T_37ad6_row3_col3\" class=\"data row3 col3\" >1719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_37ad6_level0_row4\" class=\"row_heading level0 row4\" >FIFA World Cup</th>\n",
              "      <td id=\"T_37ad6_row4_col0\" class=\"data row4 col0\" >199</td>\n",
              "      <td id=\"T_37ad6_row4_col1\" class=\"data row4 col1\" >290</td>\n",
              "      <td id=\"T_37ad6_row4_col2\" class=\"data row4 col2\" >411</td>\n",
              "      <td id=\"T_37ad6_row4_col3\" class=\"data row4 col3\" >900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_37ad6_level0_row5\" class=\"row_heading level0 row5\" >Copa AmÃ©rica</th>\n",
              "      <td id=\"T_37ad6_row5_col0\" class=\"data row5 col0\" >172</td>\n",
              "      <td id=\"T_37ad6_row5_col1\" class=\"data row5 col1\" >225</td>\n",
              "      <td id=\"T_37ad6_row5_col2\" class=\"data row5 col2\" >416</td>\n",
              "      <td id=\"T_37ad6_row5_col3\" class=\"data row5 col3\" >813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_37ad6_level0_row6\" class=\"row_heading level0 row6\" >AFC Asian Cup qualification</th>\n",
              "      <td id=\"T_37ad6_row6_col0\" class=\"data row6 col0\" >123</td>\n",
              "      <td id=\"T_37ad6_row6_col1\" class=\"data row6 col1\" >174</td>\n",
              "      <td id=\"T_37ad6_row6_col2\" class=\"data row6 col2\" >424</td>\n",
              "      <td id=\"T_37ad6_row6_col3\" class=\"data row6 col3\" >721</td>\n",
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              "      <th id=\"T_37ad6_level0_row7\" class=\"row_heading level0 row7\" >African Cup of Nations</th>\n",
              "      <td id=\"T_37ad6_row7_col0\" class=\"data row7 col0\" >193</td>\n",
              "      <td id=\"T_37ad6_row7_col1\" class=\"data row7 col1\" >186</td>\n",
              "      <td id=\"T_37ad6_row7_col2\" class=\"data row7 col2\" >311</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_37ad6_level0_row8\" class=\"row_heading level0 row8\" >CECAFA Cup</th>\n",
              "      <td id=\"T_37ad6_row8_col0\" class=\"data row8 col0\" >147</td>\n",
              "      <td id=\"T_37ad6_row8_col1\" class=\"data row8 col1\" >185</td>\n",
              "      <td id=\"T_37ad6_row8_col2\" class=\"data row8 col2\" >288</td>\n",
              "      <td id=\"T_37ad6_row8_col3\" class=\"data row8 col3\" >620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_37ad6_level0_row9\" class=\"row_heading level0 row9\" >CFU Caribbean Cup qualification</th>\n",
              "      <td id=\"T_37ad6_row9_col0\" class=\"data row9 col0\" >124</td>\n",
              "      <td id=\"T_37ad6_row9_col1\" class=\"data row9 col1\" >170</td>\n",
              "      <td id=\"T_37ad6_row9_col2\" class=\"data row9 col2\" >310</td>\n",
              "      <td id=\"T_37ad6_row9_col3\" class=\"data row9 col3\" >604</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "tournament_df=pd.crosstab(data_home[\"tournament\"], data_home[\"Win_Statues\"],margins=True)\n",
        "tournament_df=tournament_df.sort_values(\"All\",ascending=False).head(10)\n",
        "tournament_df.style.bar(color=\"brown\",subset=[\"Draw\",\"Lost\",\"Win\",\"All\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Home team results**"
      ],
      "metadata": {
        "id": "jB5n0fIVn8A0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "ymKI5W4fbI1e",
        "outputId": "02c26ab1-9473-4aaf-8bba-bfe58a569e7d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(6,4));\n",
        "sns.displot(data_home, x=\"Win_Statues\");\n",
        "plt.title(\"Home Team Winning Status\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRK8Ts2tbI1e",
        "outputId": "c00404ff-b696-4679-c0aa-5b37c77ba6c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Win     12350\n",
              "Lost     7111\n",
              "Draw     5388\n",
              "Name: Win_Statues, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "data_home['Win_Statues'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**How each team perform while playing a tournament in home ground**s"
      ],
      "metadata": {
        "id": "o-Xr6tEmoFw6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets take teams which plays atleast 200 games"
      ],
      "metadata": {
        "id": "CxTI3bULoPoQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "uvxxDtxxbI1e",
        "outputId": "24842c4d-6699-4ccc-d689-2288d2ac8dbd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f39f833b7d0>"
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              "</style>\n",
              "<table id=\"T_8d015_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Win_Statues</th>\n",
              "      <th class=\"col_heading level0 col0\" >Draw</th>\n",
              "      <th class=\"col_heading level0 col1\" >Lost</th>\n",
              "      <th class=\"col_heading level0 col2\" >Win</th>\n",
              "      <th class=\"col_heading level0 col3\" >Total</th>\n",
              "      <th class=\"col_heading level0 col4\" >team_win_probability</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >home_team</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row0\" class=\"row_heading level0 row0\" >Brazil</th>\n",
              "      <td id=\"T_8d015_row0_col0\" class=\"data row0 col0\" >108</td>\n",
              "      <td id=\"T_8d015_row0_col1\" class=\"data row0 col1\" >56</td>\n",
              "      <td id=\"T_8d015_row0_col2\" class=\"data row0 col2\" >406</td>\n",
              "      <td id=\"T_8d015_row0_col3\" class=\"data row0 col3\" >570</td>\n",
              "      <td id=\"T_8d015_row0_col4\" class=\"data row0 col4\" >0.712281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row1\" class=\"row_heading level0 row1\" >Spain</th>\n",
              "      <td id=\"T_8d015_row1_col0\" class=\"data row1 col0\" >66</td>\n",
              "      <td id=\"T_8d015_row1_col1\" class=\"data row1 col1\" >49</td>\n",
              "      <td id=\"T_8d015_row1_col2\" class=\"data row1 col2\" >249</td>\n",
              "      <td id=\"T_8d015_row1_col3\" class=\"data row1 col3\" >364</td>\n",
              "      <td id=\"T_8d015_row1_col4\" class=\"data row1 col4\" >0.684066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row2\" class=\"row_heading level0 row2\" >Argentina</th>\n",
              "      <td id=\"T_8d015_row2_col0\" class=\"data row2 col0\" >120</td>\n",
              "      <td id=\"T_8d015_row2_col1\" class=\"data row2 col1\" >68</td>\n",
              "      <td id=\"T_8d015_row2_col2\" class=\"data row2 col2\" >362</td>\n",
              "      <td id=\"T_8d015_row2_col3\" class=\"data row2 col3\" >550</td>\n",
              "      <td id=\"T_8d015_row2_col4\" class=\"data row2 col4\" >0.658182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row3\" class=\"row_heading level0 row3\" >Ivory Coast</th>\n",
              "      <td id=\"T_8d015_row3_col0\" class=\"data row3 col0\" >64</td>\n",
              "      <td id=\"T_8d015_row3_col1\" class=\"data row3 col1\" >35</td>\n",
              "      <td id=\"T_8d015_row3_col2\" class=\"data row3 col2\" >175</td>\n",
              "      <td id=\"T_8d015_row3_col3\" class=\"data row3 col3\" >274</td>\n",
              "      <td id=\"T_8d015_row3_col4\" class=\"data row3 col4\" >0.638686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row4\" class=\"row_heading level0 row4\" >Iran</th>\n",
              "      <td id=\"T_8d015_row4_col0\" class=\"data row4 col0\" >61</td>\n",
              "      <td id=\"T_8d015_row4_col1\" class=\"data row4 col1\" >42</td>\n",
              "      <td id=\"T_8d015_row4_col2\" class=\"data row4 col2\" >177</td>\n",
              "      <td id=\"T_8d015_row4_col3\" class=\"data row4 col3\" >280</td>\n",
              "      <td id=\"T_8d015_row4_col4\" class=\"data row4 col4\" >0.632143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row5\" class=\"row_heading level0 row5\" >Nigeria</th>\n",
              "      <td id=\"T_8d015_row5_col0\" class=\"data row5 col0\" >67</td>\n",
              "      <td id=\"T_8d015_row5_col1\" class=\"data row5 col1\" >33</td>\n",
              "      <td id=\"T_8d015_row5_col2\" class=\"data row5 col2\" >171</td>\n",
              "      <td id=\"T_8d015_row5_col3\" class=\"data row5 col3\" >271</td>\n",
              "      <td id=\"T_8d015_row5_col4\" class=\"data row5 col4\" >0.630996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row6\" class=\"row_heading level0 row6\" >Egypt</th>\n",
              "      <td id=\"T_8d015_row6_col0\" class=\"data row6 col0\" >68</td>\n",
              "      <td id=\"T_8d015_row6_col1\" class=\"data row6 col1\" >74</td>\n",
              "      <td id=\"T_8d015_row6_col2\" class=\"data row6 col2\" >239</td>\n",
              "      <td id=\"T_8d015_row6_col3\" class=\"data row6 col3\" >381</td>\n",
              "      <td id=\"T_8d015_row6_col4\" class=\"data row6 col4\" >0.627297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row7\" class=\"row_heading level0 row7\" >Italy</th>\n",
              "      <td id=\"T_8d015_row7_col0\" class=\"data row7 col0\" >119</td>\n",
              "      <td id=\"T_8d015_row7_col1\" class=\"data row7 col1\" >49</td>\n",
              "      <td id=\"T_8d015_row7_col2\" class=\"data row7 col2\" >281</td>\n",
              "      <td id=\"T_8d015_row7_col3\" class=\"data row7 col3\" >449</td>\n",
              "      <td id=\"T_8d015_row7_col4\" class=\"data row7 col4\" >0.625835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row8\" class=\"row_heading level0 row8\" >Germany</th>\n",
              "      <td id=\"T_8d015_row8_col0\" class=\"data row8 col0\" >109</td>\n",
              "      <td id=\"T_8d015_row8_col1\" class=\"data row8 col1\" >84</td>\n",
              "      <td id=\"T_8d015_row8_col2\" class=\"data row8 col2\" >320</td>\n",
              "      <td id=\"T_8d015_row8_col3\" class=\"data row8 col3\" >513</td>\n",
              "      <td id=\"T_8d015_row8_col4\" class=\"data row8 col4\" >0.623782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row9\" class=\"row_heading level0 row9\" >England</th>\n",
              "      <td id=\"T_8d015_row9_col0\" class=\"data row9 col0\" >109</td>\n",
              "      <td id=\"T_8d015_row9_col1\" class=\"data row9 col1\" >82</td>\n",
              "      <td id=\"T_8d015_row9_col2\" class=\"data row9 col2\" >314</td>\n",
              "      <td id=\"T_8d015_row9_col3\" class=\"data row9 col3\" >505</td>\n",
              "      <td id=\"T_8d015_row9_col4\" class=\"data row9 col4\" >0.621782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row10\" class=\"row_heading level0 row10\" >Ghana</th>\n",
              "      <td id=\"T_8d015_row10_col0\" class=\"data row10 col0\" >71</td>\n",
              "      <td id=\"T_8d015_row10_col1\" class=\"data row10 col1\" >39</td>\n",
              "      <td id=\"T_8d015_row10_col2\" class=\"data row10 col2\" >177</td>\n",
              "      <td id=\"T_8d015_row10_col3\" class=\"data row10 col3\" >287</td>\n",
              "      <td id=\"T_8d015_row10_col4\" class=\"data row10 col4\" >0.616725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row11\" class=\"row_heading level0 row11\" >Russia</th>\n",
              "      <td id=\"T_8d015_row11_col0\" class=\"data row11 col0\" >70</td>\n",
              "      <td id=\"T_8d015_row11_col1\" class=\"data row11 col1\" >49</td>\n",
              "      <td id=\"T_8d015_row11_col2\" class=\"data row11 col2\" >185</td>\n",
              "      <td id=\"T_8d015_row11_col3\" class=\"data row11 col3\" >304</td>\n",
              "      <td id=\"T_8d015_row11_col4\" class=\"data row11 col4\" >0.608553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row12\" class=\"row_heading level0 row12\" >Czechoslovakia</th>\n",
              "      <td id=\"T_8d015_row12_col0\" class=\"data row12 col0\" >51</td>\n",
              "      <td id=\"T_8d015_row12_col1\" class=\"data row12 col1\" >40</td>\n",
              "      <td id=\"T_8d015_row12_col2\" class=\"data row12 col2\" >139</td>\n",
              "      <td id=\"T_8d015_row12_col3\" class=\"data row12 col3\" >230</td>\n",
              "      <td id=\"T_8d015_row12_col4\" class=\"data row12 col4\" >0.604348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row13\" class=\"row_heading level0 row13\" >Morocco</th>\n",
              "      <td id=\"T_8d015_row13_col0\" class=\"data row13 col0\" >68</td>\n",
              "      <td id=\"T_8d015_row13_col1\" class=\"data row13 col1\" >47</td>\n",
              "      <td id=\"T_8d015_row13_col2\" class=\"data row13 col2\" >171</td>\n",
              "      <td id=\"T_8d015_row13_col3\" class=\"data row13 col3\" >286</td>\n",
              "      <td id=\"T_8d015_row13_col4\" class=\"data row13 col4\" >0.597902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row14\" class=\"row_heading level0 row14\" >South Korea</th>\n",
              "      <td id=\"T_8d015_row14_col0\" class=\"data row14 col0\" >112</td>\n",
              "      <td id=\"T_8d015_row14_col1\" class=\"data row14 col1\" >79</td>\n",
              "      <td id=\"T_8d015_row14_col2\" class=\"data row14 col2\" >277</td>\n",
              "      <td id=\"T_8d015_row14_col3\" class=\"data row14 col3\" >468</td>\n",
              "      <td id=\"T_8d015_row14_col4\" class=\"data row14 col4\" >0.591880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row15\" class=\"row_heading level0 row15\" >Mexico</th>\n",
              "      <td id=\"T_8d015_row15_col0\" class=\"data row15 col0\" >114</td>\n",
              "      <td id=\"T_8d015_row15_col1\" class=\"data row15 col1\" >98</td>\n",
              "      <td id=\"T_8d015_row15_col2\" class=\"data row15 col2\" >303</td>\n",
              "      <td id=\"T_8d015_row15_col3\" class=\"data row15 col3\" >515</td>\n",
              "      <td id=\"T_8d015_row15_col4\" class=\"data row15 col4\" >0.588350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row16\" class=\"row_heading level0 row16\" >Senegal</th>\n",
              "      <td id=\"T_8d015_row16_col0\" class=\"data row16 col0\" >55</td>\n",
              "      <td id=\"T_8d015_row16_col1\" class=\"data row16 col1\" >36</td>\n",
              "      <td id=\"T_8d015_row16_col2\" class=\"data row16 col2\" >130</td>\n",
              "      <td id=\"T_8d015_row16_col3\" class=\"data row16 col3\" >221</td>\n",
              "      <td id=\"T_8d015_row16_col4\" class=\"data row16 col4\" >0.588235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row17\" class=\"row_heading level0 row17\" >Costa Rica</th>\n",
              "      <td id=\"T_8d015_row17_col0\" class=\"data row17 col0\" >77</td>\n",
              "      <td id=\"T_8d015_row17_col1\" class=\"data row17 col1\" >50</td>\n",
              "      <td id=\"T_8d015_row17_col2\" class=\"data row17 col2\" >179</td>\n",
              "      <td id=\"T_8d015_row17_col3\" class=\"data row17 col3\" >306</td>\n",
              "      <td id=\"T_8d015_row17_col4\" class=\"data row17 col4\" >0.584967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row18\" class=\"row_heading level0 row18\" >Iraq</th>\n",
              "      <td id=\"T_8d015_row18_col0\" class=\"data row18 col0\" >71</td>\n",
              "      <td id=\"T_8d015_row18_col1\" class=\"data row18 col1\" >44</td>\n",
              "      <td id=\"T_8d015_row18_col2\" class=\"data row18 col2\" >160</td>\n",
              "      <td id=\"T_8d015_row18_col3\" class=\"data row18 col3\" >275</td>\n",
              "      <td id=\"T_8d015_row18_col4\" class=\"data row18 col4\" >0.581818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8d015_level0_row19\" class=\"row_heading level0 row19\" >Sweden</th>\n",
              "      <td id=\"T_8d015_row19_col0\" class=\"data row19 col0\" >105</td>\n",
              "      <td id=\"T_8d015_row19_col1\" class=\"data row19 col1\" >101</td>\n",
              "      <td id=\"T_8d015_row19_col2\" class=\"data row19 col2\" >286</td>\n",
              "      <td id=\"T_8d015_row19_col3\" class=\"data row19 col3\" >492</td>\n",
              "      <td id=\"T_8d015_row19_col4\" class=\"data row19 col4\" >0.581301</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "teams_win_statues=pd.crosstab(df[\"home_team\"], df[\"Win_Statues\"],margins=True, margins_name=\"Total\")\n",
        "teams_win_statues[\"team_win_probability\"]=teams_win_statues[\"Win\"]/(teams_win_statues[\"Total\"])\n",
        "\n",
        "teams_win_statues_100=teams_win_statues.loc[teams_win_statues[\"Total\"]>200]\n",
        "teams_win_statues_100=teams_win_statues_100.sort_values(\"team_win_probability\",ascending=False)\n",
        "teams_win_statues_100.head(20).style.bar(color=\"orange\",subset=\"team_win_probability\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see teams has more than 50% wining probability while playing in the home grounds "
      ],
      "metadata": {
        "id": "nx6erfixolBZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Teams playing in away conditions**"
      ],
      "metadata": {
        "id": "YUGlkDH9orkL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets take teams which plays atleast 200 games"
      ],
      "metadata": {
        "id": "ZsZ5ICYXo4Pn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "wdPUnKyEbI1f",
        "outputId": "2a2ccdfa-d656-43b0-a3a2-f32e01e82ea5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Win_Statues  Draw  Win  Win  Total  team_win_probability\n",
              "away_team                                               \n",
              "Brazil         91  223  101    415              0.537349\n",
              "Germany        89  240  119    448              0.535714\n",
              "England       134  266  115    515              0.516505\n",
              "Spain          97  162   82    341              0.475073\n",
              "South Korea   110  177  109    396              0.446970\n",
              "Netherlands    81  163  129    373              0.436997\n",
              "Russia        115  175  111    401              0.436409\n",
              "Iran           67   96   57    220              0.436364\n",
              "Japan          53  111   93    257              0.431907\n",
              "Italy         108  150  105    363              0.413223\n",
              "Australia      56   92   76    224              0.410714\n",
              "Sweden        120  220  198    538              0.408922\n",
              "Yugoslavia     61  115  115    291              0.395189\n",
              "France         80  142  139    361              0.393352\n",
              "Zambia        103  165  153    421              0.391924\n",
              "Mexico         87  140  133    360              0.388889\n",
              "Portugal       63  114  117    294              0.387755\n",
              "Scotland       87  157  161    405              0.387654\n",
              "Argentina     124  167  143    434              0.384793\n",
              "Hungary       105  183  195    483              0.378882"
            ],
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              "      <th>Win_Statues</th>\n",
              "      <th>Draw</th>\n",
              "      <th>Win</th>\n",
              "      <th>Win</th>\n",
              "      <th>Total</th>\n",
              "      <th>team_win_probability</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>away_team</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>91</td>\n",
              "      <td>223</td>\n",
              "      <td>101</td>\n",
              "      <td>415</td>\n",
              "      <td>0.537349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>89</td>\n",
              "      <td>240</td>\n",
              "      <td>119</td>\n",
              "      <td>448</td>\n",
              "      <td>0.535714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>England</th>\n",
              "      <td>134</td>\n",
              "      <td>266</td>\n",
              "      <td>115</td>\n",
              "      <td>515</td>\n",
              "      <td>0.516505</td>\n",
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              "    <tr>\n",
              "      <th>Spain</th>\n",
              "      <td>97</td>\n",
              "      <td>162</td>\n",
              "      <td>82</td>\n",
              "      <td>341</td>\n",
              "      <td>0.475073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Korea</th>\n",
              "      <td>110</td>\n",
              "      <td>177</td>\n",
              "      <td>109</td>\n",
              "      <td>396</td>\n",
              "      <td>0.446970</td>\n",
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              "      <th>Netherlands</th>\n",
              "      <td>81</td>\n",
              "      <td>163</td>\n",
              "      <td>129</td>\n",
              "      <td>373</td>\n",
              "      <td>0.436997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russia</th>\n",
              "      <td>115</td>\n",
              "      <td>175</td>\n",
              "      <td>111</td>\n",
              "      <td>401</td>\n",
              "      <td>0.436409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iran</th>\n",
              "      <td>67</td>\n",
              "      <td>96</td>\n",
              "      <td>57</td>\n",
              "      <td>220</td>\n",
              "      <td>0.436364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>53</td>\n",
              "      <td>111</td>\n",
              "      <td>93</td>\n",
              "      <td>257</td>\n",
              "      <td>0.431907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>108</td>\n",
              "      <td>150</td>\n",
              "      <td>105</td>\n",
              "      <td>363</td>\n",
              "      <td>0.413223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>56</td>\n",
              "      <td>92</td>\n",
              "      <td>76</td>\n",
              "      <td>224</td>\n",
              "      <td>0.410714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweden</th>\n",
              "      <td>120</td>\n",
              "      <td>220</td>\n",
              "      <td>198</td>\n",
              "      <td>538</td>\n",
              "      <td>0.408922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yugoslavia</th>\n",
              "      <td>61</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>291</td>\n",
              "      <td>0.395189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>80</td>\n",
              "      <td>142</td>\n",
              "      <td>139</td>\n",
              "      <td>361</td>\n",
              "      <td>0.393352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zambia</th>\n",
              "      <td>103</td>\n",
              "      <td>165</td>\n",
              "      <td>153</td>\n",
              "      <td>421</td>\n",
              "      <td>0.391924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mexico</th>\n",
              "      <td>87</td>\n",
              "      <td>140</td>\n",
              "      <td>133</td>\n",
              "      <td>360</td>\n",
              "      <td>0.388889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portugal</th>\n",
              "      <td>63</td>\n",
              "      <td>114</td>\n",
              "      <td>117</td>\n",
              "      <td>294</td>\n",
              "      <td>0.387755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scotland</th>\n",
              "      <td>87</td>\n",
              "      <td>157</td>\n",
              "      <td>161</td>\n",
              "      <td>405</td>\n",
              "      <td>0.387654</td>\n",
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              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>124</td>\n",
              "      <td>167</td>\n",
              "      <td>143</td>\n",
              "      <td>434</td>\n",
              "      <td>0.384793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>105</td>\n",
              "      <td>183</td>\n",
              "      <td>195</td>\n",
              "      <td>483</td>\n",
              "      <td>0.378882</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "teams_away_statues=pd.crosstab(df[\"away_team\"], df[\"Win_Statues\"],margins=True, margins_name=\"Total\")\n",
        "teams_away_statues[\"team_win_probability\"]=teams_away_statues[\"Lost\"]/(teams_away_statues[\"Total\"])\n",
        "\n",
        "teams_away_statues_100=teams_away_statues.loc[teams_away_statues[\"Total\"]>200]\n",
        "teams_away_statues_100=teams_away_statues_100.sort_values(\"team_win_probability\",ascending=False)\n",
        "teams_away_statues_100.rename(columns={'Lost': 'Win'}, index={'Win': 'Lost'}, inplace=True)\n",
        "teams_away_statues_100.head(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*The probability to wining a tournament in away conditions is very low comapred to the winning probalities in home conditions*"
      ],
      "metadata": {
        "id": "61zEQDCeo_3q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Making a plot to identify wining possibilities in both home and away conditions**"
      ],
      "metadata": {
        "id": "mqrkaeLXrw9w"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "CSn67PwIbI1f",
        "outputId": "6d4b8bc6-e816-4ed4-9a67-b7ed99f3a999"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "win_prob={teams_win_statues_100.iloc[i,:].name:[teams_win_statues_100.iloc[i,4]] for i in range(teams_win_statues_100.shape[0])}\n",
        "for i in range(teams_away_statues_100.shape[0]):\n",
        "    try:\n",
        "        win_prob[teams_away_statues_100.iloc[i,:].name].append(teams_away_statues_100.iloc[i,4]) \n",
        "    except:\n",
        "        pass\n",
        "country_names=win_prob.keys()\n",
        "lst_home=[i[0] for i in win_prob.values()]\n",
        "\n",
        "lst_away=[]\n",
        "for i in win_prob.values():\n",
        "    if len(i)==2:\n",
        "        lst_away.append(i[1])\n",
        "    else:\n",
        "        lst_away.append(0)\n",
        "\n",
        "plt.figure(figsize=(16,6))\n",
        "plt.plot(country_names,lst_home, label = \"Home Win Probability\")\n",
        "plt.plot(country_names, lst_away, label = \"Away Win Probability\")\n",
        "plt.xticks(rotation = 90)\n",
        "plt.title(\"Winning Probability of each team (Home and Away)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TetvEmvgbI1g"
      },
      "source": [
        "Blue line is alway above the yellow line,showing home winning possibilies are always higher.\n",
        "\n",
        "**Finally by analysing the data, we can identify that the home team has a clear edge while playing a tournament in their home grounds**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuQpfrcAbI1g"
      },
      "source": [
        "### Finding Most Succesful Team\n",
        "\n",
        "**Finding the team which performs best in home conditions each year interval**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Home Conditions**\n",
        "\n",
        "> Convert the data set into 10 terms and findsout who has done best at each term"
      ],
      "metadata": {
        "id": "G3C8uHHPubCL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4F_oYs7bI1g",
        "outputId": "79068f70-ce4e-4468-f8dc-f5e417c7f743"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No Enough data to find the best team in the term: 1872-1886 \n",
            "\n",
            "Best 2 team in the term: 1886-1900 \n",
            "| home_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| England     |      1 |      3 |    18 |      22 |               0.818182 |\n",
            "| Scotland    |      3 |      3 |    16 |      22 |               0.727273 |\n",
            "\n",
            "Best 2 team in the term: 1900-1914 \n",
            "| home_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| England     |      6 |      1 |    14 |      21 |               0.666667 |\n",
            "| Hungary     |      6 |      3 |    16 |      25 |               0.64     |\n",
            "\n",
            "Best 2 team in the term: 1914-1928 \n",
            "| home_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Denmark     |      4 |      3 |    19 |      26 |               0.730769 |\n",
            "| Argentina   |     10 |      9 |    39 |      58 |               0.672414 |\n",
            "\n",
            "Best 2 team in the term: 1928-1942 \n",
            "| home_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Argentina   |      3 |      4 |    34 |      41 |               0.829268 |\n",
            "| England     |      1 |      4 |    20 |      25 |               0.8      |\n",
            "\n",
            "Best 2 team in the term: 1942-1956 \n",
            "| home_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Costa Rica  |      0 |      2 |    20 |      22 |               0.909091 |\n",
            "| Hungary     |      5 |      2 |    34 |      41 |               0.829268 |\n",
            "\n",
            "Best 2 team in the term: 1956-1970 \n",
            "| home_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| China PR    |      2 |      3 |    18 |      23 |               0.782609 |\n",
            "| Ghana       |     10 |      0 |    30 |      40 |               0.75     |\n",
            "\n",
            "Best 2 team in the term: 1970-1984 \n",
            "| home_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Russia      |      7 |      7 |    39 |      53 |               0.735849 |\n",
            "| Brazil      |     22 |      4 |    68 |      94 |               0.723404 |\n",
            "\n",
            "Best 2 team in the term: 1984-1998 \n",
            "| home_team      |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:---------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Czech Republic |      2 |      3 |    20 |      25 |               0.8      |\n",
            "| Nigeria        |      9 |      3 |    37 |      49 |               0.755102 |\n",
            "\n",
            "Best 2 team in the term: 1998-2012 \n",
            "| home_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Spain       |     10 |     10 |    77 |      97 |               0.793814 |\n",
            "| Jersey      |      4 |      2 |    19 |      25 |               0.76     |\n",
            "\n",
            "Best 2 team in the term: 2012-2021 \n",
            "| home_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Egypt       |      6 |      8 |    47 |      61 |               0.770492 |\n",
            "| Spain       |      8 |      5 |    43 |      56 |               0.767857 |\n"
          ]
        }
      ],
      "source": [
        "range_years= max(years)-min(years)\n",
        "no_0f_terms= 10\n",
        "term_size= int(range_years/no_0f_terms)\n",
        "for i in range(no_0f_terms+1):\n",
        "    start= years.index(term_size*i+min(years))\n",
        "    end= years.index(min(term_size*(i+1)+min(years),2021))\n",
        "    term= df.iloc[start:end]\n",
        "    best_teams= pd.crosstab(term[\"home_team\"], term[\"Win_Statues\"],margins=True, margins_name=\"Total\")\n",
        "    \n",
        "    ##Lets take teams which plays atleast 20 games\n",
        "    best_teams[\"team_win_probability\"]= best_teams[\"Win\"]/(best_teams[\"Total\"])\n",
        "    best_teams=best_teams.sort_values(\"team_win_probability\",ascending=False)\n",
        "    best_teams=best_teams.loc[best_teams[\"Total\"]>20]\n",
        "    if (best_teams.shape[0]> 2):\n",
        "        print(f\"\\nBest 2 team in the term: {term_size*i+min(years)}-{min(term_size*(i+1)+min(years),2021)} \")\n",
        "        print(best_teams.iloc[0:2].to_markdown())\n",
        "    else:\n",
        "        print(f\"No Enough data to find the best team in the term: {term_size*i+min(years)}-{min(term_size*(i+1)+min(years),2021)} \")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvEgZYIebI1g"
      },
      "source": [
        "**Finding the team which performs best in away conditions each year interval**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Away Conditions**\n",
        ">Convert the data set into 10 terms and findsout who has done best at each term "
      ],
      "metadata": {
        "id": "OqEtKAHKvJ1X"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9UhC8DPbI1h",
        "outputId": "cdd21d53-3252-4760-f627-249193ee6a5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Best 2 team in the term: 1872-1886 \n",
            "No Enough data to find the best team in the term: 1872-1886 \n",
            "\n",
            "Best 2 team in the term: 1886-1900 \n",
            "| away_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Scotland    |      4 |     13 |     4 |      21 |               0.619048 |\n",
            "| Total       |     11 |     30 |    44 |      85 |               0.352941 |\n",
            "\n",
            "Best 2 team in the term: 1900-1914 \n",
            "| away_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| England     |      6 |     18 |     4 |      28 |               0.642857 |\n",
            "| Total       |     44 |     91 |   123 |     258 |               0.352713 |\n",
            "\n",
            "Best 2 team in the term: 1914-1928 \n",
            "| away_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| England     |      8 |     13 |     5 |      26 |               0.5      |\n",
            "| Uruguay     |      8 |     20 |    17 |      45 |               0.444444 |\n",
            "\n",
            "Best 2 team in the term: 1928-1942 \n",
            "| away_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Germany     |      6 |     32 |    20 |      58 |               0.551724 |\n",
            "| Scotland    |      5 |     16 |     8 |      29 |               0.551724 |\n",
            "\n",
            "Best 2 team in the term: 1942-1956 \n",
            "| away_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Hungary     |      7 |     31 |     9 |      47 |               0.659574 |\n",
            "| Yugoslavia  |      7 |     24 |     9 |      40 |               0.6      |\n",
            "\n",
            "Best 2 team in the term: 1956-1970 \n",
            "| away_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Zambia      |      3 |     12 |     6 |      21 |               0.571429 |\n",
            "| Brazil      |     15 |     46 |    20 |      81 |               0.567901 |\n",
            "\n",
            "Best 2 team in the term: 1970-1984 \n",
            "| away_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Tahiti      |      3 |     21 |     5 |      29 |               0.724138 |\n",
            "| Germany     |     15 |     40 |    14 |      69 |               0.57971  |\n",
            "\n",
            "Best 2 team in the term: 1984-1998 \n",
            "| away_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Brazil      |     18 |     41 |    17 |      76 |               0.539474 |\n",
            "| Germany     |     22 |     44 |    17 |      83 |               0.53012  |\n",
            "\n",
            "Best 2 team in the term: 1998-2012 \n",
            "| away_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Spain       |     20 |     50 |    12 |      82 |               0.609756 |\n",
            "| France      |     22 |     46 |    12 |      80 |               0.575    |\n",
            "\n",
            "Best 2 team in the term: 2012-2021 \n",
            "| away_team   |   Draw |   Lost |   Win |   Total |   team_win_probability |\n",
            "|:------------|-------:|-------:|------:|--------:|-----------------------:|\n",
            "| Brazil      |      7 |     33 |     6 |      46 |               0.717391 |\n",
            "| Belgium     |      4 |     31 |     9 |      44 |               0.704545 |\n"
          ]
        }
      ],
      "source": [
        "range_years=max(years)-min(years)\n",
        "no_0f_terms=10\n",
        "term_size=int(range_years/no_0f_terms)\n",
        "for i in range(no_0f_terms+1):\n",
        "    start=years.index(term_size*i+min(years))\n",
        "    end=years.index(min(term_size*(i+1)+min(years),2021))\n",
        "    term=df.iloc[start:end]\n",
        "    best_teams=pd.crosstab(term[\"away_team\"], term[\"Win_Statues\"],margins=True, margins_name=\"Total\")\n",
        "    \n",
        "    ##Lets take teams which plays atleast 200 games\n",
        "    best_teams[\"team_win_probability\"]=best_teams[\"Lost\"]/(best_teams[\"Total\"])\n",
        "    best_teams=best_teams.sort_values(\"team_win_probability\",ascending=False)\n",
        "    best_teams=best_teams.loc[best_teams[\"Total\"]>20]\n",
        "    print(f\"\\nBest 2 team in the term: {term_size*i+min(years)}-{min(term_size*(i+1)+min(years),2021)} \")\n",
        "    if (best_teams.shape[0]>2):\n",
        "        print(best_teams.iloc[0:2].to_markdown())\n",
        "    else:\n",
        "        print(f\"No Enough data to find the best team in the term: {term_size*i+min(years)}-{min(term_size*(i+1)+min(years),2021)} \")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5jDUKSdEbI1h"
      },
      "source": [
        "# **FIFA World Cup 2022- QATAR**\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ML Model Creation"
      ],
      "metadata": {
        "id": "dJriRi9mTnUm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_qknaoImbI1h",
        "outputId": "d2671757-8a8e-46cc-c2b0-8d92720503f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date home_team away_team  home_score  away_score tournament     city  \\\n",
              "0  1872-11-30  Scotland   England           0           0   Friendly  Glasgow   \n",
              "1  1873-03-08   England  Scotland           4           2   Friendly   London   \n",
              "2  1874-03-07  Scotland   England           2           1   Friendly  Glasgow   \n",
              "3  1875-03-06   England  Scotland           2           2   Friendly   London   \n",
              "4  1876-03-04  Scotland   England           3           0   Friendly  Glasgow   \n",
              "\n",
              "    country  neutral Win_Statues  \n",
              "0  Scotland    False        Draw  \n",
              "1   England    False         Win  \n",
              "2  Scotland    False         Win  \n",
              "3   England    False        Draw  \n",
              "4  Scotland    False         Win  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d4f0d20b-e868-4416-af58-3507f92efc4a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>Win_Statues</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",
              "      <td>Draw</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",
              "      <td>Win</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",
              "      <td>Win</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1875-03-06</td>\n",
              "      <td>England</td>\n",
              "      <td>Scotland</td>\n",
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              "      <td>Draw</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",
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              "      <td>Friendly</td>\n",
              "      <td>Glasgow</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "df_match=df.copy() #making a copy of the main dataframe for future use\n",
        "df_match.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Making a new dataset with required features to train the machine learning model*\n",
        "\n",
        "**Year, Played Country, Team_1, Team_2, Team_1 score, Team_2 score**"
      ],
      "metadata": {
        "id": "ly8-s7J-wD1i"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "asRe1qM2bI1i",
        "outputId": "cca793a0-5bb3-4388-b880-02de191afc0d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year       Country     team_1    team_2  team_1_score  team_2_score\n",
              "0  1991        Greece    Albania    Greece             2             0\n",
              "1  2014       Hungary    Finland   Hungary             2             1\n",
              "2  1961  Soviet Union     Russia   Austria             0             1\n",
              "3  1997        Kuwait     Kuwait      Iran             0             2\n",
              "4  1985     German DR  German DR  Bulgaria             2             1"
            ],
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              "      <th>0</th>\n",
              "      <td>1991</td>\n",
              "      <td>Greece</td>\n",
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              "      <td>Greece</td>\n",
              "      <td>2</td>\n",
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              "      <th>1</th>\n",
              "      <td>2014</td>\n",
              "      <td>Hungary</td>\n",
              "      <td>Finland</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1961</td>\n",
              "      <td>Soviet Union</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1997</td>\n",
              "      <td>Kuwait</td>\n",
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              "      <td>Iran</td>\n",
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              "      <th>4</th>\n",
              "      <td>1985</td>\n",
              "      <td>German DR</td>\n",
              "      <td>German DR</td>\n",
              "      <td>Bulgaria</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "New_Dataset_part_1=pd.DataFrame(list(zip(years,df_match.values[:,7],df_match.values[:,1],df_match.values[:,2],df_match.values[:,3],df_match.values[:,4])),columns=[\"year\",\"Country\",\"team_1\",\"team_2\",\"team_1_score\",\"team_2_score\"])\n",
        "\n",
        "# Making a new dataset by changing the team_1 and team_2 and their respective scores\n",
        "New_Dataset_part_2=pd.DataFrame(list(zip(years,df_match.values[:,7],df_match.values[:,2],df_match.values[:,1],df_match.values[:,4],df_match.values[:,3])),columns=[\"year\",\"Country\",\"team_1\",\"team_2\",\"team_1_score\",\"team_2_score\"])\n",
        "New_Dataset=pd.concat([New_Dataset_part_1,New_Dataset_part_2],axis=0)\n",
        "New_Dataset =New_Dataset.sample(frac=1).reset_index(drop=True) #Shaffling the dataset\n",
        "New_Dataset.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1k2PIi43bI1i",
        "outputId": "c6520603-3e4f-4b8a-e723-6cf9c9d6389f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "343"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "# Creating a list containg all the names of the countries\n",
        "teams_1=New_Dataset.team_1.unique()\n",
        "contries=New_Dataset.Country.unique()\n",
        "all_countries=np.unique(np.concatenate((teams_1,contries), axis=0))\n",
        "len(all_countries)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "ZwE0MNx9bI1i",
        "outputId": "52430758-45f1-451b-c4b9-0ae183bb7dac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  year  team_1_score  team_2_score\n",
              "year          1.000000     -0.127807     -0.127807\n",
              "team_1_score -0.127807      1.000000     -0.160107\n",
              "team_2_score -0.127807     -0.160107      1.000000"
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c693b6c7-c33e-4923-b45d-8df4377e5e8c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c693b6c7-c33e-4923-b45d-8df4377e5e8c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Making a heatmap to see the correlation of each columns \n",
        "sns.heatmap(New_Dataset.corr())\n",
        "New_Dataset.corr()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Defining the features and labels(Targets)"
      ],
      "metadata": {
        "id": "R6K-P1FCwkoN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "X = Played Year | Host Country | Team_1 | Team_2\n",
        "\n",
        "Y = Team_1_score | Team_2_score "
      ],
      "metadata": {
        "id": "x0Kt4xjNw_9P"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "wi4WGvZIbI1j"
      },
      "outputs": [],
      "source": [
        "Y= New_Dataset.iloc[:,4:6]                      # Trainning Tragets\n",
        "categorized_data=New_Dataset.iloc[:,0:4].copy() # Trainning features\n",
        "\n",
        "from sklearn import preprocessing\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "\n",
        "# Labeling the data using LabelEncorder in Sklearn-(Giving a unique number to each string(country))\n",
        "\n",
        "label_encoder.fit(all_countries)\n",
        "#list(label_encoder.classes_)\n",
        "categorized_data['team_1']=label_encoder.transform(categorized_data['team_1'])\n",
        "categorized_data['team_2']=label_encoder.transform(categorized_data['team_2'])\n",
        "categorized_data['Country']=label_encoder.transform(categorized_data['Country'])\n",
        "\n",
        "# Converting these feature columns to categrize form to make the training processs more smoother\n",
        "categorized_data['team_1']=categorized_data['team_1'].astype(\"category\")\n",
        "categorized_data['team_2']=categorized_data['team_2'].astype(\"category\")\n",
        "categorized_data['Country']=categorized_data['team_2'].astype(\"category\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cEUj157abI1j",
        "outputId": "66e773ef-9a00-4afc-d7aa-e60dcb3ded89"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year Country team_1 team_2\n",
              "0  1991     118      2    118\n",
              "1  2014     133    101    133\n",
              "2  1961      18    245     18\n",
              "3  1997     137    156    137\n",
              "4  1985      44    111     44"
            ],
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>Country</th>\n",
              "      <th>team_1</th>\n",
              "      <th>team_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1991</td>\n",
              "      <td>118</td>\n",
              "      <td>2</td>\n",
              "      <td>118</td>\n",
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              "      <td>2014</td>\n",
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              "      <td>133</td>\n",
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              "      <th>2</th>\n",
              "      <td>1961</td>\n",
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              "      <th>3</th>\n",
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              "      <td>137</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1985</td>\n",
              "      <td>44</td>\n",
              "      <td>111</td>\n",
              "      <td>44</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5d0bad6e-9f83-4cca-b0c6-dbcb05dd3e6c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "# Input Fatures to the model (x)\n",
        "categorized_data.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7fWbSY-MbI1j",
        "outputId": "73dbab02-7873-4ba7-94d4-b9cea35d465c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   team_1_score  team_2_score\n",
              "0             2             0\n",
              "1             2             1\n",
              "2             0             1\n",
              "3             0             2\n",
              "4             2             1"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>team_1_score</th>\n",
              "      <th>team_2_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9598ec0f-8b28-463f-9dbc-62d08d7fd061 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9598ec0f-8b28-463f-9dbc-62d08d7fd061');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "# Targets to the model (Y)\n",
        "Y.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3tjpJx0bI1k",
        "outputId": "4c55bec4-9c5c-4698-ca93-6180816a9721"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 84064 entries, 0 to 84063\n",
            "Data columns (total 4 columns):\n",
            " #   Column   Non-Null Count  Dtype   \n",
            "---  ------   --------------  -----   \n",
            " 0   year     84064 non-null  int64   \n",
            " 1   Country  84064 non-null  category\n",
            " 2   team_1   84064 non-null  category\n",
            " 3   team_2   84064 non-null  category\n",
            "dtypes: category(3), int64(1)\n",
            "memory usage: 1.2 MB\n",
            "None\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 84064 entries, 0 to 84063\n",
            "Data columns (total 2 columns):\n",
            " #   Column        Non-Null Count  Dtype\n",
            "---  ------        --------------  -----\n",
            " 0   team_1_score  84064 non-null  int64\n",
            " 1   team_2_score  84064 non-null  int64\n",
            "dtypes: int64(2)\n",
            "memory usage: 1.3 MB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# Info about the X and Y dataframes\n",
        "print(categorized_data.info())\n",
        "print(Y.info())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Making the Model**"
      ],
      "metadata": {
        "id": "Nc_Vvpco0ulA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we use random forrest classifier for prediction\n",
        "And Multi Output Regressor for predict multiple targets (team_1 score and team_2 score)\n",
        "\n",
        ">Multi target Regression\n",
        "\n",
        "> \" This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.\""
      ],
      "metadata": {
        "id": "pkkrHeuV2GXY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLkV3gfrbI1k",
        "outputId": "2aba9e7c-08b1-430c-85d6-bd9dada0d416"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiOutputRegressor(estimator=RandomForestClassifier(random_state=85))"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "X= categorized_data\n",
        "from sklearn.multioutput import MultiOutputRegressor\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = MultiOutputRegressor(RandomForestClassifier(random_state=random_num))\n",
        "model.fit(X,Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08vjnhKVbI1l",
        "outputId": "7448c657-094d-47f0-a4f6-f8242a67a59f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 0],\n",
              "       [0, 1],\n",
              "       [0, 1],\n",
              "       ...,\n",
              "       [1, 0],\n",
              "       [5, 0],\n",
              "       [1, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "# Making the predictions\n",
        "prd=model.predict(X)\n",
        "prd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Accuracy Check"
      ],
      "metadata": {
        "id": "Y8QGhyiEUIKN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating the Confusion matrix for each predictions**"
      ],
      "metadata": {
        "id": "-WzU13Y83SVy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "6qeLQgYpbI1l"
      },
      "outputs": [],
      "source": [
        "score_team_1=[i[0] for i in prd]\n",
        "score_team_2=[i[1] for i in prd]\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm1=confusion_matrix(list(Y.iloc[:,0]),score_team_1)\n",
        "cm2=confusion_matrix(list(Y.iloc[:,1]),score_team_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 910
        },
        "id": "3dgJRo_sbI1l",
        "outputId": "441a0514-b8a6-462e-b358-b86cf2cf8154"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(114.0, 0.5, 'Predicted')"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Plotting the Confussion Matrix for score of team 01\n",
        "plt.figure(figsize=(15,15))\n",
        "sns.heatmap(cm1, annot=True,fmt=\"d\", cmap='YlGnBu', linecolor='black', linewidths=1)\n",
        "plt.title(\"Confusion Matrix for Team 1 Score\")\n",
        "plt.xlabel(\"Actual\")\n",
        "plt.ylabel(\"Predicted\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0qdp0BNbI1m",
        "outputId": "daf1ab3e-e98e-4c30-c9a2-542b77e751a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.89      0.88     25743\n",
            "           1       0.86      0.89      0.87     26012\n",
            "           2       0.86      0.84      0.85     16565\n",
            "           3       0.86      0.81      0.83      8078\n",
            "           4       0.86      0.80      0.83      3846\n",
            "           5       0.85      0.79      0.82      1766\n",
            "           6       0.88      0.79      0.83       946\n",
            "           7       0.87      0.82      0.84       495\n",
            "           8       0.84      0.80      0.82       264\n",
            "           9       0.84      0.86      0.85       155\n",
            "          10       0.90      0.84      0.87        83\n",
            "          11       0.86      0.93      0.89        41\n",
            "          12       0.86      0.89      0.87        35\n",
            "          13       1.00      0.81      0.89        21\n",
            "          14       1.00      0.93      0.96        14\n",
            "\n",
            "    accuracy                           0.86     84064\n",
            "   macro avg       0.88      0.85      0.86     84064\n",
            "weighted avg       0.86      0.86      0.86     84064\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Classification Report to team 1 Score\n",
        "from sklearn.metrics import classification_report\n",
        "report_1=classification_report(Y.iloc[:,0],score_team_1)\n",
        "print(report_1)\n",
        "\n",
        "#Has a good Accuracy in predicting the team 1 Score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 910
        },
        "id": "tM9qI4l8bI1m",
        "outputId": "72acce29-bb8b-48c6-b1f3-aa806a961d29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(114.0, 0.5, 'Predicted')"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Plotting the Confussion Matrix for score of team 02\n",
        "\n",
        "plt.figure(figsize=(15,15))\n",
        "sns.heatmap(cm2, annot=True,fmt=\"d\", cmap='YlGnBu', linecolor='black', linewidths=1)\n",
        "plt.title(\"Confusion Matrix for team 2 score\")\n",
        "plt.xlabel(\"Actual\")\n",
        "plt.ylabel(\"Predicted\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_10J1PabI1m",
        "outputId": "2a0d15d4-9508-4143-a063-101f976756d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.89      0.88     25743\n",
            "           1       0.86      0.88      0.87     26012\n",
            "           2       0.86      0.84      0.85     16565\n",
            "           3       0.86      0.81      0.83      8078\n",
            "           4       0.86      0.80      0.83      3846\n",
            "           5       0.86      0.79      0.82      1766\n",
            "           6       0.85      0.81      0.83       946\n",
            "           7       0.86      0.84      0.85       495\n",
            "           8       0.85      0.79      0.82       264\n",
            "           9       0.88      0.80      0.84       155\n",
            "          10       0.90      0.83      0.86        83\n",
            "          11       0.88      0.93      0.90        41\n",
            "          12       0.91      0.83      0.87        35\n",
            "          13       0.90      0.90      0.90        21\n",
            "          14       1.00      0.93      0.96        14\n",
            "\n",
            "    accuracy                           0.86     84064\n",
            "   macro avg       0.88      0.84      0.86     84064\n",
            "weighted avg       0.86      0.86      0.86     84064\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Classification Report to team 2 Score\n",
        "report_2=classification_report(Y.iloc[:,1],score_team_2)\n",
        "print(report_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "kfSaqKUnbI1m"
      },
      "outputs": [],
      "source": [
        "#Fuction to Select the winning team for the prediction array\n",
        "\n",
        "def select_winning_team(probability_array):\n",
        "    prob_lst=[round(probability_array[0][i],3) for i in range(2)]\n",
        "    if (prob_lst[0]>prob_lst[1]):\n",
        "        out=0\n",
        "    elif (prob_lst[0]<prob_lst[1]):\n",
        "        out=1\n",
        "    elif (prob_lst[0]==prob_lst[1]):\n",
        "        out=2\n",
        "    return out,prob_lst"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Sample Prediction**"
      ],
      "metadata": {
        "id": "fzlf1yDC39oj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h18qt3wvbI1m",
        "outputId": "bdd81da5-6862-40d7-fe50-844d4b56932b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Team 01 is Sri Lanka -281\n",
            "Team 02 is Brazil -39\n",
            "Played in  Qatar -237\n"
          ]
        }
      ],
      "source": [
        "mactch_played=2015\n",
        "team_1=\"Sri Lanka\"\n",
        "team_2=\"Brazil\"\n",
        "stadium=\"Qatar\"\n",
        "\n",
        "team_lst=[team_1,team_2]\n",
        "team_1_num=label_encoder.transform([team_1])[0]\n",
        "team_2_num=label_encoder.transform([team_2])[0]\n",
        "stadium_num=label_encoder.transform([stadium])[0]\n",
        "\n",
        "print(f\"Team 01 is {team_1} -{team_1_num}\")\n",
        "print(f\"Team 02 is {team_2} -{team_2_num}\")\n",
        "print(f\"Played in  {stadium} -{stadium_num}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mry5GslzbI1n",
        "outputId": "8443e7dd-4810-44a4-e6c2-2afe414be5d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sri Lanka vs Brazil \n",
            " Brazil wins 🏆⚽🎯\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Sample Prediction Output\n",
        "X_feature=np.array([[mactch_played,stadium_num,team_1_num,team_2_num]])\n",
        "res=model.predict(X_feature)\n",
        "win,_=select_winning_team(res)\n",
        "try:\n",
        "    print(f\"{team_1} vs {team_2} \\n {team_lst[win]} wins 🏆⚽🎯\\n\")\n",
        "except IndexError:\n",
        "    print(f\"{team_1} vs {team_2} \\n  Match Draw ⚽⚽⚽\\n\") "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BWzlZu7obI1n"
      },
      "source": [
        "**FIFA WORLD CUP - 2022**\n",
        "\n",
        "Every team plays with evey other team in the same group.That means one team plays 3 matches.Then the model predict who is going to win the match.\n",
        "* Winning team gets 02 Point\n",
        "* Match draw (Both teams scored same number of goals)- both teams get 1 points each\n",
        "* Team Lost gets no points\n",
        "\n",
        "According this points system final winner will be selected"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "9yf6qFf4bI1n"
      },
      "outputs": [],
      "source": [
        "Group_A= [\"Qatar\",\"Ecuador\",\"Senegal\",\"Netherlands\"]\n",
        "Group_B= [\"England\",\"Iran\",\"United States\",\"Wales\"]\n",
        "Group_C= [\"Argentina\",\"Saudi Arabia\",\"Mexico\",\"Poland\"]\n",
        "Group_D= [\"France\",\"Australia\",\"Denmark\",\"Tunisia\"]\n",
        "Group_E= [\"Spain\",\"Costa Rica\",\"Germany\",\"Japan\"]\n",
        "Group_F= [\"Belgium\",\"Canada\",\"Morocco\",\"Croatia\"]\n",
        "Group_G= [\"Brazil\",\"Serbia\",\"Switzerland\",\"Cameroon\"]\n",
        "Group_H= [\"Portugal\",\"Ghana\",\"Uruguay\",\"South Korea\"]\n",
        "Groups={\"Group A\":Group_A,\"Group B\":Group_B,\"Group C\":Group_C,\"Group D\":Group_D,\"Group E\":Group_E,\"Group F\":Group_F,\"Group G\":Group_G,\"Group H\":Group_H}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "zdY3YsMkbI1o"
      },
      "outputs": [],
      "source": [
        "#check the contry names are same in the dataset\n",
        "for group in Groups.keys():\n",
        "    for country in Groups[group]:\n",
        "        if country not in countries:\n",
        "            print(f\"{group} : {country}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Initialize basic parameters**"
      ],
      "metadata": {
        "id": "_PGY1BP86mMd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "dD0Qz6IzbI1o"
      },
      "outputs": [],
      "source": [
        "year=2022\n",
        "stadium=\"Qatar\"\n",
        "stadium_num=label_encoder.transform([stadium])[0]\n",
        "host_num=stadium_num"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Group stage Matches"
      ],
      "metadata": {
        "id": "_Uwl-Hdd6xnz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lAPsMOV8bI1o",
        "outputId": "27af6450-d78c-4eab-850f-fac1cb7e4368"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Group A Matches\n",
            "Qatar vs Ecuador \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            "Qatar vs Senegal \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            "Qatar vs Netherlands \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            "Ecuador vs Senegal \n",
            "  Results of the Match [0 0]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            "Ecuador vs Netherlands \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            "Senegal vs Netherlands \n",
            "  Results of the Match [2 2]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            "Group B Matches\n",
            "England vs Iran \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            " England vs United States \n",
            "  Results of the Match [1 0]\n",
            "   England wins 🎊🏆⚽🎖️\n",
            "\n",
            " England vs Wales \n",
            "  Results of the Match [1 0]\n",
            "   England wins 🎊🏆⚽🎖️\n",
            "\n",
            " Iran vs United States \n",
            "  Results of the Match [1 0]\n",
            "   Iran wins 🎊🏆⚽🎖️\n",
            "\n",
            " Iran vs Wales \n",
            "  Results of the Match [1 0]\n",
            "   Iran wins 🎊🏆⚽🎖️\n",
            "\n",
            " United States vs Wales \n",
            "  Results of the Match [1 0]\n",
            "   United States wins 🎊🏆⚽🎖️\n",
            "\n",
            "Group C Matches\n",
            "Argentina vs Saudi Arabia \n",
            "  Results of the Match [2 2]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            " Argentina vs Mexico \n",
            "  Results of the Match [2 0]\n",
            "   Argentina wins 🎊🏆⚽🎖️\n",
            "\n",
            " Argentina vs Poland \n",
            "  Results of the Match [2 3]\n",
            "   Poland wins 🎊🏆⚽🎖️\n",
            "\n",
            "Saudi Arabia vs Mexico \n",
            "  Results of the Match [0 0]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            " Saudi Arabia vs Poland \n",
            "  Results of the Match [1 2]\n",
            "   Poland wins 🎊🏆⚽🎖️\n",
            "\n",
            "Mexico vs Poland \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            "Group D Matches\n",
            "France vs Australia \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            "France vs Denmark \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            " France vs Tunisia \n",
            "  Results of the Match [1 0]\n",
            "   France wins 🎊🏆⚽🎖️\n",
            "\n",
            " Australia vs Denmark \n",
            "  Results of the Match [0 1]\n",
            "   Denmark wins 🎊🏆⚽🎖️\n",
            "\n",
            " Australia vs Tunisia \n",
            "  Results of the Match [1 0]\n",
            "   Australia wins 🎊🏆⚽🎖️\n",
            "\n",
            " Denmark vs Tunisia \n",
            "  Results of the Match [1 0]\n",
            "   Denmark wins 🎊🏆⚽🎖️\n",
            "\n",
            "Group E Matches\n",
            " Spain vs Costa Rica \n",
            "  Results of the Match [0 1]\n",
            "   Costa Rica wins 🎊🏆⚽🎖️\n",
            "\n",
            " Spain vs Germany \n",
            "  Results of the Match [0 1]\n",
            "   Germany wins 🎊🏆⚽🎖️\n",
            "\n",
            " Spain vs Japan \n",
            "  Results of the Match [0 1]\n",
            "   Japan wins 🎊🏆⚽🎖️\n",
            "\n",
            " Costa Rica vs Germany \n",
            "  Results of the Match [0 1]\n",
            "   Germany wins 🎊🏆⚽🎖️\n",
            "\n",
            " Costa Rica vs Japan \n",
            "  Results of the Match [0 1]\n",
            "   Japan wins 🎊🏆⚽🎖️\n",
            "\n",
            "Germany vs Japan \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            "Group F Matches\n",
            " Belgium vs Canada \n",
            "  Results of the Match [0 1]\n",
            "   Canada wins 🎊🏆⚽🎖️\n",
            "\n",
            " Belgium vs Morocco \n",
            "  Results of the Match [0 1]\n",
            "   Morocco wins 🎊🏆⚽🎖️\n",
            "\n",
            " Belgium vs Croatia \n",
            "  Results of the Match [0 1]\n",
            "   Croatia wins 🎊🏆⚽🎖️\n",
            "\n",
            "Canada vs Morocco \n",
            "  Results of the Match [0 0]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            " Canada vs Croatia \n",
            "  Results of the Match [0 1]\n",
            "   Croatia wins 🎊🏆⚽🎖️\n",
            "\n",
            " Morocco vs Croatia \n",
            "  Results of the Match [0 1]\n",
            "   Croatia wins 🎊🏆⚽🎖️\n",
            "\n",
            "Group G Matches\n",
            "Brazil vs Serbia \n",
            "  Results of the Match [0 0]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            " Brazil vs Switzerland \n",
            "  Results of the Match [1 0]\n",
            "   Brazil wins 🎊🏆⚽🎖️\n",
            "\n",
            " Brazil vs Cameroon \n",
            "  Results of the Match [0 1]\n",
            "   Cameroon wins 🎊🏆⚽🎖️\n",
            "\n",
            "Serbia vs Switzerland \n",
            "  Results of the Match [0 0]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            " Serbia vs Cameroon \n",
            "  Results of the Match [0 1]\n",
            "   Cameroon wins 🎊🏆⚽🎖️\n",
            "\n",
            " Switzerland vs Cameroon \n",
            "  Results of the Match [0 1]\n",
            "   Cameroon wins 🎊🏆⚽🎖️\n",
            "\n",
            "Group H Matches\n",
            "Portugal vs Ghana \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            "Portugal vs Uruguay \n",
            "  Results of the Match [1 1]\n",
            "   Match Draw ⚽⚽⚽\n",
            "\n",
            " Portugal vs South Korea \n",
            "  Results of the Match [2 1]\n",
            "   Portugal wins 🎊🏆⚽🎖️\n",
            "\n",
            " Ghana vs Uruguay \n",
            "  Results of the Match [1 0]\n",
            "   Ghana wins 🎊🏆⚽🎖️\n",
            "\n",
            " Ghana vs South Korea \n",
            "  Results of the Match [1 0]\n",
            "   Ghana wins 🎊🏆⚽🎖️\n",
            "\n",
            " Uruguay vs South Korea \n",
            "  Results of the Match [1 0]\n",
            "   Uruguay wins 🎊🏆⚽🎖️\n",
            "\n"
          ]
        }
      ],
      "source": [
        "Group_standings={}\n",
        "for grp_name in list(Groups.keys()):\n",
        "    print(f\"{grp_name} Matches\")\n",
        "    probable_countries=Groups[grp_name]\n",
        "    team_wins_dct={}\n",
        "    goal_scored_dct={}\n",
        "    goal_against_dct={}\n",
        "    win_dct={}\n",
        "    draw_dct={}\n",
        "    lost_dct={}\n",
        "    for i in range(len(probable_countries)):\n",
        "        j=i+1\n",
        "        team_1=probable_countries[i]\n",
        "        team_1_num=label_encoder.transform([team_1])[0]\n",
        "        team_wins=0\n",
        "        while j<len((probable_countries)):\n",
        "            team_2=probable_countries[j]\n",
        "            team_2_num=label_encoder.transform([team_2])[0]\n",
        "            team_lst=[team_1,team_2]\n",
        "            Input_vector=np.array([[year,stadium_num,team_1_num,team_2_num]])\n",
        "            res=model.predict(Input_vector)\n",
        "\n",
        "            win,prob_lst=select_winning_team(res)\n",
        "            goal_scored_dct[team_1] = goal_scored_dct.get(team_1,0)+prob_lst[0]\n",
        "            goal_scored_dct[team_2] = goal_scored_dct.get(team_2,0)+prob_lst[1]\n",
        "\n",
        "            goal_against_dct[team_1] = goal_against_dct.get(team_1,0)+prob_lst[1]\n",
        "            goal_against_dct[team_2] = goal_against_dct.get(team_2,0)+prob_lst[0]\n",
        "\n",
        "            try:\n",
        "                print(f\" {team_1} vs {team_2} \\n  Results of the Match {res[0]}\\n   {team_lst[win]} wins 🎊🏆⚽🎖️\\n\")\n",
        "                if (win)==0:\n",
        "                    team_wins_dct[team_1] = team_wins_dct.get(team_1,0)+2\n",
        "                    team_wins_dct[team_2] = team_wins_dct.get(team_2,0)\n",
        "                    \n",
        "                    win_dct[team_1] = win_dct.get(team_1,0)+1\n",
        "                    win_dct[team_2] = win_dct.get(team_2,0)\n",
        "                    lost_dct[team_2] = lost_dct.get(team_2,0)+1\n",
        "                    lost_dct[team_1] = lost_dct.get(team_1,0)\n",
        "                    draw_dct[team_2] = draw_dct.get(team_2,0)\n",
        "                    draw_dct[team_1] = draw_dct.get(team_1,0)\n",
        "\n",
        "                elif (win)==1:\n",
        "                    team_wins_dct[team_2] = team_wins_dct.get(team_2,0)+2\n",
        "                    team_wins_dct[team_1] = team_wins_dct.get(team_1,0)\n",
        "                    \n",
        "                    win_dct[team_2] = win_dct.get(team_2,0)+1\n",
        "                    win_dct[team_1] = win_dct.get(team_1,0)\n",
        "                    lost_dct[team_1] = lost_dct.get(team_1,0)+1\n",
        "                    lost_dct[team_2] = lost_dct.get(team_2,0)\n",
        "                    draw_dct[team_1] = draw_dct.get(team_1,0)\n",
        "                    draw_dct[team_2] = draw_dct.get(team_2,0)\n",
        "\n",
        "            except IndexError:\n",
        "                print(f\"{team_1} vs {team_2} \\n  Results of the Match {res[0]}\\n   Match Draw ⚽⚽⚽\\n\") \n",
        "                team_wins_dct[team_1] = team_wins_dct.get(team_1,0)+1\n",
        "                team_wins_dct[team_2] = team_wins_dct.get(team_2,0)+1\n",
        "                \n",
        "                draw_dct[team_1] = draw_dct.get(team_1,0)+1\n",
        "                draw_dct[team_2] = draw_dct.get(team_2,0)+1\n",
        "                \n",
        "                win_dct[team_1] = win_dct.get(team_1,0)\n",
        "                lost_dct[team_1] = lost_dct.get(team_1,0)\n",
        "                \n",
        "                win_dct[team_2] = win_dct.get(team_2,0)\n",
        "                lost_dct[team_2] = lost_dct.get(team_2,0)\n",
        "                    \n",
        "            j=j+1\n",
        "    group_results=[win_dct,draw_dct,lost_dct,team_wins_dct,goal_scored_dct,goal_against_dct]\n",
        "    Group_standings[grp_name]=group_results"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Display group stage results"
      ],
      "metadata": {
        "id": "oeQVw9oW7ILT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQM7Zs8ObI1o",
        "outputId": "a7368e0b-182b-4761-9437-331ab07c84e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Group A Final Rankings\n",
            "|    | Team        |   Wins |   Draw |   Lost |   Goal Scored |   Goal Against |   Goal Differance |   Points |\n",
            "|---:|:------------|-------:|-------:|-------:|--------------:|---------------:|------------------:|---------:|\n",
            "|  1 | Ecuador     |      0 |      3 |      0 |             2 |              2 |                 0 |        3 |\n",
            "|  2 | Netherlands |      0 |      3 |      0 |             4 |              4 |                 0 |        3 |\n",
            "|  3 | Qatar       |      0 |      3 |      0 |             3 |              3 |                 0 |        3 |\n",
            "|  4 | Senegal     |      0 |      3 |      0 |             3 |              3 |                 0 |        3 |\n",
            "\n",
            "\n",
            "Group B Final Rankings\n",
            "|    | Team          |   Wins |   Draw |   Lost |   Goal Scored |   Goal Against |   Goal Differance |   Points |\n",
            "|---:|:--------------|-------:|-------:|-------:|--------------:|---------------:|------------------:|---------:|\n",
            "|  1 | England       |      2 |      1 |      0 |             3 |              1 |                 2 |        5 |\n",
            "|  2 | Iran          |      2 |      1 |      0 |             3 |              1 |                 2 |        5 |\n",
            "|  3 | United States |      1 |      0 |      2 |             1 |              2 |                -1 |        2 |\n",
            "|  4 | Wales         |      0 |      0 |      3 |             0 |              3 |                -3 |        0 |\n",
            "\n",
            "\n",
            "Group C Final Rankings\n",
            "|    | Team         |   Wins |   Draw |   Lost |   Goal Scored |   Goal Against |   Goal Differance |   Points |\n",
            "|---:|:-------------|-------:|-------:|-------:|--------------:|---------------:|------------------:|---------:|\n",
            "|  1 | Poland       |      2 |      1 |      0 |             6 |              4 |                 2 |        5 |\n",
            "|  2 | Argentina    |      1 |      1 |      1 |             6 |              5 |                 1 |        3 |\n",
            "|  3 | Mexico       |      0 |      2 |      1 |             1 |              3 |                -2 |        2 |\n",
            "|  4 | Saudi Arabia |      0 |      2 |      1 |             3 |              4 |                -1 |        2 |\n",
            "\n",
            "\n",
            "Group D Final Rankings\n",
            "|    | Team      |   Wins |   Draw |   Lost |   Goal Scored |   Goal Against |   Goal Differance |   Points |\n",
            "|---:|:----------|-------:|-------:|-------:|--------------:|---------------:|------------------:|---------:|\n",
            "|  1 | Denmark   |      2 |      1 |      0 |             3 |              1 |                 2 |        5 |\n",
            "|  2 | France    |      1 |      2 |      0 |             3 |              2 |                 1 |        4 |\n",
            "|  3 | Australia |      1 |      1 |      1 |             2 |              2 |                 0 |        3 |\n",
            "|  4 | Tunisia   |      0 |      0 |      3 |             0 |              3 |                -3 |        0 |\n",
            "\n",
            "\n",
            "Group E Final Rankings\n",
            "|    | Team       |   Wins |   Draw |   Lost |   Goal Scored |   Goal Against |   Goal Differance |   Points |\n",
            "|---:|:-----------|-------:|-------:|-------:|--------------:|---------------:|------------------:|---------:|\n",
            "|  1 | Germany    |      2 |      1 |      0 |             3 |              1 |                 2 |        5 |\n",
            "|  2 | Japan      |      2 |      1 |      0 |             3 |              1 |                 2 |        5 |\n",
            "|  3 | Costa Rica |      1 |      0 |      2 |             1 |              2 |                -1 |        2 |\n",
            "|  4 | Spain      |      0 |      0 |      3 |             0 |              3 |                -3 |        0 |\n",
            "\n",
            "\n",
            "Group F Final Rankings\n",
            "|    | Team    |   Wins |   Draw |   Lost |   Goal Scored |   Goal Against |   Goal Differance |   Points |\n",
            "|---:|:--------|-------:|-------:|-------:|--------------:|---------------:|------------------:|---------:|\n",
            "|  1 | Croatia |      3 |      0 |      0 |             3 |              0 |                 3 |        6 |\n",
            "|  2 | Canada  |      1 |      1 |      1 |             1 |              1 |                 0 |        3 |\n",
            "|  3 | Morocco |      1 |      1 |      1 |             1 |              1 |                 0 |        3 |\n",
            "|  4 | Belgium |      0 |      0 |      3 |             0 |              3 |                -3 |        0 |\n",
            "\n",
            "\n",
            "Group G Final Rankings\n",
            "|    | Team        |   Wins |   Draw |   Lost |   Goal Scored |   Goal Against |   Goal Differance |   Points |\n",
            "|---:|:------------|-------:|-------:|-------:|--------------:|---------------:|------------------:|---------:|\n",
            "|  1 | Cameroon    |      3 |      0 |      0 |             3 |              0 |                 3 |        6 |\n",
            "|  2 | Brazil      |      1 |      1 |      1 |             1 |              1 |                 0 |        3 |\n",
            "|  3 | Serbia      |      0 |      2 |      1 |             0 |              1 |                -1 |        2 |\n",
            "|  4 | Switzerland |      0 |      1 |      2 |             0 |              2 |                -2 |        1 |\n",
            "\n",
            "\n",
            "Group H Final Rankings\n",
            "|    | Team        |   Wins |   Draw |   Lost |   Goal Scored |   Goal Against |   Goal Differance |   Points |\n",
            "|---:|:------------|-------:|-------:|-------:|--------------:|---------------:|------------------:|---------:|\n",
            "|  1 | Ghana       |      2 |      1 |      0 |             3 |              1 |                 2 |        5 |\n",
            "|  2 | Portugal    |      1 |      2 |      0 |             4 |              3 |                 1 |        4 |\n",
            "|  3 | Uruguay     |      1 |      1 |      1 |             2 |              2 |                 0 |        3 |\n",
            "|  4 | South Korea |      0 |      0 |      3 |             1 |              4 |                -3 |        0 |\n"
          ]
        }
      ],
      "source": [
        "for grp_name in list(Group_standings.keys()):\n",
        "\n",
        "    team_wins_dct= dict(sorted(Group_standings[grp_name][3].items()))\n",
        "    goal_scored_dct=dict(sorted(Group_standings[grp_name][4].items()))\n",
        "    goal_against_dct=dict(sorted(Group_standings[grp_name][5].items()))\n",
        "    \n",
        "    win_dct=dict(sorted(Group_standings[grp_name][0].items()))\n",
        "    draw_dct=dict(sorted(Group_standings[grp_name][1].items()))\n",
        "    lost_dct=dict(sorted(Group_standings[grp_name][2].items()))\n",
        "    \n",
        "    lst_teams=list(team_wins_dct.keys())\n",
        "    \n",
        "    win_lst=list(win_dct.values())\n",
        "    draw_lst=list(draw_dct.values())\n",
        "    lost_lst=list(lost_dct.values())\n",
        "    \n",
        "    lst_win_count=list(team_wins_dct.values())\n",
        "    goal_scored=list(goal_scored_dct.values())\n",
        "    goal_against=list(goal_against_dct.values())\n",
        "    goal_differance=[goal_scored[i]-goal_against[i] for i in range (len(goal_scored))]\n",
        "    ranking_table=pd.DataFrame(list(zip(lst_teams,win_lst,draw_lst,lost_lst,goal_scored,goal_against,goal_differance,lst_win_count)),columns=[\"Team\",\"Wins\",\"Draw\",\"Lost\",\"Goal Scored\",\"Goal Against\",\"Goal Differance\",\"Points\"])\n",
        "    ranking_table=ranking_table.sort_values(\"Points\",ascending=False).reset_index(drop=True)\n",
        "    ranking_table.index = ranking_table.index + 1\n",
        "    print(f\"\\n\\n{grp_name} Final Rankings\")\n",
        "    print(ranking_table.to_markdown())\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Round of 16**"
      ],
      "metadata": {
        "id": "aTTlVSvo7XMz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Dyba7OdbI1p",
        "outputId": "ef1b19e6-e1e3-470d-9cd0-866a8177862f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Round of 16\n",
            "\n",
            "Qatar vs Iran \n",
            "  Match Draw ⚽⚽\n",
            "   Qatar wins at Penaly Shoot-Out 🥅🎯\n",
            "    Qatar into the Quater-Finals ⏩⏩ \n",
            "\n",
            "Poland vs France \n",
            "  France wins 🏆🏆\n",
            "    France into the Quater-Finals ⏩⏩ \n",
            "\n",
            "Germany vs Canada \n",
            "  Canada wins 🏆🏆\n",
            "    Canada into the Quater-Finals ⏩⏩ \n",
            "\n",
            "Cameroon vs Portugal \n",
            "  Portugal wins 🏆🏆\n",
            "    Portugal into the Quater-Finals ⏩⏩ \n",
            "\n",
            "Ecuador vs England \n",
            "  Match Draw ⚽⚽\n",
            "   Ecuador wins at Penaly Shoot-Out 🥅🎯\n",
            "    Ecuador into the Quater-Finals ⏩⏩ \n",
            "\n",
            "Argentina vs Denmark \n",
            "  Match Draw ⚽⚽\n",
            "   Argentina wins at Penaly Shoot-Out 🥅🎯\n",
            "    Argentina into the Quater-Finals ⏩⏩ \n",
            "\n",
            "Japan vs Croatia \n",
            "  Match Draw ⚽⚽\n",
            "   Croatia wins at Penaly Shoot-Out 🥅🎯\n",
            "    Croatia into the Quater-Finals ⏩⏩ \n",
            "\n",
            "Brazil vs Ghana \n",
            "  Match Draw ⚽⚽\n",
            "   Ghana wins at Penaly Shoot-Out 🥅🎯\n",
            "    Ghana into the Quater-Finals ⏩⏩ \n",
            "\n"
          ]
        }
      ],
      "source": [
        "##Round of 16 Section_1\n",
        "qualified_teams_1=[]\n",
        "standings=list(Group_standings.keys())\n",
        "i=0\n",
        "print(f\"Round of 16\\n\")\n",
        "while i < (len(standings)):\n",
        "    A_team= sorted(Group_standings[standings[i]][3].items(), key=lambda x: x[1], reverse=True)\n",
        "    team_1=A_team[0][0]\n",
        "    B_team= sorted(Group_standings[standings[i+1]][3].items(), key=lambda x: x[1], reverse=True)\n",
        "    team_2=B_team[1][0]\n",
        "    \n",
        "    team_1_num=label_encoder.transform([team_1])[0]\n",
        "    team_2_num=label_encoder.transform([team_2])[0]\n",
        "    team_lst=[team_1,team_2]\n",
        "    \n",
        "    Input_vector=np.array([[year,host_num,team_1_num,team_2_num]])\n",
        "    res=model.predict(Input_vector)\n",
        "    win,_=select_winning_team(res)\n",
        "\n",
        "    try:\n",
        "            print(f\"{team_1} vs {team_2} \\n  {team_lst[win]} wins 🏆🏆\")\n",
        "            print(f\"    {team_lst[win]} into the Quater-Finals ⏩⏩ \\n\")\n",
        "            qualified_teams_1.append(team_lst[win])\n",
        "    except IndexError:\n",
        "            print(f\"{team_1} vs {team_2} \\n  Match Draw ⚽⚽\") \n",
        "            winning_team=random.choice(team_lst)\n",
        "            print(f\"   {winning_team} wins at Penaly Shoot-Out 🥅🎯\")\n",
        "            print(f\"    {winning_team} into the Quater-Finals ⏩⏩ \\n\")\n",
        "            qualified_teams_1.append(winning_team)\n",
        "    i=i+2\n",
        "    \n",
        "##Round of 16 Section_2\n",
        "qualified_teams_2=[]\n",
        "standings=list(Group_standings.keys())\n",
        "i=0\n",
        "while i < (len(standings)):\n",
        "    A_team= sorted(Group_standings[standings[i]][3].items(), key=lambda x: x[1], reverse=True)\n",
        "    team_1=A_team[1][0]\n",
        "    B_team= sorted(Group_standings[standings[i+1]][3].items(), key=lambda x: x[1], reverse=True)\n",
        "    team_2=B_team[0][0]\n",
        "    \n",
        "    team_1_num=label_encoder.transform([team_1])[0]\n",
        "    team_2_num=label_encoder.transform([team_2])[0]\n",
        "    team_lst=[team_1,team_2]\n",
        "    \n",
        "    Input_vector=np.array([[year,host_num,team_1_num,team_2_num]])\n",
        "    res=model.predict(Input_vector)\n",
        "    win,_=select_winning_team(res)\n",
        "\n",
        "    try:\n",
        "            print(f\"{team_1} vs {team_2} \\n  {team_lst[win]} wins 🏆🏆\")\n",
        "            print(f\"    {team_lst[win]} into the Quater-Finals ⏩⏩ \\n\")\n",
        "            qualified_teams_2.append(team_lst[win])\n",
        "            \n",
        "    except IndexError:\n",
        "            print(f\"{team_1} vs {team_2} \\n  Match Draw ⚽⚽\") \n",
        "            winning_team=random.choice(team_lst)\n",
        "            print(f\"   {winning_team} wins at Penaly Shoot-Out 🥅🎯\")\n",
        "            print(f\"    {winning_team} into the Quater-Finals ⏩⏩ \\n\")\n",
        "            qualified_teams_2.append(winning_team)\n",
        "    i=i+2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4KvMA7jbI1p",
        "outputId": "1a4ee3ee-b651-4589-f0b8-5c13c394ec22"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Teams selected to the Quater Finals - ['Qatar', 'France', 'Canada', 'Portugal', 'Ecuador', 'Argentina', 'Croatia', 'Ghana']\n"
          ]
        }
      ],
      "source": [
        "print(f\"Teams selected to the Quater Finals - {qualified_teams_1+qualified_teams_2}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Quarter Finals**"
      ],
      "metadata": {
        "id": "uj9MqDF68AEQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0gEFtwmebI1q",
        "outputId": "1f01d791-3ea2-4116-dcf0-4d2da8d31aa4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quater Final Matches\n",
            "\n",
            "Qatar vs France \n",
            "  Match Draw ⚽⚽\n",
            "   France wins at Penaly Shoot-Out 🥅🎯\n",
            "    France into the Semi-Finals ⏩⏩ \n",
            "\n",
            "Canada vs Portugal \n",
            "  Portugal wins 🏆🏆\n",
            "    Portugal into the Semi-Finals ⏩⏩ \n",
            "\n",
            "Ecuador vs Argentina \n",
            "  Match Draw ⚽⚽\n",
            "   Argentina wins at Penaly Shoot-Out 🥅🎯\n",
            "    Argentina into the Semi-Finals ⏩⏩ \n",
            "\n",
            "Croatia vs Ghana \n",
            "  Match Draw ⚽⚽\n",
            "   Croatia wins at Penaly Shoot-Out 🥅🎯\n",
            "    Croatia into the Semi-Finals ⏩⏩ \n",
            "\n"
          ]
        }
      ],
      "source": [
        "Semifinal_teams=[]\n",
        "i=0\n",
        "print(f\"Quater Final Matches\\n\")\n",
        "while i < (len(qualified_teams_1))-1:\n",
        "    team_1= qualified_teams_1[i]\n",
        "    team_2= qualified_teams_1[i+1]\n",
        "    \n",
        "    team_1_num=label_encoder.transform([team_1])[0]\n",
        "    team_2_num=label_encoder.transform([team_2])[0]\n",
        "    team_lst=[team_1,team_2]\n",
        "    \n",
        "    Input_vector=np.array([[year,host_num,team_1_num,team_2_num]])\n",
        "    res=model.predict(Input_vector)\n",
        "    win,_=select_winning_team(res)\n",
        "\n",
        "    try:\n",
        "            print(f\"{team_1} vs {team_2} \\n  {team_lst[win]} wins 🏆🏆\")\n",
        "            print(f\"    {team_lst[win]} into the Semi-Finals ⏩⏩ \\n\")\n",
        "            Semifinal_teams.append(team_lst[win])\n",
        "            \n",
        "    except IndexError:\n",
        "            print(f\"{team_1} vs {team_2} \\n  Match Draw ⚽⚽\")\n",
        "            winning_team=random.choice(team_lst)\n",
        "            print(f\"   {winning_team} wins at Penaly Shoot-Out 🥅🎯\")\n",
        "            print(f\"    {winning_team} into the Semi-Finals ⏩⏩ \\n\")\n",
        "            Semifinal_teams.append(winning_team)\n",
        "    i=i+2\n",
        "    \n",
        "i=0\n",
        "while i < (len(qualified_teams_2))-1:\n",
        "    team_1= qualified_teams_2[i]\n",
        "    team_2= qualified_teams_2[i+1]\n",
        "    team_1_num=label_encoder.transform([team_1])[0]\n",
        "    team_2_num=label_encoder.transform([team_2])[0]\n",
        "    team_lst=[team_1,team_2]\n",
        "    \n",
        "    Input_vector=np.array([[year,host_num,team_1_num,team_2_num]])\n",
        "    res=model.predict(Input_vector)\n",
        "    win,_=select_winning_team(res)\n",
        "\n",
        "    try:\n",
        "            print(f\"{team_1} vs {team_2} \\n  {team_lst[win]} wins 🏆🏆\")\n",
        "            print(f\"    {team_lst[win]} into the Semi-Finals ⏩⏩ \\n\")\n",
        "            Semifinal_teams.append(team_lst[win])\n",
        "            \n",
        "    except IndexError:\n",
        "            print(f\"{team_1} vs {team_2} \\n  Match Draw ⚽⚽\") \n",
        "            winning_team=random.choice(team_lst)\n",
        "            print(f\"   {winning_team} wins at Penaly Shoot-Out 🥅🎯\")\n",
        "            print(f\"    {winning_team} into the Semi-Finals ⏩⏩ \\n\")\n",
        "            Semifinal_teams.append(winning_team)\n",
        "    i=i+2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E7GEmaxhbI1q",
        "outputId": "8c1291cb-089e-4c36-8344-c4fbb896b56a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Teams selected to the Semi-Finals - ['France', 'Portugal', 'Argentina', 'Croatia']\n"
          ]
        }
      ],
      "source": [
        "print(f\"Teams selected to the Semi-Finals - {Semifinal_teams}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Quarter Finals**"
      ],
      "metadata": {
        "id": "hhmVYkns8N_v"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQZQpeiNbI1q",
        "outputId": "801c8067-7b0e-470d-af89-c62a34385d82"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semi Final Matches\n",
            "\n",
            "France vs Portugal \n",
            "  France wins 🏆🏆\n",
            "    France into the FiIFA-Finals ⏩⏩ \n",
            "\n",
            "Argentina vs Croatia \n",
            "  Match Draw ⚽⚽\n",
            "   Croatia wins at Penaly Shoot-Out 🥅🎯\n",
            "    Croatia into the FIFA-Finals ⏩⏩ \n",
            "\n"
          ]
        }
      ],
      "source": [
        "final_teams=[]\n",
        "third_place_match_teams=[]\n",
        "i=0\n",
        "print(f\"Semi Final Matches\\n\")\n",
        "while i < (len(Semifinal_teams))-1:\n",
        "    team_1= Semifinal_teams[i]\n",
        "    team_2= Semifinal_teams[i+1]\n",
        "    \n",
        "    team_1_num= label_encoder.transform([team_1])[0]\n",
        "    team_2_num= label_encoder.transform([team_2])[0]\n",
        "    team_lst= [team_1,team_2]\n",
        "    \n",
        "    Input_vector= np.array([[year,host_num,team_1_num,team_2_num]])\n",
        "    res= model.predict(Input_vector)\n",
        "    win,_= select_winning_team(res)\n",
        "\n",
        "    try:\n",
        "            print(f\"{team_1} vs {team_2} \\n  {team_lst[win]} wins 🏆🏆\")\n",
        "            print(f\"    {team_lst[win]} into the FiIFA-Finals ⏩⏩ \\n\")\n",
        "            final_teams.append(team_lst[win])\n",
        "            third_place_match_teams.append(team_lst[(win+1)%2])\n",
        "\n",
        "            \n",
        "    except IndexError:\n",
        "            print(f\"{team_1} vs {team_2} \\n  Match Draw ⚽⚽\") \n",
        "            winning_team=random.choice(team_lst)\n",
        "            print(f\"   {winning_team} wins at Penaly Shoot-Out 🥅🎯\")\n",
        "            print(f\"    {winning_team} into the FIFA-Finals ⏩⏩ \\n\")\n",
        "            final_teams.append(winning_team)\n",
        "            team_lst.remove(winning_team)\n",
        "            third_place_match_teams.append(team_lst[0])\n",
        "    i=i+2\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwi0KRH_bI1r",
        "outputId": "19a2344b-5536-4bc6-ec19-de07a3506526"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Teams selected to the Finals - ['France', 'Croatia']\n",
            "Teams selected to the Third Place match - ['Portugal', 'Argentina']\n"
          ]
        }
      ],
      "source": [
        "print(f\"Teams selected to the Finals - {final_teams}\")\n",
        "print(f\"Teams selected to the Third Place match - {third_place_match_teams}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Finals and Third Place match**"
      ],
      "metadata": {
        "id": "7nDb99738eQt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ed-l8evybI1r",
        "outputId": "796af575-8100-4e9d-9970-8aa3068e35e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FiIFA FINAl\n",
            "\n",
            "Croatia vs France \n",
            "  France are the Winners 🎉🏆🎉\n",
            "\n",
            "\n",
            "Third Place match\n",
            "\n",
            "Argentina vs Portugal \n",
            "  Argentina Wins the 3rd Place 🎉🏆🎉\n",
            "\n",
            "\n",
            "\n",
            "Winner is France 🥇🥇🥇\n",
            "Runner-up is Croatia 🥈🥈🥈\n",
            "3rd Place is Argentina 🥉🥉🥉\n"
          ]
        }
      ],
      "source": [
        "print(f\"FiIFA FINAl\\n\")\n",
        "team_1= final_teams[1]\n",
        "team_2= final_teams[0]\n",
        "    \n",
        "team_1_num=label_encoder.transform([team_1])[0]\n",
        "team_2_num=label_encoder.transform([team_2])[0]\n",
        "team_lst=[team_1,team_2]\n",
        "    \n",
        "Input_vector=np.array([[year,host_num,team_1_num,team_2_num]])\n",
        "res=model.predict(Input_vector)\n",
        "win,_=select_winning_team(res)\n",
        "\n",
        "try:\n",
        "    print(f\"{team_1} vs {team_2} \\n  {team_lst[win]} are the Winners 🎉🏆🎉\\n\\n\")\n",
        "    winner=team_lst[win]\n",
        "    place_2=team_lst[(win+1)%2]\n",
        "            \n",
        "except IndexError:\n",
        "    print(f\"{team_1} vs {team_2} \\n  Match Draw ⚽⚽\") \n",
        "    winning_team=random.choice(team_lst)\n",
        "    print(f\"   {winning_team} wins at Penaly Shoot-Out 🥅🎯\")\n",
        "    print(f\"    {winning_team} are the Winners 🎉🏆🎉\\n\\n\")\n",
        "    winner=winning_team\n",
        "    \n",
        "    team_lst.remove(winning_team)\n",
        "    place_2=team_lst[0]\n",
        "\n",
        "print(f\"Third Place match\\n\")\n",
        "team_1= third_place_match_teams[1]\n",
        "team_2= third_place_match_teams[0]\n",
        "    \n",
        "team_1_num =label_encoder.transform([team_1])[0]\n",
        "team_2_num =label_encoder.transform([team_2])[0]\n",
        "team_lst =[team_1,team_2]\n",
        "    \n",
        "Input_vector =np.array([[year,host_num,team_1_num,team_2_num]])\n",
        "res =model.predict(Input_vector)\n",
        "win,_=select_winning_team(res)\n",
        "\n",
        "try:\n",
        "    print(f\"{team_1} vs {team_2} \\n  {team_lst[win]} Wins the 3rd Place 🎉🏆🎉\\n\")\n",
        "    place_3=team_lst[win]\n",
        "            \n",
        "except IndexError:\n",
        "    print(f\"{team_1} vs {team_2} \\n  Match Draw ⚽⚽\") \n",
        "    winning_team=random.choice(team_lst)\n",
        "    print(f\"   {winning_team} wins at Penaly Shoot-Out 🥅🎯\")\n",
        "    print(f\"    {winning_team} Wins the 3rd Place 🎉🏆🎉\\n\")\n",
        "    place_3 =winning_team\n",
        "    \n",
        "\n",
        "    \n",
        "print(f\"\\n\\nWinner is {winner} 🥇🥇🥇\")\n",
        "print(f\"Runner-up is {place_2} 🥈🥈🥈\")\n",
        "print(f\"3rd Place is {place_3} 🥉🥉🥉\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Fi.jpg](data:image/jpeg;base64,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)"
      ],
      "metadata": {
        "id": "Nj4mnhEO8sRi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Thank You**\n"
      ],
      "metadata": {
        "id": "Hgy1783qeq-l"
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.13"
    },
    "varInspector": {
      "cols": {
        "lenName": 16,
        "lenType": 16,
        "lenVar": 40
      },
      "kernels_config": {
        "python": {
          "delete_cmd_postfix": "",
          "delete_cmd_prefix": "del ",
          "library": "var_list.py",
          "varRefreshCmd": "print(var_dic_list())"
        },
        "r": {
          "delete_cmd_postfix": ") ",
          "delete_cmd_prefix": "rm(",
          "library": "var_list.r",
          "varRefreshCmd": "cat(var_dic_list()) "
        }
      },
      "types_to_exclude": [
        "module",
        "function",
        "builtin_function_or_method",
        "instance",
        "_Feature"
      ],
      "window_display": false
    },
    "colab": {
      "provenance": []
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}