WORLD_CUP.ipynb

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        "<a href=\"https://colab.research.google.com/github/ay0788/WorldCup/blob/main/WORLD_CUP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# **WORLD CUP ANALYSIS**\n",
        "\n"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "## ***Load data***"
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      "source": [
        "import pandas as pd\n",
        "\n",
        "url = 'https://raw.githubusercontent.com/rangelak/fifa-stats/master/data/fifa-world-cup.csv'\n",
        "df = pd.read_csv(url)\n",
        "df\n"
      ],
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              "                               EDITION  YEAR      LOCATION     WINNER  TEAMS  \\\n",
              "0         2014 FIFA World Cup Brazil ™  2014        Brazil    Germany     32   \n",
              "1   2010 FIFA World Cup South Africa ™  2010  South Africa      Spain     32   \n",
              "2        2006 FIFA World Cup Germany ™  2006       Germany      Italy     32   \n",
              "3    2002 FIFA World Cup Korea/Japan ™  2002   Korea/Japan     Brazil     32   \n",
              "4         1998 FIFA World Cup France ™  1998        France     France     32   \n",
              "5            1994 FIFA World Cup USA ™  1994           USA     Brazil     24   \n",
              "6          1990 FIFA World Cup Italy ™  1990         Italy    Germany     24   \n",
              "7         1986 FIFA World Cup Mexico ™  1986        Mexico  Argentina     24   \n",
              "8          1982 FIFA World Cup Spain ™  1982         Spain      Italy     24   \n",
              "9      1978 FIFA World Cup Argentina ™  1978     Argentina  Argentina     16   \n",
              "10       1974 FIFA World Cup Germany ™  1974       Germany    Germany     16   \n",
              "11        1970 FIFA World Cup Mexico ™  1970        Mexico     Brazil     16   \n",
              "12       1966 FIFA World Cup England ™  1966       England    England     16   \n",
              "13         1962 FIFA World Cup Chile ™  1962         Chile     Brazil     16   \n",
              "14        1958 FIFA World Cup Sweden ™  1958        Sweden     Brazil     16   \n",
              "15   1954 FIFA World Cup Switzerland ™  1954   Switzerland    Germany     16   \n",
              "16        1950 FIFA World Cup Brazil ™  1950        Brazil    Uruguay     13   \n",
              "17        1938 FIFA World Cup France ™  1938        France      Italy     15   \n",
              "18         1934 FIFA World Cup Italy ™  1934         Italy      Italy     16   \n",
              "19       1930 FIFA World Cup Uruguay ™  1930       Uruguay    Uruguay     13   \n",
              "\n",
              "    MATCHES  GOALS  AVERAGE_GOALS  AVERAGE_ATTENDANCE  \n",
              "0        64    171            2.7               52918  \n",
              "1        64    145            2.3               49669  \n",
              "2        64    147            2.3               52491  \n",
              "3        64    161            2.5               42268  \n",
              "4        64    171            2.7               43517  \n",
              "5        52    141            2.7               68991  \n",
              "6        52    115            2.2               48388  \n",
              "7        52    132            2.5               46039  \n",
              "8        52    146            2.8               40571  \n",
              "9        38    102            2.7               40678  \n",
              "10       38     97            2.6               49098  \n",
              "11       32     95            3.0               50124  \n",
              "12       32     89            2.8               48847  \n",
              "13       32     89            2.8               27911  \n",
              "14       35    126            3.6               23423  \n",
              "15       26    140            5.4               29561  \n",
              "16       22     88            4.0               47511  \n",
              "17       18     84            4.7               20872  \n",
              "18       17     70            4.1               21352  \n",
              "19       18     70            3.9               32808  "
            ],
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              "      <th></th>\n",
              "      <th>EDITION</th>\n",
              "      <th>YEAR</th>\n",
              "      <th>LOCATION</th>\n",
              "      <th>WINNER</th>\n",
              "      <th>TEAMS</th>\n",
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              "      <th>0</th>\n",
              "      <td>2014 FIFA World Cup Brazil ™</td>\n",
              "      <td>2014</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Germany</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>171</td>\n",
              "      <td>2.7</td>\n",
              "      <td>52918</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010 FIFA World Cup South Africa ™</td>\n",
              "      <td>2010</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Spain</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>145</td>\n",
              "      <td>2.3</td>\n",
              "      <td>49669</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006 FIFA World Cup Germany ™</td>\n",
              "      <td>2006</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Italy</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>147</td>\n",
              "      <td>2.3</td>\n",
              "      <td>52491</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2002 FIFA World Cup Korea/Japan ™</td>\n",
              "      <td>2002</td>\n",
              "      <td>Korea/Japan</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>161</td>\n",
              "      <td>2.5</td>\n",
              "      <td>42268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1998 FIFA World Cup France ™</td>\n",
              "      <td>1998</td>\n",
              "      <td>France</td>\n",
              "      <td>France</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>171</td>\n",
              "      <td>2.7</td>\n",
              "      <td>43517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1994 FIFA World Cup USA ™</td>\n",
              "      <td>1994</td>\n",
              "      <td>USA</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>141</td>\n",
              "      <td>2.7</td>\n",
              "      <td>68991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1990 FIFA World Cup Italy ™</td>\n",
              "      <td>1990</td>\n",
              "      <td>Italy</td>\n",
              "      <td>Germany</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>115</td>\n",
              "      <td>2.2</td>\n",
              "      <td>48388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1986 FIFA World Cup Mexico ™</td>\n",
              "      <td>1986</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>132</td>\n",
              "      <td>2.5</td>\n",
              "      <td>46039</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1982 FIFA World Cup Spain ™</td>\n",
              "      <td>1982</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Italy</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>146</td>\n",
              "      <td>2.8</td>\n",
              "      <td>40571</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1978 FIFA World Cup Argentina ™</td>\n",
              "      <td>1978</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>16</td>\n",
              "      <td>38</td>\n",
              "      <td>102</td>\n",
              "      <td>2.7</td>\n",
              "      <td>40678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1974 FIFA World Cup Germany ™</td>\n",
              "      <td>1974</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Germany</td>\n",
              "      <td>16</td>\n",
              "      <td>38</td>\n",
              "      <td>97</td>\n",
              "      <td>2.6</td>\n",
              "      <td>49098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1970 FIFA World Cup Mexico ™</td>\n",
              "      <td>1970</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>16</td>\n",
              "      <td>32</td>\n",
              "      <td>95</td>\n",
              "      <td>3.0</td>\n",
              "      <td>50124</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1966 FIFA World Cup England ™</td>\n",
              "      <td>1966</td>\n",
              "      <td>England</td>\n",
              "      <td>England</td>\n",
              "      <td>16</td>\n",
              "      <td>32</td>\n",
              "      <td>89</td>\n",
              "      <td>2.8</td>\n",
              "      <td>48847</td>\n",
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              "      <th>13</th>\n",
              "      <td>1962 FIFA World Cup Chile ™</td>\n",
              "      <td>1962</td>\n",
              "      <td>Chile</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>16</td>\n",
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              "      <td>2.8</td>\n",
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              "      <th>14</th>\n",
              "      <td>1958 FIFA World Cup Sweden ™</td>\n",
              "      <td>1958</td>\n",
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              "      <td>16</td>\n",
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              "      <td>3.6</td>\n",
              "      <td>23423</td>\n",
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              "      <th>15</th>\n",
              "      <td>1954 FIFA World Cup Switzerland ™</td>\n",
              "      <td>1954</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>Germany</td>\n",
              "      <td>16</td>\n",
              "      <td>26</td>\n",
              "      <td>140</td>\n",
              "      <td>5.4</td>\n",
              "      <td>29561</td>\n",
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              "      <th>16</th>\n",
              "      <td>1950 FIFA World Cup Brazil ™</td>\n",
              "      <td>1950</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>13</td>\n",
              "      <td>22</td>\n",
              "      <td>88</td>\n",
              "      <td>4.0</td>\n",
              "      <td>47511</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1938 FIFA World Cup France ™</td>\n",
              "      <td>1938</td>\n",
              "      <td>France</td>\n",
              "      <td>Italy</td>\n",
              "      <td>15</td>\n",
              "      <td>18</td>\n",
              "      <td>84</td>\n",
              "      <td>4.7</td>\n",
              "      <td>20872</td>\n",
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              "      <th>18</th>\n",
              "      <td>1934 FIFA World Cup Italy ™</td>\n",
              "      <td>1934</td>\n",
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              "      <td>Italy</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
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              "      <td>4.1</td>\n",
              "      <td>21352</td>\n",
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              "      <th>19</th>\n",
              "      <td>1930 FIFA World Cup Uruguay ™</td>\n",
              "      <td>1930</td>\n",
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              "      <td>Uruguay</td>\n",
              "      <td>13</td>\n",
              "      <td>18</td>\n",
              "      <td>70</td>\n",
              "      <td>3.9</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"EDITION\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"2014 FIFA World Cup Brazil \\u2122\",\n          \"1938 FIFA World Cup France \\u2122\",\n          \"1954 FIFA World Cup Switzerland \\u2122\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YEAR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1930,\n        \"max\": 2014,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          2014,\n          1938,\n          1954\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOCATION\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Argentina\",\n          \"Chile\",\n          \"Brazil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WINNER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Spain\",\n          \"Argentina\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TEAMS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 13,\n        \"max\": 32,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          24,\n          15,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MATCHES\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 17,\n        \"max\": 64,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          18,\n          52,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GOALS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32,\n        \"min\": 70,\n        \"max\": 171,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          171,\n          145,\n          115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AVERAGE_GOALS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8743569065318807,\n        \"min\": 2.2,\n        \"max\": 5.4,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          4.1,\n          4.0,\n          2.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AVERAGE_ATTENDANCE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12415,\n        \"min\": 20872,\n        \"max\": 68991,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          52918,\n          20872,\n          29561\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data preparation**"
      ],
      "metadata": {
        "id": "jTTMcElMfmHq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***Data separation as X and Y***\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "25gdh3JOfriz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['WINNER']\n",
        "y\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "nIuxZa3yfqu_",
        "outputId": "e554473f-2adc-4d72-ee67-722d48a82fda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Germany\n",
              "1         Spain\n",
              "2         Italy\n",
              "3        Brazil\n",
              "4        France\n",
              "5        Brazil\n",
              "6       Germany\n",
              "7     Argentina\n",
              "8         Italy\n",
              "9     Argentina\n",
              "10      Germany\n",
              "11       Brazil\n",
              "12      England\n",
              "13       Brazil\n",
              "14       Brazil\n",
              "15      Germany\n",
              "16      Uruguay\n",
              "17        Italy\n",
              "18        Italy\n",
              "19      Uruguay\n",
              "Name: WINNER, dtype: object"
            ],
            "text/html": [
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              "      <th>WINNER</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>Italy</td>\n",
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              "      <td>Brazil</td>\n",
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              "      <td>France</td>\n",
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              "      <td>Brazil</td>\n",
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              "      <th>6</th>\n",
              "      <td>Germany</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Argentina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Italy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Argentina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Germany</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Brazil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>England</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Brazil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Brazil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Germany</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Uruguay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Italy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Italy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Uruguay</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('WINNER', axis=1)\n",
        "X\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7a4n_pX1fxlQ",
        "outputId": "cb03e14e-91aa-42d1-84f0-4af394a2f51a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               EDITION  YEAR      LOCATION  TEAMS  MATCHES  \\\n",
              "0         2014 FIFA World Cup Brazil ™  2014        Brazil     32       64   \n",
              "1   2010 FIFA World Cup South Africa ™  2010  South Africa     32       64   \n",
              "2        2006 FIFA World Cup Germany ™  2006       Germany     32       64   \n",
              "3    2002 FIFA World Cup Korea/Japan ™  2002   Korea/Japan     32       64   \n",
              "4         1998 FIFA World Cup France ™  1998        France     32       64   \n",
              "5            1994 FIFA World Cup USA ™  1994           USA     24       52   \n",
              "6          1990 FIFA World Cup Italy ™  1990         Italy     24       52   \n",
              "7         1986 FIFA World Cup Mexico ™  1986        Mexico     24       52   \n",
              "8          1982 FIFA World Cup Spain ™  1982         Spain     24       52   \n",
              "9      1978 FIFA World Cup Argentina ™  1978     Argentina     16       38   \n",
              "10       1974 FIFA World Cup Germany ™  1974       Germany     16       38   \n",
              "11        1970 FIFA World Cup Mexico ™  1970        Mexico     16       32   \n",
              "12       1966 FIFA World Cup England ™  1966       England     16       32   \n",
              "13         1962 FIFA World Cup Chile ™  1962         Chile     16       32   \n",
              "14        1958 FIFA World Cup Sweden ™  1958        Sweden     16       35   \n",
              "15   1954 FIFA World Cup Switzerland ™  1954   Switzerland     16       26   \n",
              "16        1950 FIFA World Cup Brazil ™  1950        Brazil     13       22   \n",
              "17        1938 FIFA World Cup France ™  1938        France     15       18   \n",
              "18         1934 FIFA World Cup Italy ™  1934         Italy     16       17   \n",
              "19       1930 FIFA World Cup Uruguay ™  1930       Uruguay     13       18   \n",
              "\n",
              "    GOALS  AVERAGE_GOALS  AVERAGE_ATTENDANCE  \n",
              "0     171            2.7               52918  \n",
              "1     145            2.3               49669  \n",
              "2     147            2.3               52491  \n",
              "3     161            2.5               42268  \n",
              "4     171            2.7               43517  \n",
              "5     141            2.7               68991  \n",
              "6     115            2.2               48388  \n",
              "7     132            2.5               46039  \n",
              "8     146            2.8               40571  \n",
              "9     102            2.7               40678  \n",
              "10     97            2.6               49098  \n",
              "11     95            3.0               50124  \n",
              "12     89            2.8               48847  \n",
              "13     89            2.8               27911  \n",
              "14    126            3.6               23423  \n",
              "15    140            5.4               29561  \n",
              "16     88            4.0               47511  \n",
              "17     84            4.7               20872  \n",
              "18     70            4.1               21352  \n",
              "19     70            3.9               32808  "
            ],
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              "      <td>2014</td>\n",
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              "      <td>2.7</td>\n",
              "      <td>52918</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010 FIFA World Cup South Africa ™</td>\n",
              "      <td>2010</td>\n",
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              "      <td>32</td>\n",
              "      <td>64</td>\n",
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              "      <td>2.3</td>\n",
              "      <td>49669</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006 FIFA World Cup Germany ™</td>\n",
              "      <td>2006</td>\n",
              "      <td>Germany</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>147</td>\n",
              "      <td>2.3</td>\n",
              "      <td>52491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2002 FIFA World Cup Korea/Japan ™</td>\n",
              "      <td>2002</td>\n",
              "      <td>Korea/Japan</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>161</td>\n",
              "      <td>2.5</td>\n",
              "      <td>42268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1998 FIFA World Cup France ™</td>\n",
              "      <td>1998</td>\n",
              "      <td>France</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>171</td>\n",
              "      <td>2.7</td>\n",
              "      <td>43517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1994 FIFA World Cup USA ™</td>\n",
              "      <td>1994</td>\n",
              "      <td>USA</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>141</td>\n",
              "      <td>2.7</td>\n",
              "      <td>68991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1990 FIFA World Cup Italy ™</td>\n",
              "      <td>1990</td>\n",
              "      <td>Italy</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>115</td>\n",
              "      <td>2.2</td>\n",
              "      <td>48388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1986 FIFA World Cup Mexico ™</td>\n",
              "      <td>1986</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>132</td>\n",
              "      <td>2.5</td>\n",
              "      <td>46039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1982 FIFA World Cup Spain ™</td>\n",
              "      <td>1982</td>\n",
              "      <td>Spain</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>146</td>\n",
              "      <td>2.8</td>\n",
              "      <td>40571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1978 FIFA World Cup Argentina ™</td>\n",
              "      <td>1978</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>16</td>\n",
              "      <td>38</td>\n",
              "      <td>102</td>\n",
              "      <td>2.7</td>\n",
              "      <td>40678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1974 FIFA World Cup Germany ™</td>\n",
              "      <td>1974</td>\n",
              "      <td>Germany</td>\n",
              "      <td>16</td>\n",
              "      <td>38</td>\n",
              "      <td>97</td>\n",
              "      <td>2.6</td>\n",
              "      <td>49098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1970 FIFA World Cup Mexico ™</td>\n",
              "      <td>1970</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>16</td>\n",
              "      <td>32</td>\n",
              "      <td>95</td>\n",
              "      <td>3.0</td>\n",
              "      <td>50124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1966 FIFA World Cup England ™</td>\n",
              "      <td>1966</td>\n",
              "      <td>England</td>\n",
              "      <td>16</td>\n",
              "      <td>32</td>\n",
              "      <td>89</td>\n",
              "      <td>2.8</td>\n",
              "      <td>48847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1962 FIFA World Cup Chile ™</td>\n",
              "      <td>1962</td>\n",
              "      <td>Chile</td>\n",
              "      <td>16</td>\n",
              "      <td>32</td>\n",
              "      <td>89</td>\n",
              "      <td>2.8</td>\n",
              "      <td>27911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1958 FIFA World Cup Sweden ™</td>\n",
              "      <td>1958</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>16</td>\n",
              "      <td>35</td>\n",
              "      <td>126</td>\n",
              "      <td>3.6</td>\n",
              "      <td>23423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1954 FIFA World Cup Switzerland ™</td>\n",
              "      <td>1954</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>16</td>\n",
              "      <td>26</td>\n",
              "      <td>140</td>\n",
              "      <td>5.4</td>\n",
              "      <td>29561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1950 FIFA World Cup Brazil ™</td>\n",
              "      <td>1950</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>13</td>\n",
              "      <td>22</td>\n",
              "      <td>88</td>\n",
              "      <td>4.0</td>\n",
              "      <td>47511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1938 FIFA World Cup France ™</td>\n",
              "      <td>1938</td>\n",
              "      <td>France</td>\n",
              "      <td>15</td>\n",
              "      <td>18</td>\n",
              "      <td>84</td>\n",
              "      <td>4.7</td>\n",
              "      <td>20872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1934 FIFA World Cup Italy ™</td>\n",
              "      <td>1934</td>\n",
              "      <td>Italy</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>70</td>\n",
              "      <td>4.1</td>\n",
              "      <td>21352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1930 FIFA World Cup Uruguay ™</td>\n",
              "      <td>1930</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>13</td>\n",
              "      <td>18</td>\n",
              "      <td>70</td>\n",
              "      <td>3.9</td>\n",
              "      <td>32808</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"EDITION\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"2014 FIFA World Cup Brazil \\u2122\",\n          \"1938 FIFA World Cup France \\u2122\",\n          \"1954 FIFA World Cup Switzerland \\u2122\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YEAR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1930,\n        \"max\": 2014,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          2014,\n          1938,\n          1954\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOCATION\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Argentina\",\n          \"Chile\",\n          \"Brazil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TEAMS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 13,\n        \"max\": 32,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          24,\n          15,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MATCHES\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 17,\n        \"max\": 64,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          18,\n          52,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GOALS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32,\n        \"min\": 70,\n        \"max\": 171,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          171,\n          145,\n          115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AVERAGE_GOALS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8743569065318807,\n        \"min\": 2.2,\n        \"max\": 5.4,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          4.1,\n          4.0,\n          2.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AVERAGE_ATTENDANCE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12415,\n        \"min\": 20872,\n        \"max\": 68991,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          52918,\n          20872,\n          29561\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we’ll check our dataset for null values using seaborn:"
      ],
      "metadata": {
        "id": "nzd3M1zi3eKs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(df.isnull())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "-FCUtF2m3OFE",
        "outputId": "72287569-8db3-4c98-e3f0-87b727c7a424"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data splitting**"
      ],
      "metadata": {
        "id": "jX3czPdigWbN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**sklearn.model_selection**"
      ],
      "metadata": {
        "id": "95EbYqnlgYYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)"
      ],
      "metadata": {
        "id": "ba_TCN5ZgVBe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yropkLFkgtMS",
        "outputId": "60eb4821-b97a-4450-fdf4-a81a8a18eb75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               EDITION  YEAR      LOCATION  TEAMS  MATCHES  \\\n",
              "13         1962 FIFA World Cup Chile ™  1962         Chile     16       32   \n",
              "6          1990 FIFA World Cup Italy ™  1990         Italy     24       52   \n",
              "16        1950 FIFA World Cup Brazil ™  1950        Brazil     13       22   \n",
              "1   2010 FIFA World Cup South Africa ™  2010  South Africa     32       64   \n",
              "9      1978 FIFA World Cup Argentina ™  1978     Argentina     16       38   \n",
              "14        1958 FIFA World Cup Sweden ™  1958        Sweden     16       35   \n",
              "12       1966 FIFA World Cup England ™  1966       England     16       32   \n",
              "5            1994 FIFA World Cup USA ™  1994           USA     24       52   \n",
              "2        2006 FIFA World Cup Germany ™  2006       Germany     32       64   \n",
              "4         1998 FIFA World Cup France ™  1998        France     32       64   \n",
              "10       1974 FIFA World Cup Germany ™  1974       Germany     16       38   \n",
              "0         2014 FIFA World Cup Brazil ™  2014        Brazil     32       64   \n",
              "15   1954 FIFA World Cup Switzerland ™  1954   Switzerland     16       26   \n",
              "7         1986 FIFA World Cup Mexico ™  1986        Mexico     24       52   \n",
              "3    2002 FIFA World Cup Korea/Japan ™  2002   Korea/Japan     32       64   \n",
              "8          1982 FIFA World Cup Spain ™  1982         Spain     24       52   \n",
              "\n",
              "    GOALS  AVERAGE_GOALS  AVERAGE_ATTENDANCE  \n",
              "13     89            2.8               27911  \n",
              "6     115            2.2               48388  \n",
              "16     88            4.0               47511  \n",
              "1     145            2.3               49669  \n",
              "9     102            2.7               40678  \n",
              "14    126            3.6               23423  \n",
              "12     89            2.8               48847  \n",
              "5     141            2.7               68991  \n",
              "2     147            2.3               52491  \n",
              "4     171            2.7               43517  \n",
              "10     97            2.6               49098  \n",
              "0     171            2.7               52918  \n",
              "15    140            5.4               29561  \n",
              "7     132            2.5               46039  \n",
              "3     161            2.5               42268  \n",
              "8     146            2.8               40571  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-687af835-967c-4a43-b5c0-d49d3a0eb39a\" 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>EDITION</th>\n",
              "      <th>YEAR</th>\n",
              "      <th>LOCATION</th>\n",
              "      <th>TEAMS</th>\n",
              "      <th>MATCHES</th>\n",
              "      <th>GOALS</th>\n",
              "      <th>AVERAGE_GOALS</th>\n",
              "      <th>AVERAGE_ATTENDANCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1962 FIFA World Cup Chile ™</td>\n",
              "      <td>1962</td>\n",
              "      <td>Chile</td>\n",
              "      <td>16</td>\n",
              "      <td>32</td>\n",
              "      <td>89</td>\n",
              "      <td>2.8</td>\n",
              "      <td>27911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1990 FIFA World Cup Italy ™</td>\n",
              "      <td>1990</td>\n",
              "      <td>Italy</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>115</td>\n",
              "      <td>2.2</td>\n",
              "      <td>48388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1950 FIFA World Cup Brazil ™</td>\n",
              "      <td>1950</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>13</td>\n",
              "      <td>22</td>\n",
              "      <td>88</td>\n",
              "      <td>4.0</td>\n",
              "      <td>47511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010 FIFA World Cup South Africa ™</td>\n",
              "      <td>2010</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>145</td>\n",
              "      <td>2.3</td>\n",
              "      <td>49669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1978 FIFA World Cup Argentina ™</td>\n",
              "      <td>1978</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>16</td>\n",
              "      <td>38</td>\n",
              "      <td>102</td>\n",
              "      <td>2.7</td>\n",
              "      <td>40678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1958 FIFA World Cup Sweden ™</td>\n",
              "      <td>1958</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>16</td>\n",
              "      <td>35</td>\n",
              "      <td>126</td>\n",
              "      <td>3.6</td>\n",
              "      <td>23423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1966 FIFA World Cup England ™</td>\n",
              "      <td>1966</td>\n",
              "      <td>England</td>\n",
              "      <td>16</td>\n",
              "      <td>32</td>\n",
              "      <td>89</td>\n",
              "      <td>2.8</td>\n",
              "      <td>48847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1994 FIFA World Cup USA ™</td>\n",
              "      <td>1994</td>\n",
              "      <td>USA</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>141</td>\n",
              "      <td>2.7</td>\n",
              "      <td>68991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006 FIFA World Cup Germany ™</td>\n",
              "      <td>2006</td>\n",
              "      <td>Germany</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>147</td>\n",
              "      <td>2.3</td>\n",
              "      <td>52491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1998 FIFA World Cup France ™</td>\n",
              "      <td>1998</td>\n",
              "      <td>France</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>171</td>\n",
              "      <td>2.7</td>\n",
              "      <td>43517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1974 FIFA World Cup Germany ™</td>\n",
              "      <td>1974</td>\n",
              "      <td>Germany</td>\n",
              "      <td>16</td>\n",
              "      <td>38</td>\n",
              "      <td>97</td>\n",
              "      <td>2.6</td>\n",
              "      <td>49098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014 FIFA World Cup Brazil ™</td>\n",
              "      <td>2014</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>171</td>\n",
              "      <td>2.7</td>\n",
              "      <td>52918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1954 FIFA World Cup Switzerland ™</td>\n",
              "      <td>1954</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>16</td>\n",
              "      <td>26</td>\n",
              "      <td>140</td>\n",
              "      <td>5.4</td>\n",
              "      <td>29561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1986 FIFA World Cup Mexico ™</td>\n",
              "      <td>1986</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>132</td>\n",
              "      <td>2.5</td>\n",
              "      <td>46039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2002 FIFA World Cup Korea/Japan ™</td>\n",
              "      <td>2002</td>\n",
              "      <td>Korea/Japan</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>161</td>\n",
              "      <td>2.5</td>\n",
              "      <td>42268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1982 FIFA World Cup Spain ™</td>\n",
              "      <td>1982</td>\n",
              "      <td>Spain</td>\n",
              "      <td>24</td>\n",
              "      <td>52</td>\n",
              "      <td>146</td>\n",
              "      <td>2.8</td>\n",
              "      <td>40571</td>\n",
              "    </tr>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"EDITION\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"1962 FIFA World Cup Chile \\u2122\",\n          \"1990 FIFA World Cup Italy \\u2122\",\n          \"1958 FIFA World Cup Sweden \\u2122\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YEAR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 1950,\n        \"max\": 2014,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          1962,\n          1990,\n          1958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOCATION\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"France\",\n          \"Mexico\",\n          \"Chile\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TEAMS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 13,\n        \"max\": 32,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          24,\n          32,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MATCHES\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 22,\n        \"max\": 64,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          32,\n          52,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GOALS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 88,\n        \"max\": 171,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          97,\n          132,\n          89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AVERAGE_GOALS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8065358020571686,\n        \"min\": 2.2,\n        \"max\": 5.4,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          5.4,\n          2.2,\n          3.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AVERAGE_ATTENDANCE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10974,\n        \"min\": 23423,\n        \"max\": 68991,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          27911,\n          48388,\n          23423\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "VCtkyjyviNIu",
        "outputId": "465bc482-35d0-4011-b873-66e80ffd991b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          EDITION  YEAR LOCATION  TEAMS  MATCHES  GOALS  \\\n",
              "17   1938 FIFA World Cup France ™  1938   France     15       18     84   \n",
              "19  1930 FIFA World Cup Uruguay ™  1930  Uruguay     13       18     70   \n",
              "11   1970 FIFA World Cup Mexico ™  1970   Mexico     16       32     95   \n",
              "18    1934 FIFA World Cup Italy ™  1934    Italy     16       17     70   \n",
              "\n",
              "    AVERAGE_GOALS  AVERAGE_ATTENDANCE  \n",
              "17            4.7               20872  \n",
              "19            3.9               32808  \n",
              "11            3.0               50124  \n",
              "18            4.1               21352  "
            ],
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1930 FIFA World Cup Uruguay ™</td>\n",
              "      <td>1930</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>13</td>\n",
              "      <td>18</td>\n",
              "      <td>70</td>\n",
              "      <td>3.9</td>\n",
              "      <td>32808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1970 FIFA World Cup Mexico ™</td>\n",
              "      <td>1970</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>16</td>\n",
              "      <td>32</td>\n",
              "      <td>95</td>\n",
              "      <td>3.0</td>\n",
              "      <td>50124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1934 FIFA World Cup Italy ™</td>\n",
              "      <td>1934</td>\n",
              "      <td>Italy</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>70</td>\n",
              "      <td>4.1</td>\n",
              "      <td>21352</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d531e3f-b846-49aa-92e6-3afad07498b8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4d531e3f-b846-49aa-92e6-3afad07498b8 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-4d531e3f-b846-49aa-92e6-3afad07498b8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-90353076-91b5-4a6a-a8fc-bb3a7a67491e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-90353076-91b5-4a6a-a8fc-bb3a7a67491e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8c96002f-e356-4a78-ba02-ddd717831a09\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8c96002f-e356-4a78-ba02-ddd717831a09 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test",
              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"EDITION\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1930 FIFA World Cup Uruguay \\u2122\",\n          \"1934 FIFA World Cup Italy \\u2122\",\n          \"1938 FIFA World Cup France \\u2122\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YEAR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 1930,\n        \"max\": 1970,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1930,\n          1934,\n          1938\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOCATION\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Uruguay\",\n          \"Italy\",\n          \"France\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TEAMS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 13,\n        \"max\": 16,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          15,\n          13,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MATCHES\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 17,\n        \"max\": 32,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          18,\n          32,\n          17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GOALS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 70,\n        \"max\": 95,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          84,\n          70,\n          95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AVERAGE_GOALS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.704154339142587,\n        \"min\": 3.0,\n        \"max\": 4.7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.9,\n          4.1,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AVERAGE_ATTENDANCE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13715,\n        \"min\": 20872,\n        \"max\": 50124,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          32808,\n          21352,\n          20872\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " We’ll visually explore the data before building our model."
      ],
      "metadata": {
        "id": "gMRe2Q_73qOg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='WINNER', data=df).set(title='count(WINNER)=f(x)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "bO2CdBcW3pYG",
        "outputId": "02fa9e0e-eba8-406f-a477-60c4b7088844"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'count(WINNER)=f(x)')]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Building**"
      ],
      "metadata": {
        "id": "FeMXhL1QiTKH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***CLASSIFICATION***"
      ],
      "metadata": {
        "id": "5QBwmJIhiU0f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***1.   Training the model***\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "1JtRSNJKiYav"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we’ll convert all “non-null object” columns to dummy variables using the following process:\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "i1B22vws47EQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "WINNER=pd.get_dummies(df.WINNER)\n",
        "EDITION=pd.get_dummies(df.EDITION)\n",
        "LOCATION=pd.get_dummies(df.LOCATION)\n"
      ],
      "metadata": {
        "id": "6E0c56cM47Re"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# Assuming 'df' is your DataFrame\n",
        "\n",
        "# Create a OneHotEncoder object\n",
        "encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore') # sparse=False for dense output\n",
        "\n",
        "# Select categorical columns for encoding\n",
        "categorical_cols = ['EDITION', 'LOCATION']  # Add other categorical columns if needed\n",
        "\n",
        "# Fit and transform the encoder on the categorical columns\n",
        "encoded_data = encoder.fit_transform(df[categorical_cols])\n",
        "\n",
        "# Create a DataFrame from the encoded data\n",
        "encoded_df = pd.DataFrame(encoded_data, columns=encoder.get_feature_names_out(categorical_cols))\n",
        "\n",
        "# Concatenate the encoded DataFrame with the original DataFrame\n",
        "df = pd.concat([df, encoded_df], axis=1)\n",
        "\n",
        "# Drop the original categorical columns\n",
        "df = df.drop(categorical_cols, axis=1)\n",
        "\n",
        "\n",
        "# Now proceed with model training\n",
        "y = df['WINNER']\n",
        "X = df.drop('WINNER', axis=1)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)\n",
        "\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "i8rZjmMsihzx",
        "outputId": "ac6f3fb9-4fe3-4ffd-c2f3-43606f075eaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***2.  Applying the model to make a prediction***\n",
        "\n"
      ],
      "metadata": {
        "id": "363ZL1eeitqu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_model_train_pred = model.predict(X_train)\n",
        "y_model_test_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "B24jqGLHis50"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_model_train_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mUumD9UEi5tl",
        "outputId": "67a95e72-bb13-485e-86e2-1fa7546dfd90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Brazil', 'Germany', 'Germany', 'Germany', 'Brazil', 'Brazil',\n",
              "       'Germany', 'Germany', 'Germany', 'Germany', 'Germany', 'Germany',\n",
              "       'Brazil', 'Germany', 'Brazil', 'Brazil'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_model_test_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8kS_hr27i-I9",
        "outputId": "4046d1f7-f37b-4e4c-e014-66a4f1c34cc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Brazil', 'Brazil', 'Germany', 'Brazil'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    }
  ]
}