soccer_englad.ipynb

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      "provenance": [],
      "authorship_tag": "ABX9TyOhW40YzjH/RS56SDNxrusd",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/malzate10/mental-Ataraxia/blob/main/soccer_englad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KNruYHpIWQTa",
        "outputId": "cfadad17-fafa-4169-83a4-2684cccc24d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9656 sha256=61ddf13e95585e90aa3f8ca8053d40a27740d7a6daaf993af6abf36263571297\n",
            "  Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n",
            "Successfully built wget\n",
            "Installing collected packages: wget\n",
            "Successfully installed wget-3.2\n",
            "READY\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import keras\n",
        "from keras.utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras import backend as K\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "#from sklearn.preprocessing import scale\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score\n",
        "!pip install wget\n",
        "import wget\n",
        "print('READY')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url='https://www.football-data.co.uk/mmz4281/1718/E0.csv'\n",
        "wget.download(url, 'E0.csv')\n",
        "print('Datos Descargados!')"
      ],
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        "colab": {
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        },
        "id": "77CWvCZSaOez",
        "outputId": "29e0368f-53b7-45e4-c605-93b5b07a76bd"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Datos Descargados!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dato=pd.read_csv('./E0.csv')"
      ],
      "metadata": {
        "id": "SNzknOo1pIxW"
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      "execution_count": 5,
      "outputs": []
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      "source": [
        "dato.shape"
      ],
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        "outputId": "1bb4fa82-c698-46c5-ce56-9996c374d7d7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(380, 65)"
            ]
          },
          "metadata": {},
          "execution_count": 6
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      "source": [
        "dato.head()"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Div        Date        HomeTeam      AwayTeam  FTHG  FTAG FTR  HTHG  HTAG  \\\n",
              "0  E0  11/08/2017         Arsenal     Leicester     4     3   H     2     2   \n",
              "1  E0  12/08/2017        Brighton      Man City     0     2   A     0     0   \n",
              "2  E0  12/08/2017         Chelsea       Burnley     2     3   A     0     3   \n",
              "3  E0  12/08/2017  Crystal Palace  Huddersfield     0     3   A     0     2   \n",
              "4  E0  12/08/2017         Everton         Stoke     1     0   H     1     0   \n",
              "\n",
              "  HTR  ... BbAv<2.5  BbAH  BbAHh  BbMxAHH  BbAvAHH  BbMxAHA  BbAvAHA   PSCH  \\\n",
              "0   D  ...     2.32    21  -1.00     1.91     1.85     2.10     2.02   1.49   \n",
              "1   D  ...     2.27    20   1.50     1.95     1.91     2.01     1.96  11.75   \n",
              "2   A  ...     2.23    20  -1.75     2.03     1.97     1.95     1.90   1.33   \n",
              "3   A  ...     1.72    18  -0.75     2.10     2.05     1.86     1.83   1.79   \n",
              "4   H  ...     1.76    19  -0.75     1.94     1.90     2.01     1.98   1.82   \n",
              "\n",
              "   PSCD   PSCA  \n",
              "0  4.73   7.25  \n",
              "1  6.15   1.29  \n",
              "2  5.40  12.25  \n",
              "3  3.56   5.51  \n",
              "4  3.49   5.42  \n",
              "\n",
              "[5 rows x 65 columns]"
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              "      <th>Div</th>\n",
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              "      <th>0</th>\n",
              "      <td>E0</td>\n",
              "      <td>11/08/2017</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>Leicester</td>\n",
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              "      <td>7.25</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>E0</td>\n",
              "      <td>12/08/2017</td>\n",
              "      <td>Brighton</td>\n",
              "      <td>Man City</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
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              "      <td>D</td>\n",
              "      <td>...</td>\n",
              "      <td>2.27</td>\n",
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              "      <td>1.95</td>\n",
              "      <td>1.91</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>12/08/2017</td>\n",
              "      <td>Chelsea</td>\n",
              "      <td>Burnley</td>\n",
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              "      <td>A</td>\n",
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              "      <td>3</td>\n",
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              "      <td>2.23</td>\n",
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              "      <td>1.97</td>\n",
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              "      <td>1.90</td>\n",
              "      <td>1.33</td>\n",
              "      <td>5.40</td>\n",
              "      <td>12.25</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>E0</td>\n",
              "      <td>12/08/2017</td>\n",
              "      <td>Crystal Palace</td>\n",
              "      <td>Huddersfield</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>1.72</td>\n",
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              "      <td>Stoke</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1.76</td>\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4a0f118b-9e1c-477b-a2c4-a2a49fdeb69c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dato"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dato.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wC_jc8QSqIlp",
        "outputId": "a52205b1-bd1a-4e66-8c16-e1ab108081e1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Div         0\n",
              "Date        0\n",
              "HomeTeam    0\n",
              "AwayTeam    0\n",
              "FTHG        0\n",
              "           ..\n",
              "BbMxAHA     0\n",
              "BbAvAHA     0\n",
              "PSCH        0\n",
              "PSCD        0\n",
              "PSCA        0\n",
              "Length: 65, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "team_name = {}\n",
        "index = 0\n",
        "for idx, row in dato.iterrows():\n",
        "    name = row['HomeTeam']\n",
        "    if(name not in team_name.keys()):\n",
        "        team_name[name] = index\n",
        "        index += 1\n",
        "    name = row['AwayTeam']\n",
        "    if(name not in team_name.keys()):\n",
        "        team_name[name] = index\n",
        "        index += 1\n",
        "\n",
        "team_name"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EziPb4HhqY62",
        "outputId": "44235c6e-c725-40fc-ef74-9e89f4332d63"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Arsenal': 0,\n",
              " 'Leicester': 1,\n",
              " 'Brighton': 2,\n",
              " 'Man City': 3,\n",
              " 'Chelsea': 4,\n",
              " 'Burnley': 5,\n",
              " 'Crystal Palace': 6,\n",
              " 'Huddersfield': 7,\n",
              " 'Everton': 8,\n",
              " 'Stoke': 9,\n",
              " 'Southampton': 10,\n",
              " 'Swansea': 11,\n",
              " 'Watford': 12,\n",
              " 'Liverpool': 13,\n",
              " 'West Brom': 14,\n",
              " 'Bournemouth': 15,\n",
              " 'Man United': 16,\n",
              " 'West Ham': 17,\n",
              " 'Newcastle': 18,\n",
              " 'Tottenham': 19}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total number of matches.\n",
        "n_matches = dato.shape[0]\n",
        "\n",
        "# Calculate number of features. -1 because we are saving one as the target variable (win/lose/draw)\n",
        "n_features = dato.shape[1] - 1\n",
        "\n",
        "# Calculate matches won by home team.\n",
        "n_homewins = len(dato[dato.FTR == 'H'])\n",
        "\n",
        "# Calculate win rate for home team.\n",
        "win_rate = (float(n_homewins) / (n_matches)) * 100\n",
        "\n",
        "# Print the results\n",
        "print(\"Total number of matches: {}\".format(n_matches))\n",
        "print(\"Number of features: {}\".format(n_features))\n",
        "print(\"Number of matches won by home team: {}\".format(n_homewins))\n",
        "print(\"Win rate of home team: {:.2f}%\".format(win_rate))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uj86ktzJqli-",
        "outputId": "ee40d4bd-239c-4581-f480-4a0a6dbbd7d7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of matches: 380\n",
            "Number of features: 64\n",
            "Number of matches won by home team: 173\n",
            "Win rate of home team: 45.53%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lb_make = LabelEncoder()\n",
        "dato['categorical FTR'] = lb_make.fit_transform(dato['FTR'])"
      ],
      "metadata": {
        "id": "RLz-_nnIq3L7"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dato['FTR'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ERDMwGiq5FK",
        "outputId": "f15c992e-c4ee-4b13-8553-baa678d97d86"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FTR\n",
              "H    173\n",
              "A    108\n",
              "D     99\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dato['categorical FTR'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpMj5oRdrGNZ",
        "outputId": "b95e8ad4-308e-403b-a788-11aa7b9d1911"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "categorical FTR\n",
              "2    173\n",
              "0    108\n",
              "1     99\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate into feature set and target variable\n",
        "#FTR = Full Time Result (H=Home Win, D=Draw, A=Away Win)\n",
        "columns_name=['FTHG','FTAG','HTHG','HTAG','HS','AS','HST','AST']\n",
        "X_all = dato[columns_name]\n",
        "y_all = dato['categorical FTR']"
      ],
      "metadata": {
        "id": "7ff0EDT0rIoo"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_all = to_categorical(y_all)"
      ],
      "metadata": {
        "id": "s9aLYMMDrSJe"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_all[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-20j9rtrXmM",
        "outputId": "b096a6cc-1f41-4739-b383-d91490d6d610"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 1.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [0., 0., 1.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.],\n",
              "       [0., 0., 1.],\n",
              "       [1., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_all.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CBtJhcYjrdkD",
        "outputId": "8299ed74-f1b5-46a2-f84b-607f37a967ba"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   FTHG  FTAG  HTHG  HTAG  HS  AS  HST  AST\n",
              "0     4     3     2     2  27   6   10    3\n",
              "1     0     2     0     0   6  14    2    4\n",
              "2     2     3     0     3  19  10    6    5\n",
              "3     0     3     0     2  14   8    4    6\n",
              "4     1     0     1     0   9   9    4    1"
            ],
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              "type": "dataframe",
              "variable_name": "X_all",
              "summary": "{\n  \"name\": \"X_all\",\n  \"rows\": 380,\n  \"fields\": [\n    {\n      \"column\": \"FTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4,\n          0,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 2,\n        \"max\": 35,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          3,\n          10,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 30,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          16,\n          25,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 15,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          7,\n          9,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 14,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          11,\n          10,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in X_all:\n",
        "    sns.kdeplot(X_all[col], shade=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oKErXWO7rnby",
        "outputId": "b05e71ae-1938-426a-a568-7e4f1df7f900"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-4a8a73b7215f>:2: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_all[col], shade=True)\n",
            "<ipython-input-18-4a8a73b7215f>:2: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_all[col], shade=True)\n",
            "<ipython-input-18-4a8a73b7215f>:2: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_all[col], shade=True)\n",
            "<ipython-input-18-4a8a73b7215f>:2: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_all[col], shade=True)\n",
            "<ipython-input-18-4a8a73b7215f>:2: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_all[col], shade=True)\n",
            "<ipython-input-18-4a8a73b7215f>:2: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_all[col], shade=True)\n",
            "<ipython-input-18-4a8a73b7215f>:2: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_all[col], shade=True)\n",
            "<ipython-input-18-4a8a73b7215f>:2: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_all[col], shade=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit on training data column\n",
        "scale = StandardScaler().fit(X_all)\n",
        "\n",
        "# transform the training data column\n",
        "scaled_df= scale.transform(X_all)\n",
        "X_allnorm= pd.DataFrame(scaled_df, columns=columns_name)\n",
        "X_allnorm.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Yg4lRcUervor",
        "outputId": "c8142a7a-0956-44d7-8678-65f117d7eb00"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       FTHG      FTAG      HTHG      HTAG        HS        AS       HST  \\\n",
              "0  1.844414  1.575043  1.756913  1.964501  2.325776 -1.001640  1.984099   \n",
              "1 -1.144402  0.724878 -0.767196 -0.682699 -1.316236  0.647865 -0.971149   \n",
              "2  0.350006  1.575043 -0.767196  3.288101  0.938343 -0.176888  0.506475   \n",
              "3 -1.144402  1.575043 -0.767196  1.964501  0.071197 -0.589264 -0.232337   \n",
              "4 -0.397198 -0.975453  0.494858 -0.682699 -0.795949 -0.383076 -0.232337   \n",
              "\n",
              "        AST  \n",
              "0 -0.331312  \n",
              "1  0.101329  \n",
              "2  0.533969  \n",
              "3  0.966610  \n",
              "4 -1.196593  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c62085d4-d9e3-4cb8-8e0b-886bb6246386\" 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>FTHG</th>\n",
              "      <th>FTAG</th>\n",
              "      <th>HTHG</th>\n",
              "      <th>HTAG</th>\n",
              "      <th>HS</th>\n",
              "      <th>AS</th>\n",
              "      <th>HST</th>\n",
              "      <th>AST</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.844414</td>\n",
              "      <td>1.575043</td>\n",
              "      <td>1.756913</td>\n",
              "      <td>1.964501</td>\n",
              "      <td>2.325776</td>\n",
              "      <td>-1.001640</td>\n",
              "      <td>1.984099</td>\n",
              "      <td>-0.331312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.144402</td>\n",
              "      <td>0.724878</td>\n",
              "      <td>-0.767196</td>\n",
              "      <td>-0.682699</td>\n",
              "      <td>-1.316236</td>\n",
              "      <td>0.647865</td>\n",
              "      <td>-0.971149</td>\n",
              "      <td>0.101329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.350006</td>\n",
              "      <td>1.575043</td>\n",
              "      <td>-0.767196</td>\n",
              "      <td>3.288101</td>\n",
              "      <td>0.938343</td>\n",
              "      <td>-0.176888</td>\n",
              "      <td>0.506475</td>\n",
              "      <td>0.533969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.144402</td>\n",
              "      <td>1.575043</td>\n",
              "      <td>-0.767196</td>\n",
              "      <td>1.964501</td>\n",
              "      <td>0.071197</td>\n",
              "      <td>-0.589264</td>\n",
              "      <td>-0.232337</td>\n",
              "      <td>0.966610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.397198</td>\n",
              "      <td>-0.975453</td>\n",
              "      <td>0.494858</td>\n",
              "      <td>-0.682699</td>\n",
              "      <td>-0.795949</td>\n",
              "      <td>-0.383076</td>\n",
              "      <td>-0.232337</td>\n",
              "      <td>-1.196593</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_allnorm",
              "summary": "{\n  \"name\": \"X_allnorm\",\n  \"rows\": 380,\n  \"fields\": [\n    {\n      \"column\": \"FTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0013183921348099,\n        \"min\": -1.144401707621983,\n        \"max\": 4.086025340959588,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.8444137487103436,\n          -1.144401707621983,\n          2.591617612793425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0013183921348097,\n        \"min\": -0.9754525248511572,\n        \"max\": 4.125537742719115,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.5750426089339786,\n          0.7248775643389334,\n          4.125537742719115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0013183921348099,\n        \"min\": -0.7671963318944575,\n        \"max\": 4.281021955895912,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.7671963318944575,\n          4.281021955895912,\n          0.49485824005313495\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0013183921348097,\n        \"min\": -0.6826989390686176,\n        \"max\": 3.2881010126570147,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.6826989390686176,\n          0.6409010448399266,\n          1.9645010287484708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0013183921348099,\n        \"min\": -2.0099525244252248,\n        \"max\": 3.713209295804639,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          -1.8365233783576531,\n          -0.6225193558846518,\n          1.9789178351289225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0013183921348097,\n        \"min\": -2.238769195252752,\n        \"max\": 3.9468752123772464,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          1.0602411554832472,\n          2.9159344777722467,\n          -1.2078284606477525\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0013183921348097,\n        \"min\": -1.709961255438975,\n        \"max\": 3.831129794022172,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.8758812343095601,\n          1.614693374237713,\n          1.9840994442017894\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0013183921348099,\n        \"min\": -1.6292330400083612,\n        \"max\": 4.427733957087713,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          3.129812457709983,\n          2.6971719579174063,\n          -0.33131154063063106\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit scaler on training data\n",
        "scale_2 = MinMaxScaler().fit(X_all)\n",
        "\n",
        "# transform training data\n",
        "scale_df_normalize = scale_2.transform(X_all)\n",
        "X_allnorm_norma= pd.DataFrame(scale_df_normalize, columns=columns_name)\n",
        "X_allnorm_norma.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7sAEbQUhsNN8",
        "outputId": "3597b877-f6db-4f23-9cb1-de2a934cf988"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       FTHG      FTAG  HTHG      HTAG        HS        AS       HST       AST\n",
              "0  0.571429  0.500000  0.50  0.666667  0.757576  0.200000  0.666667  0.214286\n",
              "1  0.000000  0.333333  0.00  0.000000  0.121212  0.466667  0.133333  0.285714\n",
              "2  0.285714  0.500000  0.00  1.000000  0.515152  0.333333  0.400000  0.357143\n",
              "3  0.000000  0.500000  0.00  0.666667  0.363636  0.266667  0.266667  0.428571\n",
              "4  0.142857  0.000000  0.25  0.000000  0.212121  0.300000  0.266667  0.071429"
            ],
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              "    .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-984d731e-8bf0-41d0-9bf5-24f2caa23086 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-984d731e-8bf0-41d0-9bf5-24f2caa23086');\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",
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              "  .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",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5ff2d373-42d6-43f3-b193-66a45de1c815 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_allnorm_norma",
              "summary": "{\n  \"name\": \"X_allnorm_norma\",\n  \"rows\": 380,\n  \"fields\": [\n    {\n      \"column\": \"FTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1914410396769333,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.5714285714285714,\n          0.0,\n          0.7142857142857142\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19629882426961823,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.5,\n          0.3333333333333333,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19835085074601477,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          1.0,\n          0.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25217044532793853,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.3333333333333333,\n          0.6666666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17495895164023043,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          0.030303030303030304,\n          0.24242424242424243,\n          0.696969696969697\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16187778122190186,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          0.5333333333333333,\n          0.8333333333333334,\n          0.16666666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18070780342658052,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.4666666666666667,\n          0.6,\n          0.6666666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16531679842648597,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.7857142857142857,\n          0.7142857142857142,\n          0.21428571428571427\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Standardize\n",
        "for col in X_allnorm:\n",
        "    sns.kdeplot(X_allnorm[col], shade=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tTsdSehKsf6F",
        "outputId": "cec2d639-e5c8-4360-b097-0dcee5bbf96d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-4e6131e13992>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm[col], shade=True)\n",
            "<ipython-input-22-4e6131e13992>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm[col], shade=True)\n",
            "<ipython-input-22-4e6131e13992>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm[col], shade=True)\n",
            "<ipython-input-22-4e6131e13992>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm[col], shade=True)\n",
            "<ipython-input-22-4e6131e13992>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm[col], shade=True)\n",
            "<ipython-input-22-4e6131e13992>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm[col], shade=True)\n",
            "<ipython-input-22-4e6131e13992>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm[col], shade=True)\n",
            "<ipython-input-22-4e6131e13992>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm[col], shade=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalize\n",
        "for col in X_allnorm_norma:\n",
        "    sns.kdeplot(X_allnorm_norma[col], shade=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CxaMdBN5sqHu",
        "outputId": "219e4cb0-fcb3-4f95-a1a9-eee00d2d2371"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-d50d0def5cab>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm_norma[col], shade=True)\n",
            "<ipython-input-23-d50d0def5cab>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm_norma[col], shade=True)\n",
            "<ipython-input-23-d50d0def5cab>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm_norma[col], shade=True)\n",
            "<ipython-input-23-d50d0def5cab>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm_norma[col], shade=True)\n",
            "<ipython-input-23-d50d0def5cab>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm_norma[col], shade=True)\n",
            "<ipython-input-23-d50d0def5cab>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm_norma[col], shade=True)\n",
            "<ipython-input-23-d50d0def5cab>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm_norma[col], shade=True)\n",
            "<ipython-input-23-d50d0def5cab>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(X_allnorm_norma[col], shade=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Shuffle and split the dataset into training and testing set.\n",
        "X_train, X_test, y_train, y_test = train_test_split( X_allnorm_norma, y_all, test_size=0.3, random_state=4)\n",
        "print ('Train set:', X_train.shape,  y_train.shape)\n",
        "print ('Test set:', X_test.shape,  y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hv2tngvesxTL",
        "outputId": "683162ab-48b2-4b54-95f8-a279519ec70c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set: (266, 8) (266, 3)\n",
            "Test set: (114, 8) (114, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_cols = X_all.shape[1] # Numero de predictores\n",
        "print('El numero de predictores es:',n_cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i2BP39yIs_AI",
        "outputId": "b5bef89d-bb34-4137-8620-8b747c909aeb"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El numero de predictores es: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Definición del Modelo\n",
        "def regression_model():\n",
        "    # create model\n",
        "    model = Sequential()\n",
        "    model.add(Dense(10, activation='relu', input_shape=(n_cols,)))\n",
        "    model.add(Dense(10, activation='relu'))\n",
        "    model.add(Dense(10, activation='relu'))\n",
        "    model.add(Dense(3, activation='softmax'))\n",
        "    #model.add(Dense(1))\n",
        "\n",
        "    # compile model\n",
        "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "    return model"
      ],
      "metadata": {
        "id": "vML9_rF8va5J"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# build the model\n",
        "model = regression_model()"
      ],
      "metadata": {
        "id": "l28Q6HEFvscZ"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs=100\n",
        "# fit the model\n",
        "model_fit=model.fit(X_train, y_train, validation_data=(X_test,y_test), epochs=epochs, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IXDlOCVmvx9G",
        "outputId": "9facf228-8ea1-4846-e7e5-c7a85b283ba2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "9/9 - 1s - loss: 1.1018 - accuracy: 0.2481 - val_loss: 1.1010 - val_accuracy: 0.1930 - 1s/epoch - 165ms/step\n",
            "Epoch 2/100\n",
            "9/9 - 0s - loss: 1.0893 - accuracy: 0.3158 - val_loss: 1.0948 - val_accuracy: 0.3421 - 71ms/epoch - 8ms/step\n",
            "Epoch 3/100\n",
            "9/9 - 0s - loss: 1.0818 - accuracy: 0.4323 - val_loss: 1.0906 - val_accuracy: 0.3947 - 55ms/epoch - 6ms/step\n",
            "Epoch 4/100\n",
            "9/9 - 0s - loss: 1.0757 - accuracy: 0.4586 - val_loss: 1.0871 - val_accuracy: 0.4035 - 69ms/epoch - 8ms/step\n",
            "Epoch 5/100\n",
            "9/9 - 0s - loss: 1.0700 - accuracy: 0.4699 - val_loss: 1.0835 - val_accuracy: 0.4123 - 73ms/epoch - 8ms/step\n",
            "Epoch 6/100\n",
            "9/9 - 0s - loss: 1.0648 - accuracy: 0.4737 - val_loss: 1.0797 - val_accuracy: 0.4123 - 78ms/epoch - 9ms/step\n",
            "Epoch 7/100\n",
            "9/9 - 0s - loss: 1.0595 - accuracy: 0.4737 - val_loss: 1.0749 - val_accuracy: 0.4211 - 69ms/epoch - 8ms/step\n",
            "Epoch 8/100\n",
            "9/9 - 0s - loss: 1.0535 - accuracy: 0.4737 - val_loss: 1.0698 - val_accuracy: 0.4211 - 70ms/epoch - 8ms/step\n",
            "Epoch 9/100\n",
            "9/9 - 0s - loss: 1.0474 - accuracy: 0.4737 - val_loss: 1.0643 - val_accuracy: 0.4211 - 57ms/epoch - 6ms/step\n",
            "Epoch 10/100\n",
            "9/9 - 0s - loss: 1.0413 - accuracy: 0.4812 - val_loss: 1.0583 - val_accuracy: 0.4211 - 67ms/epoch - 7ms/step\n",
            "Epoch 11/100\n",
            "9/9 - 0s - loss: 1.0344 - accuracy: 0.4850 - val_loss: 1.0524 - val_accuracy: 0.4298 - 68ms/epoch - 8ms/step\n",
            "Epoch 12/100\n",
            "9/9 - 0s - loss: 1.0270 - accuracy: 0.4962 - val_loss: 1.0449 - val_accuracy: 0.4386 - 58ms/epoch - 6ms/step\n",
            "Epoch 13/100\n",
            "9/9 - 0s - loss: 1.0191 - accuracy: 0.5150 - val_loss: 1.0354 - val_accuracy: 0.4474 - 59ms/epoch - 7ms/step\n",
            "Epoch 14/100\n",
            "9/9 - 0s - loss: 1.0101 - accuracy: 0.5376 - val_loss: 1.0249 - val_accuracy: 0.4561 - 57ms/epoch - 6ms/step\n",
            "Epoch 15/100\n",
            "9/9 - 0s - loss: 1.0003 - accuracy: 0.5564 - val_loss: 1.0129 - val_accuracy: 0.4912 - 68ms/epoch - 8ms/step\n",
            "Epoch 16/100\n",
            "9/9 - 0s - loss: 0.9890 - accuracy: 0.5714 - val_loss: 1.0006 - val_accuracy: 0.5439 - 78ms/epoch - 9ms/step\n",
            "Epoch 17/100\n",
            "9/9 - 0s - loss: 0.9775 - accuracy: 0.5827 - val_loss: 0.9868 - val_accuracy: 0.5614 - 66ms/epoch - 7ms/step\n",
            "Epoch 18/100\n",
            "9/9 - 0s - loss: 0.9632 - accuracy: 0.6015 - val_loss: 0.9719 - val_accuracy: 0.5614 - 60ms/epoch - 7ms/step\n",
            "Epoch 19/100\n",
            "9/9 - 0s - loss: 0.9476 - accuracy: 0.6090 - val_loss: 0.9553 - val_accuracy: 0.5877 - 78ms/epoch - 9ms/step\n",
            "Epoch 20/100\n",
            "9/9 - 0s - loss: 0.9300 - accuracy: 0.6128 - val_loss: 0.9356 - val_accuracy: 0.5965 - 86ms/epoch - 10ms/step\n",
            "Epoch 21/100\n",
            "9/9 - 0s - loss: 0.9102 - accuracy: 0.6504 - val_loss: 0.9138 - val_accuracy: 0.5965 - 69ms/epoch - 8ms/step\n",
            "Epoch 22/100\n",
            "9/9 - 0s - loss: 0.8874 - accuracy: 0.6579 - val_loss: 0.8910 - val_accuracy: 0.6053 - 69ms/epoch - 8ms/step\n",
            "Epoch 23/100\n",
            "9/9 - 0s - loss: 0.8639 - accuracy: 0.6617 - val_loss: 0.8662 - val_accuracy: 0.6140 - 80ms/epoch - 9ms/step\n",
            "Epoch 24/100\n",
            "9/9 - 0s - loss: 0.8398 - accuracy: 0.6729 - val_loss: 0.8386 - val_accuracy: 0.6404 - 71ms/epoch - 8ms/step\n",
            "Epoch 25/100\n",
            "9/9 - 0s - loss: 0.8145 - accuracy: 0.6767 - val_loss: 0.8153 - val_accuracy: 0.6228 - 69ms/epoch - 8ms/step\n",
            "Epoch 26/100\n",
            "9/9 - 0s - loss: 0.7893 - accuracy: 0.6767 - val_loss: 0.7894 - val_accuracy: 0.6491 - 111ms/epoch - 12ms/step\n",
            "Epoch 27/100\n",
            "9/9 - 0s - loss: 0.7653 - accuracy: 0.6805 - val_loss: 0.7610 - val_accuracy: 0.6667 - 104ms/epoch - 12ms/step\n",
            "Epoch 28/100\n",
            "9/9 - 0s - loss: 0.7395 - accuracy: 0.6880 - val_loss: 0.7437 - val_accuracy: 0.6579 - 88ms/epoch - 10ms/step\n",
            "Epoch 29/100\n",
            "9/9 - 0s - loss: 0.7173 - accuracy: 0.6880 - val_loss: 0.7142 - val_accuracy: 0.6754 - 87ms/epoch - 10ms/step\n",
            "Epoch 30/100\n",
            "9/9 - 0s - loss: 0.6911 - accuracy: 0.6955 - val_loss: 0.6888 - val_accuracy: 0.6754 - 104ms/epoch - 12ms/step\n",
            "Epoch 31/100\n",
            "9/9 - 0s - loss: 0.6687 - accuracy: 0.6917 - val_loss: 0.6701 - val_accuracy: 0.6754 - 109ms/epoch - 12ms/step\n",
            "Epoch 32/100\n",
            "9/9 - 0s - loss: 0.6487 - accuracy: 0.6955 - val_loss: 0.6493 - val_accuracy: 0.6754 - 82ms/epoch - 9ms/step\n",
            "Epoch 33/100\n",
            "9/9 - 0s - loss: 0.6290 - accuracy: 0.7256 - val_loss: 0.6247 - val_accuracy: 0.7018 - 84ms/epoch - 9ms/step\n",
            "Epoch 34/100\n",
            "9/9 - 0s - loss: 0.6112 - accuracy: 0.7406 - val_loss: 0.6121 - val_accuracy: 0.6930 - 83ms/epoch - 9ms/step\n",
            "Epoch 35/100\n",
            "9/9 - 0s - loss: 0.5960 - accuracy: 0.7218 - val_loss: 0.6029 - val_accuracy: 0.6930 - 85ms/epoch - 9ms/step\n",
            "Epoch 36/100\n",
            "9/9 - 0s - loss: 0.5780 - accuracy: 0.7519 - val_loss: 0.5760 - val_accuracy: 0.7105 - 86ms/epoch - 10ms/step\n",
            "Epoch 37/100\n",
            "9/9 - 0s - loss: 0.5642 - accuracy: 0.7669 - val_loss: 0.5560 - val_accuracy: 0.7456 - 78ms/epoch - 9ms/step\n",
            "Epoch 38/100\n",
            "9/9 - 0s - loss: 0.5491 - accuracy: 0.7932 - val_loss: 0.5491 - val_accuracy: 0.7281 - 67ms/epoch - 7ms/step\n",
            "Epoch 39/100\n",
            "9/9 - 0s - loss: 0.5341 - accuracy: 0.7895 - val_loss: 0.5363 - val_accuracy: 0.7368 - 72ms/epoch - 8ms/step\n",
            "Epoch 40/100\n",
            "9/9 - 0s - loss: 0.5211 - accuracy: 0.7932 - val_loss: 0.5266 - val_accuracy: 0.7807 - 70ms/epoch - 8ms/step\n",
            "Epoch 41/100\n",
            "9/9 - 0s - loss: 0.5048 - accuracy: 0.8158 - val_loss: 0.5019 - val_accuracy: 0.7895 - 93ms/epoch - 10ms/step\n",
            "Epoch 42/100\n",
            "9/9 - 0s - loss: 0.4936 - accuracy: 0.8271 - val_loss: 0.4915 - val_accuracy: 0.8070 - 93ms/epoch - 10ms/step\n",
            "Epoch 43/100\n",
            "9/9 - 0s - loss: 0.4797 - accuracy: 0.8233 - val_loss: 0.4837 - val_accuracy: 0.8158 - 93ms/epoch - 10ms/step\n",
            "Epoch 44/100\n",
            "9/9 - 0s - loss: 0.4677 - accuracy: 0.8346 - val_loss: 0.4740 - val_accuracy: 0.8158 - 68ms/epoch - 8ms/step\n",
            "Epoch 45/100\n",
            "9/9 - 0s - loss: 0.4552 - accuracy: 0.8308 - val_loss: 0.4622 - val_accuracy: 0.8246 - 66ms/epoch - 7ms/step\n",
            "Epoch 46/100\n",
            "9/9 - 0s - loss: 0.4436 - accuracy: 0.8421 - val_loss: 0.4451 - val_accuracy: 0.8421 - 83ms/epoch - 9ms/step\n",
            "Epoch 47/100\n",
            "9/9 - 0s - loss: 0.4315 - accuracy: 0.8459 - val_loss: 0.4345 - val_accuracy: 0.8509 - 83ms/epoch - 9ms/step\n",
            "Epoch 48/100\n",
            "9/9 - 0s - loss: 0.4191 - accuracy: 0.8609 - val_loss: 0.4233 - val_accuracy: 0.8772 - 70ms/epoch - 8ms/step\n",
            "Epoch 49/100\n",
            "9/9 - 0s - loss: 0.4074 - accuracy: 0.8647 - val_loss: 0.4104 - val_accuracy: 0.8860 - 80ms/epoch - 9ms/step\n",
            "Epoch 50/100\n",
            "9/9 - 0s - loss: 0.3947 - accuracy: 0.8684 - val_loss: 0.4049 - val_accuracy: 0.8772 - 91ms/epoch - 10ms/step\n",
            "Epoch 51/100\n",
            "9/9 - 0s - loss: 0.3835 - accuracy: 0.8797 - val_loss: 0.3888 - val_accuracy: 0.8947 - 82ms/epoch - 9ms/step\n",
            "Epoch 52/100\n",
            "9/9 - 0s - loss: 0.3714 - accuracy: 0.8835 - val_loss: 0.3730 - val_accuracy: 0.9035 - 80ms/epoch - 9ms/step\n",
            "Epoch 53/100\n",
            "9/9 - 0s - loss: 0.3603 - accuracy: 0.8872 - val_loss: 0.3666 - val_accuracy: 0.9035 - 117ms/epoch - 13ms/step\n",
            "Epoch 54/100\n",
            "9/9 - 0s - loss: 0.3500 - accuracy: 0.8985 - val_loss: 0.3514 - val_accuracy: 0.9035 - 83ms/epoch - 9ms/step\n",
            "Epoch 55/100\n",
            "9/9 - 0s - loss: 0.3397 - accuracy: 0.9023 - val_loss: 0.3427 - val_accuracy: 0.9211 - 102ms/epoch - 11ms/step\n",
            "Epoch 56/100\n",
            "9/9 - 0s - loss: 0.3265 - accuracy: 0.8910 - val_loss: 0.3464 - val_accuracy: 0.9035 - 94ms/epoch - 10ms/step\n",
            "Epoch 57/100\n",
            "9/9 - 0s - loss: 0.3162 - accuracy: 0.8985 - val_loss: 0.3244 - val_accuracy: 0.9211 - 93ms/epoch - 10ms/step\n",
            "Epoch 58/100\n",
            "9/9 - 0s - loss: 0.3053 - accuracy: 0.9135 - val_loss: 0.3077 - val_accuracy: 0.9298 - 85ms/epoch - 9ms/step\n",
            "Epoch 59/100\n",
            "9/9 - 0s - loss: 0.2949 - accuracy: 0.9248 - val_loss: 0.3065 - val_accuracy: 0.9123 - 90ms/epoch - 10ms/step\n",
            "Epoch 60/100\n",
            "9/9 - 0s - loss: 0.2842 - accuracy: 0.9248 - val_loss: 0.2904 - val_accuracy: 0.9386 - 50ms/epoch - 6ms/step\n",
            "Epoch 61/100\n",
            "9/9 - 0s - loss: 0.2714 - accuracy: 0.9286 - val_loss: 0.2829 - val_accuracy: 0.9386 - 46ms/epoch - 5ms/step\n",
            "Epoch 62/100\n",
            "9/9 - 0s - loss: 0.2610 - accuracy: 0.9398 - val_loss: 0.2761 - val_accuracy: 0.9386 - 60ms/epoch - 7ms/step\n",
            "Epoch 63/100\n",
            "9/9 - 0s - loss: 0.2520 - accuracy: 0.9398 - val_loss: 0.2625 - val_accuracy: 0.9386 - 52ms/epoch - 6ms/step\n",
            "Epoch 64/100\n",
            "9/9 - 0s - loss: 0.2400 - accuracy: 0.9549 - val_loss: 0.2599 - val_accuracy: 0.9386 - 70ms/epoch - 8ms/step\n",
            "Epoch 65/100\n",
            "9/9 - 0s - loss: 0.2321 - accuracy: 0.9586 - val_loss: 0.2466 - val_accuracy: 0.9386 - 66ms/epoch - 7ms/step\n",
            "Epoch 66/100\n",
            "9/9 - 0s - loss: 0.2210 - accuracy: 0.9624 - val_loss: 0.2309 - val_accuracy: 0.9561 - 59ms/epoch - 7ms/step\n",
            "Epoch 67/100\n",
            "9/9 - 0s - loss: 0.2109 - accuracy: 0.9624 - val_loss: 0.2267 - val_accuracy: 0.9561 - 61ms/epoch - 7ms/step\n",
            "Epoch 68/100\n",
            "9/9 - 0s - loss: 0.2028 - accuracy: 0.9662 - val_loss: 0.2181 - val_accuracy: 0.9561 - 59ms/epoch - 7ms/step\n",
            "Epoch 69/100\n",
            "9/9 - 0s - loss: 0.1951 - accuracy: 0.9812 - val_loss: 0.2031 - val_accuracy: 0.9649 - 63ms/epoch - 7ms/step\n",
            "Epoch 70/100\n",
            "9/9 - 0s - loss: 0.1835 - accuracy: 0.9812 - val_loss: 0.2017 - val_accuracy: 0.9737 - 64ms/epoch - 7ms/step\n",
            "Epoch 71/100\n",
            "9/9 - 0s - loss: 0.1740 - accuracy: 0.9850 - val_loss: 0.1873 - val_accuracy: 0.9649 - 56ms/epoch - 6ms/step\n",
            "Epoch 72/100\n",
            "9/9 - 0s - loss: 0.1669 - accuracy: 0.9962 - val_loss: 0.1760 - val_accuracy: 0.9649 - 50ms/epoch - 6ms/step\n",
            "Epoch 73/100\n",
            "9/9 - 0s - loss: 0.1582 - accuracy: 0.9962 - val_loss: 0.1690 - val_accuracy: 0.9737 - 71ms/epoch - 8ms/step\n",
            "Epoch 74/100\n",
            "9/9 - 0s - loss: 0.1512 - accuracy: 0.9962 - val_loss: 0.1682 - val_accuracy: 0.9737 - 71ms/epoch - 8ms/step\n",
            "Epoch 75/100\n",
            "9/9 - 0s - loss: 0.1423 - accuracy: 0.9962 - val_loss: 0.1495 - val_accuracy: 0.9912 - 68ms/epoch - 8ms/step\n",
            "Epoch 76/100\n",
            "9/9 - 0s - loss: 0.1349 - accuracy: 0.9962 - val_loss: 0.1479 - val_accuracy: 0.9825 - 67ms/epoch - 7ms/step\n",
            "Epoch 77/100\n",
            "9/9 - 0s - loss: 0.1290 - accuracy: 0.9962 - val_loss: 0.1449 - val_accuracy: 0.9825 - 56ms/epoch - 6ms/step\n",
            "Epoch 78/100\n",
            "9/9 - 0s - loss: 0.1218 - accuracy: 0.9962 - val_loss: 0.1295 - val_accuracy: 0.9912 - 58ms/epoch - 6ms/step\n",
            "Epoch 79/100\n",
            "9/9 - 0s - loss: 0.1158 - accuracy: 0.9962 - val_loss: 0.1254 - val_accuracy: 0.9912 - 62ms/epoch - 7ms/step\n",
            "Epoch 80/100\n",
            "9/9 - 0s - loss: 0.1086 - accuracy: 0.9962 - val_loss: 0.1190 - val_accuracy: 0.9912 - 79ms/epoch - 9ms/step\n",
            "Epoch 81/100\n",
            "9/9 - 0s - loss: 0.1029 - accuracy: 0.9962 - val_loss: 0.1135 - val_accuracy: 0.9912 - 80ms/epoch - 9ms/step\n",
            "Epoch 82/100\n",
            "9/9 - 0s - loss: 0.0979 - accuracy: 0.9962 - val_loss: 0.1076 - val_accuracy: 0.9912 - 72ms/epoch - 8ms/step\n",
            "Epoch 83/100\n",
            "9/9 - 0s - loss: 0.0928 - accuracy: 0.9962 - val_loss: 0.1047 - val_accuracy: 0.9912 - 67ms/epoch - 7ms/step\n",
            "Epoch 84/100\n",
            "9/9 - 0s - loss: 0.0880 - accuracy: 0.9962 - val_loss: 0.0972 - val_accuracy: 0.9912 - 72ms/epoch - 8ms/step\n",
            "Epoch 85/100\n",
            "9/9 - 0s - loss: 0.0843 - accuracy: 1.0000 - val_loss: 0.0908 - val_accuracy: 1.0000 - 68ms/epoch - 8ms/step\n",
            "Epoch 86/100\n",
            "9/9 - 0s - loss: 0.0787 - accuracy: 1.0000 - val_loss: 0.0903 - val_accuracy: 0.9912 - 65ms/epoch - 7ms/step\n",
            "Epoch 87/100\n",
            "9/9 - 0s - loss: 0.0755 - accuracy: 1.0000 - val_loss: 0.0835 - val_accuracy: 1.0000 - 49ms/epoch - 5ms/step\n",
            "Epoch 88/100\n",
            "9/9 - 0s - loss: 0.0712 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 1.0000 - 63ms/epoch - 7ms/step\n",
            "Epoch 89/100\n",
            "9/9 - 0s - loss: 0.0683 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 1.0000 - 60ms/epoch - 7ms/step\n",
            "Epoch 90/100\n",
            "9/9 - 0s - loss: 0.0642 - accuracy: 1.0000 - val_loss: 0.0720 - val_accuracy: 1.0000 - 47ms/epoch - 5ms/step\n",
            "Epoch 91/100\n",
            "9/9 - 0s - loss: 0.0618 - accuracy: 1.0000 - val_loss: 0.0690 - val_accuracy: 1.0000 - 62ms/epoch - 7ms/step\n",
            "Epoch 92/100\n",
            "9/9 - 0s - loss: 0.0577 - accuracy: 1.0000 - val_loss: 0.0641 - val_accuracy: 1.0000 - 48ms/epoch - 5ms/step\n",
            "Epoch 93/100\n",
            "9/9 - 0s - loss: 0.0557 - accuracy: 1.0000 - val_loss: 0.0620 - val_accuracy: 1.0000 - 61ms/epoch - 7ms/step\n",
            "Epoch 94/100\n",
            "9/9 - 0s - loss: 0.0526 - accuracy: 1.0000 - val_loss: 0.0597 - val_accuracy: 1.0000 - 61ms/epoch - 7ms/step\n",
            "Epoch 95/100\n",
            "9/9 - 0s - loss: 0.0500 - accuracy: 1.0000 - val_loss: 0.0563 - val_accuracy: 1.0000 - 67ms/epoch - 7ms/step\n",
            "Epoch 96/100\n",
            "9/9 - 0s - loss: 0.0477 - accuracy: 1.0000 - val_loss: 0.0545 - val_accuracy: 1.0000 - 49ms/epoch - 5ms/step\n",
            "Epoch 97/100\n",
            "9/9 - 0s - loss: 0.0457 - accuracy: 1.0000 - val_loss: 0.0524 - val_accuracy: 1.0000 - 46ms/epoch - 5ms/step\n",
            "Epoch 98/100\n",
            "9/9 - 0s - loss: 0.0435 - accuracy: 1.0000 - val_loss: 0.0489 - val_accuracy: 1.0000 - 46ms/epoch - 5ms/step\n",
            "Epoch 99/100\n",
            "9/9 - 0s - loss: 0.0416 - accuracy: 1.0000 - val_loss: 0.0475 - val_accuracy: 1.0000 - 48ms/epoch - 5ms/step\n",
            "Epoch 100/100\n",
            "9/9 - 0s - loss: 0.0397 - accuracy: 1.0000 - val_loss: 0.0450 - val_accuracy: 1.0000 - 56ms/epoch - 6ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_history = pd.DataFrame(model_fit.history)\n",
        "model_history['epoch'] = model_fit.epoch\n",
        "model_history.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "E4PxOEfJv60I",
        "outputId": "70daf7c8-a27d-4442-ec28-eb688001a55a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        loss  accuracy  val_loss  val_accuracy  epoch\n",
              "95  0.047744       1.0  0.054492           1.0     95\n",
              "96  0.045697       1.0  0.052378           1.0     96\n",
              "97  0.043501       1.0  0.048897           1.0     97\n",
              "98  0.041648       1.0  0.047523           1.0     98\n",
              "99  0.039743       1.0  0.045023           1.0     99"
            ],
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              "      <th></th>\n",
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              "      <th>95</th>\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b185905e-2f4a-4166-acb5-74a8b6e74d26 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"model_history\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0031719993826488,\n        \"min\": 0.039742667227983475,\n        \"max\": 0.0477440282702446,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.045696962624788284,\n          0.039742667227983475,\n          0.043500687927007675\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"val_loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0037877143946150505,\n        \"min\": 0.045023079961538315,\n        \"max\": 0.05449231341481209,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.052377864718437195\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"val_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"epoch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 95,\n        \"max\": 99,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_fit(history):\n",
        "  hist = pd.DataFrame(history.history)\n",
        "  hist['epoch'] = history.epoch\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('loss')\n",
        "  plt.plot(hist['epoch'], hist['loss'],\n",
        "           label='Train Error')\n",
        "  plt.plot(hist['epoch'], hist['val_loss'],\n",
        "           label = 'Val Error')\n",
        "  plt.legend()\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('accuracy')\n",
        "  plt.plot(hist['epoch'], hist['accuracy'],\n",
        "           label='Train Error')\n",
        "  plt.plot(hist['epoch'], hist['val_accuracy'],label = 'Val Error')\n",
        "  #plt.ylim([-5,5])\n",
        "  plt.legend()\n",
        "\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "LgV5qfUIwj3c"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_fit(model_fit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "2GcM0S3jwxMU",
        "outputId": "325a22f3-6e6b-4b6b-a74a-9b88f6350f0b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the model\n",
        "test1 = model.evaluate(X_test, y_test, verbose=0)"
      ],
      "metadata": {
        "id": "fM4Uiok2w859"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#the loss value in test is:\n",
        "print('Test loss:', round(test1[0],3))\n",
        "print('Test accuracy:', round(test1[1],3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFRq5T3XxHiO",
        "outputId": "ab62917e-d626-4a43-d1f0-2845b24ba3c0"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.045\n",
            "Test accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HT='Brighton'\n",
        "AT='Liverpool'\n",
        "dato1=dato.loc[(dato['HomeTeam']==HT) & (dato['AwayTeam']==AT)]\n",
        "dato1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NGBoesNZxL_J",
        "outputId": "61278516-67ea-411d-8def-7e4e2762ce90"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 66)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dato1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "cn4CBkPWxRH_",
        "outputId": "c11d86ab-013b-4b2c-ca0b-3ad3315d4eb7"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Div        Date  HomeTeam   AwayTeam  FTHG  FTAG FTR  HTHG  HTAG HTR  ...  \\\n",
              "141  E0  02/12/2017  Brighton  Liverpool     1     5   A     0     2   A  ...   \n",
              "\n",
              "    BbAH  BbAHh  BbMxAHH  BbAvAHH  BbMxAHA  BbAvAHA  PSCH  PSCD  PSCA  \\\n",
              "141   20    1.0     2.13     2.09     1.84      1.8  7.25  4.39  1.53   \n",
              "\n",
              "     categorical FTR  \n",
              "141                0  \n",
              "\n",
              "[1 rows x 66 columns]"
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              "\n",
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              "type": "dataframe",
              "variable_name": "dato1"
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "#Separate into feature set and target variable\n",
        "#FTR = Full Time Result (H=Home Win, D=Draw, A=Away Win)\n",
        "X_all_2 = dato1[['FTHG','FTAG','HTHG','HTAG','HS','AS','HST','AST']]\n",
        "y_all_2 = dato1['categorical FTR']"
      ],
      "metadata": {
        "id": "h0nOFt2axXH6"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_all_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
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        "id": "lflPET5MxfXa",
        "outputId": "73568de0-7449-43b8-cec8-6ea8326aa87b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     FTHG  FTAG  HTHG  HTAG  HS  AS  HST  AST\n",
              "141     1     5     0     2   6  12    2    6"
            ],
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              "      <td>6</td>\n",
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              "                                                    [key], {});\n",
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              "variable_name": "X_all_2",
              "summary": "{\n  \"name\": \"X_all_2\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"FTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 6,\n        \"max\": 6,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 12,\n        \"max\": 12,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 6,\n        \"max\": 6,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Convierte numero [0,1,2] en [Away, Draw, Home]\n",
        "def actual_winner(yx):\n",
        "    if (int(yx)==0):\n",
        "      winner='Away'\n",
        "    elif (int(yx)==1):\n",
        "      winner='Draw'\n",
        "    else:\n",
        "      winner='Home'\n",
        "    return winner"
      ],
      "metadata": {
        "id": "-UlZ4ojWxlWp"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model.predict puede dar un número muy pequeño, que debemos convertir\n",
        "# en un numero \"manejable\"\n",
        "def convert_number(pred):\n",
        "  pred_=[]\n",
        "  for i in range(len(pred[0])):\n",
        "    pred_number=float(format(pred[0][i], '.3f'))\n",
        "    pred_.append(pred_number)\n",
        "\n",
        "  return (pred_)"
      ],
      "metadata": {
        "id": "tzX8V5TtxrUg"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transforma numero en carácter\n",
        "# Recibe un número [0,1,2] y dice si es [Away, Draw, Home]\n",
        "def predicted_winner(yx):\n",
        "  y=yx.index(max(yx))\n",
        "  if y==0:\n",
        "    pred_winner='Away'\n",
        "  elif y==1:\n",
        "    pred_winner='Draw'\n",
        "  elif y==2:\n",
        "    pred_winner='Home'\n",
        "  return pred_winner"
      ],
      "metadata": {
        "id": "Orn4WwCmxwq5"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resultado_actual=actual_winner(y_all_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKKDWDNNx1pN",
        "outputId": "22398d50-0bc2-4c1a-bc85-94967b41aeb6"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-39-90a987d4a770>:3: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
            "  if (int(yx)==0):\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_all_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GA0kr1Ecx5W2",
        "outputId": "7a328895-ca49-4d0b-9411-36fbc786d677"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "141    0\n",
              "Name: categorical FTR, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('El resultado actual es: ', int(y_all_2),' Es decir: ',resultado_actual)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2WAZVbFyMfY",
        "outputId": "2037787c-6907-4c46-f9a1-b173ae8f92f4"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El resultado actual es:  0  Es decir:  Away\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-9fdaecebdd7e>:1: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
            "  print('El resultado actual es: ', int(y_all_2),' Es decir: ',resultado_actual)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_allnorm = (X_all_2 - X_all.mean()) / X_all.std()\n",
        "X_allnorm.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "2YJZEbdqyPYr",
        "outputId": "35b1af1b-f6e8-4ad6-d432-dd48a5b05206"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         FTHG     FTAG      HTHG      HTAG        HS        AS      HST  \\\n",
              "141 -0.396675  3.27106 -0.766186  1.961914 -1.314503  0.235179 -0.96987   \n",
              "\n",
              "          AST  \n",
              "141  0.965337  "
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FTHG</th>\n",
              "      <th>FTAG</th>\n",
              "      <th>HTHG</th>\n",
              "      <th>HTAG</th>\n",
              "      <th>HS</th>\n",
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              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>-0.396675</td>\n",
              "      <td>3.27106</td>\n",
              "      <td>-0.766186</td>\n",
              "      <td>1.961914</td>\n",
              "      <td>-1.314503</td>\n",
              "      <td>0.235179</td>\n",
              "      <td>-0.96987</td>\n",
              "      <td>0.965337</td>\n",
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              "</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-aa08d4d4-63c5-442f-954c-5f132bfc54ae')\"\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\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-aa08d4d4-63c5-442f-954c-5f132bfc54ae 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-aa08d4d4-63c5-442f-954c-5f132bfc54ae');\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",
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              "\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_allnorm",
              "summary": "{\n  \"name\": \"X_allnorm\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"FTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -0.39667487050954486,\n        \"max\": -0.39667487050954486,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -0.39667487050954486\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3.2710601581390892,\n        \"max\": 3.2710601581390892,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.2710601581390892\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -0.7661861980371655,\n        \"max\": -0.7661861980371655,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -0.7661861980371655\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1.9619144561602995,\n        \"max\": 1.9619144561602995,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.9619144561602995\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -1.3145029098573977,\n        \"max\": -1.3145029098573977,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -1.3145029098573977\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.23517851030099016,\n        \"max\": 0.23517851030099016,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.23517851030099016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -0.9698704459431063,\n        \"max\": -0.9698704459431063,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -0.9698704459431063\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.9653372656885765,\n        \"max\": 0.9653372656885765,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9653372656885765\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred1= model.predict(X_allnorm)\n",
        "pred1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Udz6899iybYc",
        "outputId": "2e94af9f-9938-46c1-c637-62d60f99dca8"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 192ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0000000e+00, 3.2988185e-17, 1.4525912e-36]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred1_number=convert_number(pred1)\n",
        "pred1_number"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8w2isRXOydx5",
        "outputId": "dcd217af-81df-415a-fcb8-d8fecad8cf85"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0, 0.0, 0.0]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#PIE chart\n",
        "pieLabels = 'Away', 'Draw', 'Home'\n",
        "figureObject, axesObject = plt.subplots()\n",
        "#Draw the pie chart\n",
        "axesObject.pie(pred1_number, labels=pieLabels, autopct='%1.2f', startangle=90)\n",
        "# Aspect ratio - equal means pie is a circle\n",
        "axesObject.axis('equal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "ZPcYuoLfyh9M",
        "outputId": "f3cb42b7-c9dc-4e9b-98c8-403a2c455647"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_winner1=predicted_winner(pred1_number)\n",
        "pred_winner1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "4nJY2G2ay5dO",
        "outputId": "04136796-b16e-4abc-9287-198c3cbdc547"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Away'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('The Actual Winner is: ', resultado_actual)\n",
        "print('The Predicted Winner: ',pred_winner1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gfTgdqwyzEP2",
        "outputId": "355dc9c0-fed2-4ad1-9464-b609ede71cf6"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Actual Winner is:  Away\n",
            "The Predicted Winner:  Away\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url='https://www.football-data.co.uk/mmz4281/2324/E0.csv'\n",
        "wget.download(url, 'E02.csv')\n",
        "print('Datos Descargados!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CXKkGu6zHhq",
        "outputId": "217c585f-9aac-42da-c359-d13611c7fc7d"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Datos Descargados!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dato3=pd.read_csv('./E02.csv')"
      ],
      "metadata": {
        "id": "_4kIy0rszcoU"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dato3['categorical FTR'] = lb_make.fit_transform(dato3['FTR'])"
      ],
      "metadata": {
        "id": "6QytHVDezmIR"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dato3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "Z9gpk6bczqSL",
        "outputId": "45b6b7a3-a8cf-4cea-9481-40cecfb2847a"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Div        Date   Time     HomeTeam       AwayTeam  FTHG  FTAG FTR  HTHG  \\\n",
              "0  E0  11/08/2023  20:00      Burnley       Man City     0     3   A     0   \n",
              "1  E0  12/08/2023  12:30      Arsenal  Nott'm Forest     2     1   H     2   \n",
              "2  E0  12/08/2023  15:00  Bournemouth       West Ham     1     1   D     0   \n",
              "3  E0  12/08/2023  15:00     Brighton          Luton     4     1   H     1   \n",
              "4  E0  12/08/2023  15:00      Everton         Fulham     0     1   A     0   \n",
              "\n",
              "   HTAG  ...  AHCh B365CAHH  B365CAHA  PCAHH  PCAHA  MaxCAHH  MaxCAHA  \\\n",
              "0     2  ...  1.50     1.95      1.98   1.95   1.97      NaN      NaN   \n",
              "1     0  ... -2.00     1.95      1.98   1.93   1.97     2.01     2.09   \n",
              "2     0  ...  0.00     2.02      1.91   2.01   1.92     2.06     1.96   \n",
              "3     0  ... -1.75     2.01      1.92   2.00   1.91     2.14     1.93   \n",
              "4     0  ... -0.25     2.06      1.87   2.04   1.88     2.08     1.99   \n",
              "\n",
              "   AvgCAHH  AvgCAHA  categorical FTR  \n",
              "0     1.92     1.95                0  \n",
              "1     1.95     1.92                2  \n",
              "2     1.96     1.91                1  \n",
              "3     2.00     1.86                2  \n",
              "4     1.98     1.88                0  \n",
              "\n",
              "[5 rows x 107 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bf255cbf-3e6c-49a5-8e4e-e7fd7d3850a9\" 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>Div</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>HomeTeam</th>\n",
              "      <th>AwayTeam</th>\n",
              "      <th>FTHG</th>\n",
              "      <th>FTAG</th>\n",
              "      <th>FTR</th>\n",
              "      <th>HTHG</th>\n",
              "      <th>HTAG</th>\n",
              "      <th>...</th>\n",
              "      <th>AHCh</th>\n",
              "      <th>B365CAHH</th>\n",
              "      <th>B365CAHA</th>\n",
              "      <th>PCAHH</th>\n",
              "      <th>PCAHA</th>\n",
              "      <th>MaxCAHH</th>\n",
              "      <th>MaxCAHA</th>\n",
              "      <th>AvgCAHH</th>\n",
              "      <th>AvgCAHA</th>\n",
              "      <th>categorical FTR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>E0</td>\n",
              "      <td>11/08/2023</td>\n",
              "      <td>20:00</td>\n",
              "      <td>Burnley</td>\n",
              "      <td>Man City</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1.95</td>\n",
              "      <td>1.98</td>\n",
              "      <td>1.95</td>\n",
              "      <td>1.97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.92</td>\n",
              "      <td>1.95</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>E0</td>\n",
              "      <td>12/08/2023</td>\n",
              "      <td>12:30</td>\n",
              "      <td>Arsenal</td>\n",
              "      <td>Nott'm Forest</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>H</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>1.95</td>\n",
              "      <td>1.98</td>\n",
              "      <td>1.93</td>\n",
              "      <td>1.97</td>\n",
              "      <td>2.01</td>\n",
              "      <td>2.09</td>\n",
              "      <td>1.95</td>\n",
              "      <td>1.92</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>E0</td>\n",
              "      <td>12/08/2023</td>\n",
              "      <td>15:00</td>\n",
              "      <td>Bournemouth</td>\n",
              "      <td>West Ham</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>D</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.02</td>\n",
              "      <td>1.91</td>\n",
              "      <td>2.01</td>\n",
              "      <td>1.92</td>\n",
              "      <td>2.06</td>\n",
              "      <td>1.96</td>\n",
              "      <td>1.96</td>\n",
              "      <td>1.91</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>E0</td>\n",
              "      <td>12/08/2023</td>\n",
              "      <td>15:00</td>\n",
              "      <td>Brighton</td>\n",
              "      <td>Luton</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>H</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.75</td>\n",
              "      <td>2.01</td>\n",
              "      <td>1.92</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.91</td>\n",
              "      <td>2.14</td>\n",
              "      <td>1.93</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.86</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>E0</td>\n",
              "      <td>12/08/2023</td>\n",
              "      <td>15:00</td>\n",
              "      <td>Everton</td>\n",
              "      <td>Fulham</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>2.06</td>\n",
              "      <td>1.87</td>\n",
              "      <td>2.04</td>\n",
              "      <td>1.88</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.99</td>\n",
              "      <td>1.98</td>\n",
              "      <td>1.88</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 107 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf255cbf-3e6c-49a5-8e4e-e7fd7d3850a9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bf255cbf-3e6c-49a5-8e4e-e7fd7d3850a9 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-bf255cbf-3e6c-49a5-8e4e-e7fd7d3850a9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5daffd4f-2f5b-457d-a6a7-4a2d0739f0d0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5daffd4f-2f5b-457d-a6a7-4a2d0739f0d0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5daffd4f-2f5b-457d-a6a7-4a2d0739f0d0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dato3"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dato3.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwyyvxrkz8i3",
        "outputId": "af0edb50-5af5-43e5-cfa8-12ef7c8297aa"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(380, 107)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HT='Brighton'\n",
        "AT='Crystal Palace'\n",
        "dato4=dato3.loc[(dato3['HomeTeam']==HT) & (dato3['AwayTeam']==AT)]\n",
        "dato4.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0kNXBd0K0EFn",
        "outputId": "ca10baad-2d16-4c92-9946-2649741996f6"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 107)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dato4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "VDFaySZJ0L_m",
        "outputId": "ce7073ec-a621-43bc-e634-65f51a9c2a17"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Div        Date   Time  HomeTeam        AwayTeam  FTHG  FTAG FTR  HTHG  \\\n",
              "219  E0  03/02/2024  15:00  Brighton  Crystal Palace     4     1   H     3   \n",
              "\n",
              "     HTAG  ...  AHCh B365CAHH  B365CAHA  PCAHH  PCAHA  MaxCAHH  MaxCAHA  \\\n",
              "219     0  ... -0.75     1.95      1.98   1.95   1.97     1.97     1.99   \n",
              "\n",
              "     AvgCAHH  AvgCAHA  categorical FTR  \n",
              "219      1.9     1.96                2  \n",
              "\n",
              "[1 rows x 107 columns]"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Div</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>HomeTeam</th>\n",
              "      <th>AwayTeam</th>\n",
              "      <th>FTHG</th>\n",
              "      <th>FTAG</th>\n",
              "      <th>FTR</th>\n",
              "      <th>HTHG</th>\n",
              "      <th>HTAG</th>\n",
              "      <th>...</th>\n",
              "      <th>AHCh</th>\n",
              "      <th>B365CAHH</th>\n",
              "      <th>B365CAHA</th>\n",
              "      <th>PCAHH</th>\n",
              "      <th>PCAHA</th>\n",
              "      <th>MaxCAHH</th>\n",
              "      <th>MaxCAHA</th>\n",
              "      <th>AvgCAHH</th>\n",
              "      <th>AvgCAHA</th>\n",
              "      <th>categorical FTR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>E0</td>\n",
              "      <td>03/02/2024</td>\n",
              "      <td>15:00</td>\n",
              "      <td>Brighton</td>\n",
              "      <td>Crystal Palace</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>H</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>1.95</td>\n",
              "      <td>1.98</td>\n",
              "      <td>1.95</td>\n",
              "      <td>1.97</td>\n",
              "      <td>1.97</td>\n",
              "      <td>1.99</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1.96</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 107 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a33d66e-2f47-46d8-8275-26dd7d2edbd8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\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",
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              "\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-2a33d66e-2f47-46d8-8275-26dd7d2edbd8 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-2a33d66e-2f47-46d8-8275-26dd7d2edbd8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_16ebaa43-37cf-4a97-9731-5575cdf3150f\">\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('dato4')\"\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_16ebaa43-37cf-4a97-9731-5575cdf3150f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dato4');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dato4"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Separate into feature set and target variable\n",
        "#FTR = Full Time Result (H=Home Win, D=Draw, A=Away Win)\n",
        "X_all_3 = dato4[['FTHG','FTAG','HTHG','HTAG','HS','AS','HST','AST']]\n",
        "y_all_3 = dato4['categorical FTR']"
      ],
      "metadata": {
        "id": "PGrWEijM0Ru6"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_all_3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "JjIRsQ1J0YAU",
        "outputId": "9ef39a87-8e93-4ba2-d9e4-432b01eb6915"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     FTHG  FTAG  HTHG  HTAG  HS  AS  HST  AST\n",
              "219     4     1     3     0  13   7    6    5"
            ],
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FTHG</th>\n",
              "      <th>FTAG</th>\n",
              "      <th>HTHG</th>\n",
              "      <th>HTAG</th>\n",
              "      <th>HS</th>\n",
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              "      <th>AST</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2c3b75bf-7c0f-4d74-ad5d-e96236141270 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-2c3b75bf-7c0f-4d74-ad5d-e96236141270');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_b050272f-821c-49c3-875d-d33921cc82cd\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
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              "      }\n",
              "\n",
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              "        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_all_3')\"\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_b050272f-821c-49c3-875d-d33921cc82cd 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_all_3');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_all_3",
              "summary": "{\n  \"name\": \"X_all_3\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"FTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 4,\n        \"max\": 4,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 13,\n        \"max\": 13,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 7,\n        \"max\": 7,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 6,\n        \"max\": 6,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_all_3_resultado=actual_winner(y_all_3)\n",
        "print('El resultado actual es: ', int(y_all_3),' Es decir: ',y_all_3_resultado)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7G3NamW0fRI",
        "outputId": "c746c3b1-1627-461e-95d4-8c7561256731"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El resultado actual es:  2  Es decir:  Home\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-39-90a987d4a770>:3: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
            "  if (int(yx)==0):\n",
            "<ipython-input-39-90a987d4a770>:5: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
            "  elif (int(yx)==1):\n",
            "<ipython-input-61-daa9615c0bd4>:2: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
            "  print('El resultado actual es: ', int(y_all_3),' Es decir: ',y_all_3_resultado)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#normalizacion\n",
        "X_allnorm2 = (X_all_3 - X_all.mean()) / X_all.std()\n",
        "X_allnorm2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "vQXP33I80mAh",
        "outputId": "cf5a691e-137d-44b4-9939-4393d9592f6d"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         FTHG      FTAG      HTHG    HTAG        HS        AS       HST  \\\n",
              "219  1.841985 -0.125123  3.014992 -0.6818 -0.102097 -0.794405  0.505808   \n",
              "\n",
              "          AST  \n",
              "219  0.533266  "
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              "    .colab-df-convert {\n",
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              "    [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-7325c03c-614d-465b-bfc9-311c8537cfe6 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-7325c03c-614d-465b-bfc9-311c8537cfe6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_allnorm2",
              "summary": "{\n  \"name\": \"X_allnorm2\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"FTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1.8419852897918465,\n        \"max\": 1.8419852897918465,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.8419852897918465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -0.1251225197102384,\n        \"max\": -0.1251225197102384,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -0.1251225197102384\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3.0149924416267675,\n        \"max\": 3.0149924416267675,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0149924416267675\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -0.681800059233012,\n        \"max\": -0.681800059233012,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -0.681800059233012\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -0.10209731338698234,\n        \"max\": -0.10209731338698234,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -0.10209731338698234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -0.7944048297263863,\n        \"max\": -0.7944048297263863,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -0.7944048297263863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.5058083106470053,\n        \"max\": 0.5058083106470053,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.5058083106470053\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.5332664047207802,\n        \"max\": 0.5332664047207802,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.5332664047207802\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the target\n",
        "pred2= model.predict(X_allnorm2)\n",
        "pred2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hyckUpe00rXe",
        "outputId": "3e266efe-70a0-453e-e498-b6c9c2aa751e"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 20ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.3882615e-14, 7.5895741e-11, 1.0000000e+00]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred2_number=convert_number(pred2)\n",
        "pred2_number"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aUUMSQfn00PS",
        "outputId": "527b0467-0e49-4993-8a7e-257c1b57aea4"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0, 0.0, 1.0]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#PIE chart\n",
        "pieLabels = 'Away', 'Draw', 'Home'\n",
        "figureObject, axesObject = plt.subplots()\n",
        "#Draw the pie chart\n",
        "axesObject.pie(pred2_number, labels=pieLabels, autopct='%1.2f', startangle=90)\n",
        "# Aspect ratio - equal means pie is a circle\n",
        "axesObject.axis('equal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "J7VgcFwA05hR",
        "outputId": "8a706c91-4fac-4385-e0b5-e80223d852c6"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_winner2=predicted_winner(pred2_number)\n",
        "pred_winner2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KFiW10mM0-so",
        "outputId": "7a319b46-a731-495e-afa5-2518defd6573"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Home'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('The Actual Winner is: ', y_all_3_resultado)\n",
        "print('The Predicted Winner: ', pred_winner2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPQeacWf1EIm",
        "outputId": "3555514b-fd95-4927-b207-443195b1babb"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Actual Winner is:  Home\n",
            "The Predicted Winner:  Home\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_all_4 = dato3[['FTHG','FTAG','HTHG','HTAG','HS','AS','HST','AST']]\n",
        "y_all_4 = dato3['categorical FTR']"
      ],
      "metadata": {
        "id": "IDIVWDZa1IRf"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_all_4[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6U_Haqd21M8L",
        "outputId": "89a1d69b-62c9-4f23-b2d4-1e5bec6e9aea"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    2\n",
              "2    1\n",
              "3    2\n",
              "4    0\n",
              "Name: categorical FTR, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_all_4_actual=[]\n",
        "for i in range(len(y_all_4)):\n",
        "  y=actual_winner(y_all_4[i])\n",
        "  y_all_4_actual.append(y)\n",
        "\n",
        "y_all_4_actual[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8h2A2sWw1SZF",
        "outputId": "e782f911-6edd-433e-ae16-22993add4ffc"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Away', 'Home', 'Draw', 'Home', 'Away']"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_all_4_data=[]\n",
        "for i in range(len(y_all_4)):\n",
        "  y=actual_winner(y_all_4[i])\n",
        "  y_all_4_data.append(y)"
      ],
      "metadata": {
        "id": "mSHOdg2O1YIz"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#normalizacion\n",
        "X_allnorm3 = (X_all_4 - X_all.mean()) / X_all.std()\n",
        "X_allnorm3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OZ9mR_Nx1YKI",
        "outputId": "7bc8fda5-8a9f-44bb-f04e-8035b560455a"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       FTHG      FTAG      HTHG      HTAG        HS        AS       HST  \\\n",
              "0 -1.142895  1.572969 -0.766186  1.961914 -1.314503  1.264762 -1.338790   \n",
              "1  0.349545 -0.125123  1.754600 -0.681800  0.244304 -1.000321  0.874728   \n",
              "2 -0.396675 -0.125123 -0.766186 -0.681800  0.071103  1.058845  0.136889   \n",
              "3  1.841985 -0.125123  0.494207 -0.681800  2.322714 -0.382571  2.719326   \n",
              "4 -1.142895 -0.125123 -0.766186 -0.681800  0.937107 -0.382571  1.612567   \n",
              "\n",
              "        AST  \n",
              "0  1.829479  \n",
              "1 -0.762946  \n",
              "2 -0.330875  \n",
              "3 -0.330875  \n",
              "4 -0.762946  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0b96bda3-044c-4f1a-bf3f-e986c8d18557\" 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>FTHG</th>\n",
              "      <th>FTAG</th>\n",
              "      <th>HTHG</th>\n",
              "      <th>HTAG</th>\n",
              "      <th>HS</th>\n",
              "      <th>AS</th>\n",
              "      <th>HST</th>\n",
              "      <th>AST</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.142895</td>\n",
              "      <td>1.572969</td>\n",
              "      <td>-0.766186</td>\n",
              "      <td>1.961914</td>\n",
              "      <td>-1.314503</td>\n",
              "      <td>1.264762</td>\n",
              "      <td>-1.338790</td>\n",
              "      <td>1.829479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.349545</td>\n",
              "      <td>-0.125123</td>\n",
              "      <td>1.754600</td>\n",
              "      <td>-0.681800</td>\n",
              "      <td>0.244304</td>\n",
              "      <td>-1.000321</td>\n",
              "      <td>0.874728</td>\n",
              "      <td>-0.762946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.396675</td>\n",
              "      <td>-0.125123</td>\n",
              "      <td>-0.766186</td>\n",
              "      <td>-0.681800</td>\n",
              "      <td>0.071103</td>\n",
              "      <td>1.058845</td>\n",
              "      <td>0.136889</td>\n",
              "      <td>-0.330875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.841985</td>\n",
              "      <td>-0.125123</td>\n",
              "      <td>0.494207</td>\n",
              "      <td>-0.681800</td>\n",
              "      <td>2.322714</td>\n",
              "      <td>-0.382571</td>\n",
              "      <td>2.719326</td>\n",
              "      <td>-0.330875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.142895</td>\n",
              "      <td>-0.125123</td>\n",
              "      <td>-0.766186</td>\n",
              "      <td>-0.681800</td>\n",
              "      <td>0.937107</td>\n",
              "      <td>-0.382571</td>\n",
              "      <td>1.612567</td>\n",
              "      <td>-0.762946</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-0b96bda3-044c-4f1a-bf3f-e986c8d18557')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0b96bda3-044c-4f1a-bf3f-e986c8d18557 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-0b96bda3-044c-4f1a-bf3f-e986c8d18557');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-009a2f5a-885e-48df-a394-0191e6ef65c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-009a2f5a-885e-48df-a394-0191e6ef65c2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-009a2f5a-885e-48df-a394-0191e6ef65c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_allnorm3",
              "summary": "{\n  \"name\": \"X_allnorm3\",\n  \"rows\": 380,\n  \"fields\": [\n    {\n      \"column\": \"FTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.019050206305499,\n        \"min\": -1.142894923943342,\n        \"max\": 3.3344253966594404,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -1.142894923943342,\n          0.34954518292425224,\n          1.0957652363580492\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0849199119347648,\n        \"min\": -0.9741681891725703,\n        \"max\": 5.818197166526086,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.1251225197102384,\n          3.2710601581390892,\n          1.5729688192144256\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTHG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0482518240002332,\n        \"min\": -0.7661861980371655,\n        \"max\": 4.2753853215147455,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.7545995617387902,\n          4.2753853215147455,\n          0.49420668185081235\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HTAG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0883739687067664,\n        \"min\": -0.681800059233012,\n        \"max\": 5.927486229250267,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.9619144561602995,\n          -0.681800059233012,\n          5.927486229250267\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.084576301641789,\n        \"min\": -2.1805069073362655,\n        \"max\": 3.881521075015811,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          -0.7949005113700768,\n          2.495914679049622,\n          3.535119476024264\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1258897878841911,\n        \"min\": -1.8239881697537628,\n        \"max\": 3.9416785343995455,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2.294345190355743,\n          3.3239285303831196,\n          -0.7944048297263863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0644989434839867,\n        \"min\": -1.707709824238162,\n        \"max\": 3.8260855129747564,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          -1.3387901350906342,\n          0.8747279997945331,\n          3.088246134679701\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0916241874655381,\n        \"min\": -1.627087900118201,\n        \"max\": 4.853975014398743,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          4.853975014398743,\n          -1.627087900118201,\n          1.8294789876241693\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the target\n",
        "pred3= model.predict(X_allnorm3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMvdXCfK1mrr",
        "outputId": "596a1059-ed8b-4d38-a7ba-404f35ac37f7"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 0s 6ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred3_data=[]\n",
        "pred3_size=len(pred3)\n",
        "for i in range(pred3_size):\n",
        "  y=convert_number([pred3[i]])\n",
        "  pred3_data.append(y)\n",
        "\n",
        "pred3_data[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aH3u68yI1tfs",
        "outputId": "2256c675-1d1c-4f8a-ca0c-4ee1adf9bd3b"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[1.0, 0.0, 0.0],\n",
              " [0.0, 0.001, 0.999],\n",
              " [0.997, 0.003, 0.0],\n",
              " [0.0, 0.0, 1.0],\n",
              " [1.0, 0.0, 0.0]]"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_winner3=[]\n",
        "for i in range(len(pred3_data)):\n",
        "  y=predicted_winner(pred3_data[i])\n",
        "  pred_winner3.append(y)\n",
        "\n",
        "pred_winner3[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unlQEzU51y6E",
        "outputId": "2491ab58-34ac-4d92-cb92-c045d561c040"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Away', 'Home', 'Away', 'Home', 'Away']"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "actual_count = pd.Series(y_all_4_actual).value_counts()\n",
        "actual_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VycEM4Sq2HJe",
        "outputId": "de6d56bc-81b0-444a-9fa1-af85710112b7"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Home    175\n",
              "Away    123\n",
              "Draw     82\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_count = pd.Series(pred_winner3).value_counts()\n",
        "pred_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMKuNKKC2QDR",
        "outputId": "6a3ed32a-8e2e-479d-9580-8a1c8706d967"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Away    202\n",
              "Home    157\n",
              "Draw     21\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list_common = []\n",
        "for a, b in zip(y_all_4_actual, pred_winner3):\n",
        "    if a != b:\n",
        "        list_common.append([a,b])"
      ],
      "metadata": {
        "id": "ZisHNRXK2W8i"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(list_common)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gcq-bzVY2dZk",
        "outputId": "b484b96b-52bc-4d89-a450-a28ca888b32c"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "97"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist([y_all_4_actual, pred_winner3], label=['Actual', 'Predicho'])\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ZTM7d4d02jZT",
        "outputId": "6312d863-55a6-47da-c0e4-6e52975c985f"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bOLy8zzw2sg6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}