ModelsTrainingAndTest.ipynb

{
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      "cell_type": "code",
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      "metadata": {
        "id": "ByecLrm-rgUk"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import math\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "\n",
        "from sklearn import metrics\n",
        "\n",
        "from scipy.special import legendre\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jilBiTEArgUm"
      },
      "source": [
        "### Reading CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "3xRzNJT8rgUo"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('all_matches_final.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        "id": "qTs-KHC_rgUp",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>draw_size</th>\n",
              "      <th>tourney_date</th>\n",
              "      <th>match_num</th>\n",
              "      <th>Player1_ht</th>\n",
              "      <th>Player1_age</th>\n",
              "      <th>best_of</th>\n",
              "      <th>minutes</th>\n",
              "      <th>Player2_id</th>\n",
              "      <th>Player2_seed</th>\n",
              "      <th>Player2_ht</th>\n",
              "      <th>...</th>\n",
              "      <th>l_1stWon</th>\n",
              "      <th>l_2ndWon</th>\n",
              "      <th>l_SvGms</th>\n",
              "      <th>l_bpSaved</th>\n",
              "      <th>l_bpFaced</th>\n",
              "      <th>Player2_rank</th>\n",
              "      <th>Player2_rank_points</th>\n",
              "      <th>Player1_rank</th>\n",
              "      <th>Player1_rank_points</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>99360.000000</td>\n",
              "      <td>9.936000e+04</td>\n",
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              "      <td>96686.000000</td>\n",
              "      <td>99360.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.007568</td>\n",
              "      <td>2.004922e+07</td>\n",
              "      <td>63.579006</td>\n",
              "      <td>185.447762</td>\n",
              "      <td>25.844574</td>\n",
              "      <td>3.444243</td>\n",
              "      <td>103.130019</td>\n",
              "      <td>105058.821488</td>\n",
              "      <td>7.300671</td>\n",
              "      <td>185.322219</td>\n",
              "      <td>...</td>\n",
              "      <td>31.827103</td>\n",
              "      <td>15.002626</td>\n",
              "      <td>12.188710</td>\n",
              "      <td>4.813434</td>\n",
              "      <td>8.750768</td>\n",
              "      <td>100.975800</td>\n",
              "      <td>1041.937369</td>\n",
              "      <td>99.262647</td>\n",
              "      <td>1253.805670</td>\n",
              "      <td>0.500000</td>\n",
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              "      <th>std</th>\n",
              "      <td>38.242706</td>\n",
              "      <td>8.848977e+04</td>\n",
              "      <td>107.630448</td>\n",
              "      <td>7.044066</td>\n",
              "      <td>3.805208</td>\n",
              "      <td>0.831349</td>\n",
              "      <td>39.211800</td>\n",
              "      <td>11028.044887</td>\n",
              "      <td>6.260864</td>\n",
              "      <td>6.753921</td>\n",
              "      <td>...</td>\n",
              "      <td>14.449350</td>\n",
              "      <td>7.228977</td>\n",
              "      <td>4.127567</td>\n",
              "      <td>3.274505</td>\n",
              "      <td>4.130267</td>\n",
              "      <td>165.065907</td>\n",
              "      <td>1077.558313</td>\n",
              "      <td>155.127915</td>\n",
              "      <td>1734.731502</td>\n",
              "      <td>0.500003</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.990123e+07</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>14.757016</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100284.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>1.997062e+07</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>23.039014</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>102087.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>458.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>439.250000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>2.004102e+07</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>25.612594</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>103428.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>185.000000</td>\n",
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              "      <td>29.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>11.000000</td>\n",
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              "      <td>748.000000</td>\n",
              "      <td>0.500000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>2.012092e+07</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>28.410678</td>\n",
              "      <td>3.000000</td>\n",
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              "      <td>104660.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>1230.000000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>1310.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>2.021120e+07</td>\n",
              "      <td>1701.000000</td>\n",
              "      <td>211.000000</td>\n",
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              "      <td>35.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>284.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>28.000000</td>\n",
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              "      <td>1.000000</td>\n",
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              "<p>8 rows × 36 columns</p>\n",
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            "text/plain": [
              "          draw_size  tourney_date     match_num    Player1_ht   Player1_age  \\\n",
              "count  99360.000000  9.936000e+04  99360.000000  94738.000000  99247.000000   \n",
              "mean      53.007568  2.004922e+07     63.579006    185.447762     25.844574   \n",
              "std       38.242706  8.848977e+04    107.630448      7.044066      3.805208   \n",
              "min        2.000000  1.990123e+07      1.000000      1.700000     14.757016   \n",
              "25%       32.000000  1.997062e+07      9.000000    180.000000     23.039014   \n",
              "50%       32.000000  2.004102e+07     23.000000    185.000000     25.612594   \n",
              "75%       64.000000  2.012092e+07     53.000000    190.000000     28.410678   \n",
              "max      128.000000  2.021120e+07   1701.000000    211.000000     43.655031   \n",
              "\n",
              "            best_of       minutes     Player2_id  Player2_seed    Player2_ht  \\\n",
              "count  99360.000000  86872.000000   99360.000000  30545.000000  93731.000000   \n",
              "mean       3.444243    103.130019  105058.821488      7.300671    185.322219   \n",
              "std        0.831349     39.211800   11028.044887      6.260864      6.753921   \n",
              "min        3.000000      0.000000  100284.000000      1.000000      1.700000   \n",
              "25%        3.000000     75.000000  102087.000000      3.000000    180.000000   \n",
              "50%        3.000000     96.000000  103428.000000      6.000000    185.000000   \n",
              "75%        3.000000    125.000000  104660.000000      9.000000    190.000000   \n",
              "max        5.000000   1146.000000  210274.000000     35.000000    211.000000   \n",
              "\n",
              "       ...      l_1stWon      l_2ndWon       l_SvGms     l_bpSaved  \\\n",
              "count  ...  89475.000000  89475.000000  89476.000000  89475.000000   \n",
              "mean   ...     31.827103     15.002626     12.188710      4.813434   \n",
              "std    ...     14.449350      7.228977      4.127567      3.274505   \n",
              "min    ...      0.000000      0.000000      0.000000     -6.000000   \n",
              "25%    ...     22.000000     10.000000      9.000000      2.000000   \n",
              "50%    ...     29.000000     14.000000     11.000000      4.000000   \n",
              "75%    ...     39.000000     19.000000     15.000000      7.000000   \n",
              "max    ...    284.000000    101.000000     91.000000     28.000000   \n",
              "\n",
              "          l_bpFaced  Player2_rank  Player2_rank_points  Player1_rank  \\\n",
              "count  89475.000000  97685.000000         96693.000000  97675.000000   \n",
              "mean       8.750768    100.975800          1041.937369     99.262647   \n",
              "std        4.130267    165.065907          1077.558313    155.127915   \n",
              "min        0.000000      1.000000             1.000000      1.000000   \n",
              "25%        6.000000     27.000000           458.000000     26.000000   \n",
              "50%        8.000000     59.000000           745.000000     58.000000   \n",
              "75%       11.000000    103.000000          1230.000000    105.000000   \n",
              "max       38.000000   2159.000000         16950.000000   2101.000000   \n",
              "\n",
              "       Player1_rank_points             y  \n",
              "count         96686.000000  99360.000000  \n",
              "mean           1253.805670      0.500000  \n",
              "std            1734.731502      0.500003  \n",
              "min               1.000000      0.000000  \n",
              "25%             439.250000      0.000000  \n",
              "50%             748.000000      0.500000  \n",
              "75%            1310.000000      1.000000  \n",
              "max           16950.000000      1.000000  \n",
              "\n",
              "[8 rows x 36 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 99360 entries, 0 to 99359\n",
            "Data columns (total 50 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   tourney_id           99360 non-null  object \n",
            " 1   tourney_name         99360 non-null  object \n",
            " 2   surface              99360 non-null  object \n",
            " 3   draw_size            99360 non-null  int64  \n",
            " 4   tourney_level        99360 non-null  object \n",
            " 5   tourney_date         99360 non-null  int64  \n",
            " 6   match_num            99360 non-null  int64  \n",
            " 7   Player1_entry        15877 non-null  object \n",
            " 8   Player1_name         99360 non-null  object \n",
            " 9   Player1_hand         99351 non-null  object \n",
            " 10  Player1_ht           94738 non-null  float64\n",
            " 11  Player1_ioc          99360 non-null  object \n",
            " 12  Player1_age          99247 non-null  float64\n",
            " 13  score                99360 non-null  object \n",
            " 14  best_of              99360 non-null  int64  \n",
            " 15  round                99360 non-null  object \n",
            " 16  minutes              86872 non-null  float64\n",
            " 17  Player2_id           99360 non-null  int64  \n",
            " 18  Player2_seed         30545 non-null  float64\n",
            " 19  Player2_entry        15968 non-null  object \n",
            " 20  Player2_name         99360 non-null  object \n",
            " 21  Player2_hand         99319 non-null  object \n",
            " 22  Player2_ht           93731 non-null  float64\n",
            " 23  Player2_ioc          99360 non-null  object \n",
            " 24  Player2_age          99312 non-null  float64\n",
            " 25  Player1_id           99360 non-null  int64  \n",
            " 26  Player1_seed         31602 non-null  float64\n",
            " 27  w_ace                89475 non-null  float64\n",
            " 28  w_df                 89475 non-null  float64\n",
            " 29  w_svpt               89475 non-null  float64\n",
            " 30  w_1stIn              89475 non-null  float64\n",
            " 31  w_1stWon             89475 non-null  float64\n",
            " 32  w_2ndWon             89475 non-null  float64\n",
            " 33  w_SvGms              89476 non-null  float64\n",
            " 34  w_bpSaved            89475 non-null  float64\n",
            " 35  w_bpFaced            89475 non-null  float64\n",
            " 36  l_ace                89475 non-null  float64\n",
            " 37  l_df                 89475 non-null  float64\n",
            " 38  l_svpt               89475 non-null  float64\n",
            " 39  l_1stIn              89475 non-null  float64\n",
            " 40  l_1stWon             89475 non-null  float64\n",
            " 41  l_2ndWon             89475 non-null  float64\n",
            " 42  l_SvGms              89476 non-null  float64\n",
            " 43  l_bpSaved            89475 non-null  float64\n",
            " 44  l_bpFaced            89475 non-null  float64\n",
            " 45  Player2_rank         97685 non-null  float64\n",
            " 46  Player2_rank_points  96693 non-null  float64\n",
            " 47  Player1_rank         97675 non-null  float64\n",
            " 48  Player1_rank_points  96686 non-null  float64\n",
            " 49  y                    99360 non-null  int64  \n",
            "dtypes: float64(29), int64(7), object(14)\n",
            "memory usage: 37.9+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5aHffEUtrgUq"
      },
      "source": [
        "### Separating  Features\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "uUidbXPzrgUr"
      },
      "outputs": [],
      "source": [
        "players = ['Player1_id','Player1_name','Player2_id','Player2_name']\n",
        "\n",
        "to_drop = ['tourney_id','tourney_date','score']\n",
        "\n",
        "cat_cols = ['tourney_name','surface','draw_size','tourney_level','Player1_entry','Player1_hand','Player1_ioc',\n",
        "            'Player2_entry','Player2_hand','Player2_ioc','best_of','round']\n",
        "\n",
        "\n",
        "num_cols = ['match_num','Player1_seed','Player1_ht','Player1_age','Player1_rank','Player1_rank_points',\n",
        "            'Player2_seed','Player2_ht','Player2_age','Player2_rank','Player2_rank_points','minutes','w_ace','w_df',\n",
        "            'w_svpt','w_1stIn','w_1stWon','w_2ndWon','w_SvGms','w_bpSaved','w_bpFaced','l_ace','l_df','l_svpt','l_1stIn',\n",
        "            'l_1stWon','l_2ndWon','l_SvGms','l_bpSaved','l_bpFaced']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEZdxi1ArgUr"
      },
      "source": [
        "### Dropping Irrelevant Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "O64q-VnHrgUr"
      },
      "outputs": [],
      "source": [
        "df = df.drop(to_drop, axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VLuFShEYrgUs"
      },
      "source": [
        "### Replacing Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "NqYZf4nhrgUs"
      },
      "outputs": [],
      "source": [
        "for i in cat_cols:\n",
        "    df[i] = df[i].replace(np.NaN, df[i].mode()[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_4bjsiLrgUs",
        "outputId": "34be509a-6fad-41b3-9d18-292bcf328a8b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "match_num\n",
            "Player1_seed\n",
            "Player1_ht\n",
            "Player1_age\n",
            "Player1_rank\n",
            "Player1_rank_points\n",
            "Player2_seed\n",
            "Player2_ht\n",
            "Player2_age\n",
            "Player2_rank\n",
            "Player2_rank_points\n",
            "minutes\n",
            "w_ace\n",
            "w_df\n",
            "w_svpt\n",
            "w_1stIn\n",
            "w_1stWon\n",
            "w_2ndWon\n",
            "w_SvGms\n",
            "w_bpSaved\n",
            "w_bpFaced\n",
            "l_ace\n",
            "l_df\n",
            "l_svpt\n",
            "l_1stIn\n",
            "l_1stWon\n",
            "l_2ndWon\n",
            "l_SvGms\n",
            "l_bpSaved\n",
            "l_bpFaced\n"
          ]
        }
      ],
      "source": [
        "for i in num_cols:\n",
        "    print(i)\n",
        "    df[i] = df[i].replace(np.NaN,df[i].mean())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ak_W0Ru7rgUt"
      },
      "source": [
        "### convert categorical data to numeric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "loEsx7tqrgUt"
      },
      "outputs": [],
      "source": [
        "def convertCatToNum(dff):\n",
        "    dff_new = pd.get_dummies(dff, columns=cat_cols) \n",
        "    return dff_new"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "s59z-1RzrgUt",
        "outputId": "eda1cd9b-2b6f-41b0-9237-9be38e5a98d9"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>match_num</th>\n",
              "      <th>Player1_name</th>\n",
              "      <th>Player1_ht</th>\n",
              "      <th>Player1_age</th>\n",
              "      <th>minutes</th>\n",
              "      <th>Player2_id</th>\n",
              "      <th>Player2_seed</th>\n",
              "      <th>Player2_name</th>\n",
              "      <th>Player2_ht</th>\n",
              "      <th>Player2_age</th>\n",
              "      <th>...</th>\n",
              "      <th>round_BR</th>\n",
              "      <th>round_ER</th>\n",
              "      <th>round_F</th>\n",
              "      <th>round_QF</th>\n",
              "      <th>round_R128</th>\n",
              "      <th>round_R16</th>\n",
              "      <th>round_R32</th>\n",
              "      <th>round_R64</th>\n",
              "      <th>round_RR</th>\n",
              "      <th>round_SF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>270</td>\n",
              "      <td>Novak Djokovic</td>\n",
              "      <td>188.0</td>\n",
              "      <td>31.438741</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>105311</td>\n",
              "      <td>7.300671</td>\n",
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              "      <td>Bohdan Ulihrach</td>\n",
              "      <td>188.0</td>\n",
              "      <td>29.670089</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>103454</td>\n",
              "      <td>7.000000</td>\n",
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              "      <td>Albert Ramos</td>\n",
              "      <td>188.0</td>\n",
              "      <td>28.750171</td>\n",
              "      <td>171.000000</td>\n",
              "      <td>105539</td>\n",
              "      <td>7.300671</td>\n",
              "      <td>Evgeny Donskoy</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>26.442163</td>\n",
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              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Renzo Furlan</td>\n",
              "      <td>175.0</td>\n",
              "      <td>25.722108</td>\n",
              "      <td>103.130019</td>\n",
              "      <td>102338</td>\n",
              "      <td>7.300671</td>\n",
              "      <td>Yevgeny Kafelnikov</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>21.963039</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>Mike Scheidweiler</td>\n",
              "      <td>188.0</td>\n",
              "      <td>20.837782</td>\n",
              "      <td>103.130019</td>\n",
              "      <td>104051</td>\n",
              "      <td>7.300671</td>\n",
              "      <td>Andrej Kracman</td>\n",
              "      <td>185.322219</td>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "   match_num       Player1_name  Player1_ht  Player1_age     minutes  \\\n",
              "0        270     Novak Djokovic       188.0    31.438741   93.000000   \n",
              "1         25    Bohdan Ulihrach       188.0    29.670089   87.000000   \n",
              "2        286       Albert Ramos       188.0    28.750171  171.000000   \n",
              "3          2       Renzo Furlan       175.0    25.722108  103.130019   \n",
              "4          5  Mike Scheidweiler       188.0    20.837782  103.130019   \n",
              "\n",
              "   Player2_id  Player2_seed        Player2_name  Player2_ht  Player2_age  ...  \\\n",
              "0      105311      7.300671          Joao Sousa  185.000000    29.582478  ...   \n",
              "1      103454      7.000000       Nicolas Massu  183.000000    25.043121  ...   \n",
              "2      105539      7.300671      Evgeny Donskoy  185.000000    26.442163  ...   \n",
              "3      102338      7.300671  Yevgeny Kafelnikov  190.000000    21.963039  ...   \n",
              "4      104051      7.300671      Andrej Kracman  185.322219    20.068446  ...   \n",
              "\n",
              "   round_BR  round_ER  round_F  round_QF  round_R128  round_R16  round_R32  \\\n",
              "0         0         0        0         0           0          0          1   \n",
              "1         0         0        0         1           0          0          0   \n",
              "2         0         0        0         0           0          1          0   \n",
              "3         0         0        0         0           0          0          0   \n",
              "4         0         0        0         0           0          0          0   \n",
              "\n",
              "   round_R64  round_RR  round_SF  \n",
              "0          0         0         0  \n",
              "1          0         0         0  \n",
              "2          0         0         0  \n",
              "3          0         1         0  \n",
              "4          0         1         0  \n",
              "\n",
              "[5 rows x 2774 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = convertCatToNum(df)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DpqlJusgrgUu"
      },
      "source": [
        "### Normalize numeric features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "019s0oeKrgUu",
        "outputId": "c0cc3332-9ad5-438a-abad-1b4fee929dca"
      },
      "outputs": [
        {
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              "      <th>0</th>\n",
              "      <td>0.158235</td>\n",
              "      <td>Novak Djokovic</td>\n",
              "      <td>0.890110</td>\n",
              "      <td>0.577262</td>\n",
              "      <td>0.081152</td>\n",
              "      <td>105311</td>\n",
              "      <td>0.185314</td>\n",
              "      <td>Joao Sousa</td>\n",
              "      <td>0.875776</td>\n",
              "      <td>0.480688</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.014118</td>\n",
              "      <td>Bohdan Ulihrach</td>\n",
              "      <td>0.890110</td>\n",
              "      <td>0.516059</td>\n",
              "      <td>0.075916</td>\n",
              "      <td>103454</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>Nicolas Massu</td>\n",
              "      <td>0.866221</td>\n",
              "      <td>0.337423</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.167647</td>\n",
              "      <td>Albert Ramos</td>\n",
              "      <td>0.890110</td>\n",
              "      <td>0.484225</td>\n",
              "      <td>0.149215</td>\n",
              "      <td>105539</td>\n",
              "      <td>0.185314</td>\n",
              "      <td>Evgeny Donskoy</td>\n",
              "      <td>0.875776</td>\n",
              "      <td>0.381578</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000588</td>\n",
              "      <td>Renzo Furlan</td>\n",
              "      <td>0.827998</td>\n",
              "      <td>0.379441</td>\n",
              "      <td>0.089991</td>\n",
              "      <td>102338</td>\n",
              "      <td>0.185314</td>\n",
              "      <td>Yevgeny Kafelnikov</td>\n",
              "      <td>0.899666</td>\n",
              "      <td>0.240214</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.002353</td>\n",
              "      <td>Mike Scheidweiler</td>\n",
              "      <td>0.890110</td>\n",
              "      <td>0.210422</td>\n",
              "      <td>0.089991</td>\n",
              "      <td>104051</td>\n",
              "      <td>0.185314</td>\n",
              "      <td>Andrej Kracman</td>\n",
              "      <td>0.877316</td>\n",
              "      <td>0.180420</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2774 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75b0952e-fa0b-4b56-99b3-da6ee6c8209a')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-75b0952e-fa0b-4b56-99b3-da6ee6c8209a 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-75b0952e-fa0b-4b56-99b3-da6ee6c8209a');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   match_num       Player1_name  Player1_ht  Player1_age   minutes  \\\n",
              "0   0.158235     Novak Djokovic    0.890110     0.577262  0.081152   \n",
              "1   0.014118    Bohdan Ulihrach    0.890110     0.516059  0.075916   \n",
              "2   0.167647       Albert Ramos    0.890110     0.484225  0.149215   \n",
              "3   0.000588       Renzo Furlan    0.827998     0.379441  0.089991   \n",
              "4   0.002353  Mike Scheidweiler    0.890110     0.210422  0.089991   \n",
              "\n",
              "   Player2_id  Player2_seed        Player2_name  Player2_ht  Player2_age  ...  \\\n",
              "0      105311      0.185314          Joao Sousa    0.875776     0.480688  ...   \n",
              "1      103454      0.176471       Nicolas Massu    0.866221     0.337423  ...   \n",
              "2      105539      0.185314      Evgeny Donskoy    0.875776     0.381578  ...   \n",
              "3      102338      0.185314  Yevgeny Kafelnikov    0.899666     0.240214  ...   \n",
              "4      104051      0.185314      Andrej Kracman    0.877316     0.180420  ...   \n",
              "\n",
              "   round_BR  round_ER  round_F  round_QF  round_R128  round_R16  round_R32  \\\n",
              "0         0         0        0         0           0          0          1   \n",
              "1         0         0        0         1           0          0          0   \n",
              "2         0         0        0         0           0          1          0   \n",
              "3         0         0        0         0           0          0          0   \n",
              "4         0         0        0         0           0          0          0   \n",
              "\n",
              "   round_R64  round_RR  round_SF  \n",
              "0          0         0         0  \n",
              "1          0         0         0  \n",
              "2          0         0         0  \n",
              "3          0         1         0  \n",
              "4          0         1         0  \n",
              "\n",
              "[5 rows x 2774 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def normalize(dff,col_name_list):\n",
        "    result = dff.copy()\n",
        "    for feature_name in col_name_list:\n",
        "        max_value = dff[feature_name].max()\n",
        "        min_value = dff[feature_name].min()\n",
        "        result[feature_name] = (dff[feature_name] - min_value) / (max_value - min_value)\n",
        "    return result\n",
        "\n",
        "\n",
        "df = normalize(df,num_cols)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Plot Stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K2dBX2XUrgUu",
        "outputId": "7ec867cc-17dc-4288-dd3e-af736b027ebd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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i1EsfS0Q8Cuxbe3hmZlaPaoZrNkYyFSdg8/CLmZlNYNUU+WskXQjMkHQKcAtwUTZhmZlZI1Qzu+afJR0GrCEZlz8rIm7OLDIzM6tbNWPyAL8BIiJukbStpOmlX8CamdnEU/FwTTpEcx1wYfrQbsB3sgjKzMwao5ox+U+QTJdcAxARDwM7ZxGUmZk1RjVFfkNEbCzdkbQl6UwbMzObmKop8j+V9AVgm/QA7LXADdmEZWZmjVBNkf8csBL4FfC3wPeBM7MIyszMGqOaKZQvk8yLv0jSjsCcqPQ8xWZmlotqZtf0Sdo+LfBLSYr9/80uNDMzq1c1wzU7RMQa4P3A5RFxMHBoNmGZmVkjVFPkt5S0C/AB4MaM4jEzswaqpsifA/wQGIiIX0p6A/BwNmGZmVkjVFzkI+LaiHhLRHw8vf9oRPxlabmkz2cRoJmZ1a7aK0ON568auC8zM2uARhZ5NXBfZmbWAI0s8p4zb2Y2wTSlJy9piqS7JXlWjplZEzWyyF87zrLTgAcb2JaZmVWgriIv6azS7Yj4yhjrzAGOBL5ZT1tmZlY91XP6GUlPRsTry6xzHfAPwHTgf0fEUSOWdwPdAO3t7Qf29vbWHM/Q0BBtbW01b1+rgYGmN7nZ7Nn55JyXvF7jPDnnyaGenLu6upZGRMdoy8qeoEzSmrEWAduU2fYo4OmIWCqpc7R1ImIRsAigo6MjOjtHXa0ifX191LN9rRYubHqTm/X05JNzXvJ6jfPknCeHrHKu5CyUq4GDIuL3IxdIWl5m27cDR0s6ApgGbC/piog4ofpQzcysWpWMyV8O7D7GsivH2zAiPh8RcyJiHnAc8BMXeDOz5inbk4+IM5WYGxHLRyw7PbvQzMysXhXNrkkvDvL9ehqKiL6RB13NzCxb1UyhvEvSQZlFYmZmDVfx5f+Ag4EPSnoCWEcyuyYi4i2ZRGZmZnWrpsi/O7MozMwsE9WcT/4JYC7w5+ntF6rZ3szMmq+aC3mfDZwOlC4OMhW4IougzMysMarpib8POJpkPJ6IWEFyqgIzM5ugqinyG9OplAEgabtsQjIzs0appshfI+lCYIakU4BbgIuyCcvMzBqh4tk1EfHPkg4D1gDzgbMi4ubMIjMzs7pVXOQlnQpc4cJuZtY6qhmuaQd+KekaSYdL8oW7zcwmuGrmyZ8JvBG4GDgJeFjSVyT9UUaxmZlZnar6MVM6u+Z36d+LwOuA6yT9UwaxmZlZnaoZkz8N+BDwDMn1Wj8bEZskbQE8DPxdNiGamVmtqjl3zY7A+9NTGmwWES+nl/kzM7MJppoplGcDSNqZ5FJ+pcefjIgHM4jNzMzqVM25axZIehh4DPgp8DhwU0ZxmZlZA1Rz4PVc4BDgNxGxB3AocHsmUZmZWUNUU+Q3RcQqYAtJW0TErUDHeBtImibpTkn3SnpA0pfqitbMzKpSzYHX1ZLagCXAtyQ9TXpGynFsIDn//JCkqcDPJd0UEf4GYGbWBNX05I8B/gB8GvgB8AiwYLwNIjGU3p2a/kUNcZqZWQ2U/L4pwwakKcBSYE/ggog4fcTybqAboL29/cDe3t6a2xoaGqKtra2OaGszMND0JjebPTufnPOS12ucJ+c8OdSTc1dX19KIGHX4vGyRl7SWpPddOldNaYPShby3ryQISTOA64FTI+L+0dbp6OiI/v7+SnY3qr6+Pjo7O2vevlYLxv0+k62ennxyzkter3GenPPkUE/OksYs8mXH5COiIVd/iojVkm4FDgdGLfJmZtZYZcfk0xkyn5J0vqRuSdWcCmGntAePpG2Aw4Bf1x6umZlVo5KCfRmwCfgZcATwZuC0Cve/C3BZOi6/BXBNRNxYS6BmZla9Sor8XhGxD4Cki4E7K915RNwH7F9jbGZmVqdKplBuKt2IiBczjMXMzBqskp78vpLWpLcFbJPer2p2jZmZNV8ls2umNCMQMzNrvKquDGVmZq3FRd7MrMBc5M3MCsxF3syswFzkzcwKzEXezKzAXOTNzArMRd7MrMBc5M3MCsxF3syswFzkzcwKzEXezKzAXOTNzArMRd7MrMBc5M3MCizTIi9prqRbJS2T9ICkSq8Na2ZmDVDJlaHq8SLQExF3SZoOLJV0c0Qsy7hdMzMj4558RPw2Iu5Kb68FHgR2y7JNMzN7hSKiOQ1J84AlwN4RsWbY491AN0B7e/uBvb29NbcxNDREW1tbfYHWYGCg6U1uNnt2PjnnJa/XOE/OeXKoJ+eurq6lEdEx2rKmFHlJbcBPgfMi4ttjrdfR0RH9/f01t9PX10dnZ2fN29dqwYKmN7lZT08+Oeclr9c4T855cqgnZ0ljFvnMZ9dImgr8J/Ct8Qq8mZk1XtazawRcDDwYEV/Lsi0zM3utrHvybwdOBP5c0j3p3xEZt2lmZqlMp1BGxM8BZdmGmZmNzb94NTMrMBd5M7MCc5E3MyswF3kzswJzkTczKzAXeTOzAnORNzMrMBd5M7MCc5E3MyswF3kzswJzkTczKzAXeTOzAnORNzMrMBd5M7MCy/RUw802MAALF+YdhZnZxOGevJlZgbnIm5kVmIu8mVmBZX0h70skPS3p/izbMTOz0WXdk78UODzjNszMbAyZFvmIWAI8m2UbZmY2No/Jm5kVmCIi2wakecCNEbH3GMu7gW6A9vb2A3t7e2tu65lnhli9uq3m7VvR7NlDtLVNnpyHhiZXvuCcJ4t6cu7q6loaER2jLcv9x1ARsQhYBNDR0RGdnZ017+ub3+zju9+tfftW1NPTRz3PWavp65tc+YJzniyyytnDNWZmBZb1FMqrgNuA+ZIGJX04y/bMzOzVMh2uiYjjs9y/mZmNz8M1ZmYF5iJvZlZgLvJmZgXmIm9mVmAu8mZmBeYib2ZWYC7yZmYF5iJvZlZgLvJmZgWW+wnKrD4DA7BwYfPbveGG5rdpZtVzT97MrMBc5M3MCsxF3syswFzkzcwKzEXezKzAXOTNzArMUyitJgsW5NNuT08+7Zq1Khd5ayl5/S4A/NsAa00erjEzK7DMi7ykwyU9JGlA0ueybs/MzF6R6XCNpCnABcBhwCDwS0mLI2JZlu2aZSGv4xDHHJPfEFVefOylcbIek38rMBARjwJI6gWOAVzkzWxMeR57yUtWH2yKiGz2DEg6Fjg8Ij6S3j8RODgiPjlsnW6gO707H3iojiZnAc/UsX0rmmw5T7Z8wTlPFvXkvHtE7DTagtxn10TEImBRI/YlqT8iOhqxr1Yx2XKebPmCc54ssso56wOvTwFzh92fkz5mZmZNkHWR/yXwRkl7SNoKOA5YnHGbZmaWynS4JiJelPRJ4IfAFOCSiHggwyYbMuzTYiZbzpMtX3DOk0UmOWd64NXMzPLlX7yamRWYi7yZWYG1XJEvd5oESVtLujpdfoekec2PsrEqyPkzkpZJuk/SjyXtnkecjVTp6TAk/aWkkNTy0+0qyVnSB9LX+gFJVzY7xkar4L39ekm3Sro7fX8fkUecjSLpEklPS7p/jOWS9C/p83GfpAPqbjQiWuaP5ODtI8AbgK2Ae4G9RqzzceAb6e3jgKvzjrsJOXcB26a3PzYZck7Xmw4sAW4HOvKOuwmv8xuBu4HXpfd3zjvuJuS8CPhYensv4PG8464z53cCBwD3j7H8COAmQMAhwB31ttlqPfnNp0mIiI1A6TQJwx0DXJbevg44VJKaGGOjlc05Im6NiBfSu7eT/B6hlVXyOgN8GfhHYH0zg8tIJTmfAlwQEc8BRMTTTY6x0SrJOYDt09s7ACuaGF/DRcQS4NlxVjkGuDwStwMzJO1ST5utVuR3A5YPuz+YPjbqOhHxIvA8MLMp0WWjkpyH+zBJT6CVlc05/Ro7NyK+18zAMlTJ6/wm4E2SfiHpdkmHNy26bFSS8xeBEyQNAt8HTm1OaLmp9v97Wbmf1sAaR9IJQAfwZ3nHkiVJWwBfA07KOZRm25JkyKaT5NvaEkn7RMTqXKPK1vHApRGxUNLbgP+QtHdEvJx3YK2i1XrylZwmYfM6krYk+Yq3qinRZaOiU0NIehdwBnB0RGxoUmxZKZfzdGBvoE/S4yRjl4tb/OBrJa/zILA4IjZFxGPAb0iKfquqJOcPA9cARMRtwDSSE3kVVcNPBdNqRb6S0yQsBv4mvX0s8JNIj2i0qLI5S9ofuJCkwLf6OC2UyTkino+IWRExLyLmkRyHODoi+vMJtyEqeW9/h6QXj6RZJMM3jzYzyAarJOcngUMBJP0JSZFf2dQom2sx8KF0ls0hwPMR8dt6dthSwzUxxmkSJJ0D9EfEYuBikq90AyQHOI7LL+L6VZjzV4E24Nr0GPOTEXF0bkHXqcKcC6XCnH8I/IWkZcBLwGcjomW/pVaYcw9wkaRPkxyEPamVO22SriL5oJ6VHmc4G5gKEBHfIDnucAQwALwAnFx3my38fJmZWRmtNlxjZmZVcJE3MyswF3kzswJzkTczKzAXeTOzAnORNzMrMBd5M7MC+2/gw/ACZdqifwAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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o5zz5rwHvBW4Bns6nO2Zm1kr1hPzaiFiWW0/MzKzl6gn5MyWdB1wFPD7xYkRc3vJemZlZS9QT8qcBvwvMY8twTQAOeTOzbVQ9IX94RByQW0/MzKzl6jlP/qeSDsytJ2Zm1nL17MkfAdyYXovmcUBAbO0USjMzK1Y9IX9sbr0wM7Nc1PON17sgueAYW65Zc18enTIzs9aYNeQlfRCYFxFnpS9dA6wHdgQuAv4pv+6ZmVkzshx4/RPg7CnP16Xj8AcBr8+lV2Zm1hKZzq6JiI1Tnp6TvvYU8Jw8OmVmZq2RJeS7pt7mLyIuBJC0E7BrTv0yM7MWyBLylwFflLTzxAvpteG/kE4zM7NtVJaQ/zCwBrhb0pik64FVwAPpNDMz20bNenZNOvb+AUkfA/ZPXx6PiMdy7ZmZmTWtnssaXAcMAI864M3M5oZ6Qn4AeBL4uqSfS/o7SS/IsqCk7SXdIOnKhnppZmYNyRzyEXFXRHw6IpYAfwr8AfDrjIufAdzeQP/MzKwJ9ezJI2lfSe8DhkmuLf++DMssJvnS1HkN9dDMzBqmiMg2o3QdyQ1DvgFcmt6kO8tyl5Fc+mAB8HcR8YZp0weBQYDu7u4lw8PD2Xs/TbVapaurq+HlGzU+3vYmJ/X0FFNzUYraxkVyzZ2hmZr7+/vHIqIy07R6rkL5loi4o56GJb0BWBMRY5L6ZponIpYCSwEqlUr09c04WyYjIyM0s3yjzj579nnyMjRUTM1FKWobF8k1d4a8ap51uEbSgKR9JwJe0kck3SRpmaT9Zln8SOA4SatIhnheJemrTffazMwyyTIm/wlgLUzumZ8C/AWwjORbrzVFxAcjYnFE9AInAj+MiFOa6rGZmWWWJeQjIh5NH/9v4PyIGIuI84C98uuamZk1K0vIS1KXpO2Ao4Grpkybn7WhiBiZftDVzMzyleXA62eBG4GHgdsjYhRA0qHAb3Lsm5mZNSnLtWsukPR94LnATVMm3Q+cNvFE0kERcVvru2hmZo3KdAplRNwL3Dvttel78V8BDmtRv8zMrAXq+sbrLNTCdZmZWQu0MuSzfXXWzMzappUhb2Zm25jMIS/pq5LeJul3a8zyRIv6ZGZmLVLPnvz5wPOAf5N0p6RvSjpjYmJEHNHy3pmZWVMyX6AsIq6WtAI4HOgH3g4cBJyTU9/MzKxJmUNe0lXALsA1wI+AwyNiTV4dMzOz5tUzXHMzybj7wSR3hTpY0nNy6ZWZmbVEPcM17wGQtAA4FfgS0APslEvPzMysafUM17wLOApYAqwCLiAZtjEzs21UPXeGmg98BhiLiCdz6o+ZmbVQPcM1/5pnR8zMrPX8jVczsxJzyJuZlViuIS9pvqSfpTf+vk3Sx/Jsz8zMnqmeA6+NeBx4VURUJc0DfizpexFxbc7tmpkZOYd8RARQTZ/OS398SWIzszZRksM5NiBtD4wB+wPnRsT7p00fBAYBuru7lwwPDzfcVrVapaurq4neNmZ8vO1NTurpKabmohS1jYvkmjtDMzX39/ePRURlpmm5h/xkQ9LuwBXA6RFx60zzVCqVGB0dbbiNkZER+vr6Gl6+UQMDbW9y0tBQMTUXpahtXCTX3BmaqVlSzZBv29k1EbEeuBo4tl1tmpl1urzPrtkr3YMnvZjZq4Ff5NmmmZltkffZNc8DLkrH5bcDvh4RV+bcppmZpfI+u+Zm4NA82zAzs9r8jVczsxJzyJuZlZhD3sysxBzyZmYl5pA3Mysxh7yZWYk55M3MSswhb2ZWYg55M7MSc8ibmZWYQ97MrMQc8mZmJeaQNzMrMYe8mVmJOeTNzErMIW9mVmJ53/5vH0lXS1op6TZJZ+TZnpmZPVPet/97EhiKiOslLQDGJP0gIlbm3K6ZmZHznnxE/CYirk8fPwLcDuydZ5tmZraFIqI9DUm9wArg4Ih4eMrrg8AgQHd395Lh4eGG26hWq3R1dTXX0QaMj7e9yUk9PcXUXJSitnGRXHNnaKbm/v7+sYiozDStLSEvqQtYDnwiIi6vNV+lUonR0dGG2xkZGaGvr6/h5Rs1MND2JicNDRVTc1GK2sZFcs2doZmaJdUM+dzPrpE0D/gm8LWtBbyZmbVe3mfXCDgfuD0iPpNnW2Zm9mx578kfCfwZ8CpJN6Y/r8u5TTMzS+V6CmVE/BhQnm2YmVlt/sarmVmJOeTNzErMIW9mVmIOeTOzEnPIm5mVmEPezKzEHPJmZiXmkDczKzGHvJlZiTnkzcxKzCFvZlZiDnkzsxJzyJuZlZhD3sysxBzyZmYl5pA3MyuxXG8a0m7j43D22UX3wsxs25H3PV4vkLRG0q15tmNmZjPLe7jmQuDYnNswM7Macg35iFgBPJRnG2ZmVpsiIt8GpF7gyog4uMb0QWAQoLu7e8nw8HDDbT34YJX167saXn4u6ump0tXVOTVXq51VL7jmTtFMzf39/WMRUZlpWuEHXiNiKbAUoFKpRF9fX8PrOu+8Eb797caXn4uGhkZo5nc214yMdFa94Jo7RV41+xRKM7MSc8ibmZVY3qdQXgJcAxwgabWkt+bZnpmZPVOuY/IRcVKe6zczs60r/MCrNaeob/l+5zvtb9PM6ucxeTOzEnPIm5mVmEPezKzEHPJmZiXmkDczKzGHvJlZiTnkzcxKzCFvZlZiDnkzsxJzyJuZlZhD3sysxBzyZmYl5pA3Mysxh7yZWYk55M3MSswhb2ZWYrmHvKRjJd0haVzSB/Juz8zMtsj7Hq/bA+cCrwUOBE6SdGCebZqZ2RZ53/7vpcB4RNwJIGkYOB5YmXO7lrOBgWLaHRoqpl2zuSrvkN8buGfK89XAy6bOIGkQGEyfViXd0UR7i4AHm1h+zrnyys6qudPqTbnmztBMzfvWmlD4jbwjYimwtBXrkjQaEZVWrGuu6LSaO61ecM2dIq+a8z7wei+wz5Tni9PXzMysDfIO+Z8DL5K0n6QdgROBZTm3aWZmqVyHayLiSUnvAr4PbA9cEBG35dhkS4Z95phOq7nT6gXX3ClyqVkRkcd6zcxsG+BvvJqZlZhD3sysxOZcyM92mQRJO0m6NJ1+naTe9veytTLU/LeSVkq6WdJVkmqeMztXZL0chqQ/lhSS5vzpdllqlvSmdFvfJunidvex1TL8bb9A0tWSbkj/vl9XRD9bRdIFktZIurXGdEn6v+nv42ZJhzXdaETMmR+Sg7e/Al4I7AjcBBw4bZ53Al9IH58IXFp0v9tQcz+wc/r4HZ1QczrfAmAFcC1QKbrfbdjOLwJuAPZInz+36H63oealwDvSxwcCq4rud5M1vwI4DLi1xvTXAd8DBBwBXNdsm3NtT37yMgkR8QQwcZmEqY4HLkofXwYcLUlt7GOrzVpzRFwdEY+mT68l+T7CXJZlOwP8I/ApYFM7O5eTLDW/DTg3In4LEBFr2tzHVstScwC7po93A+5rY/9aLiJWAA9tZZbjgS9H4lpgd0nPa6bNuRbyM10mYe9a80TEk8AGYGFbepePLDVP9VaSPYG5bNaa04+x+0TEd9vZsRxl2c4vBl4s6SeSrpV0bNt6l48sNX8UOEXSauA/gNPb07XC1Pv/fVaFX9bAWkfSKUAFeGXRfcmTpO2AzwCnFtyVdtuBZMimj+TT2gpJvx8R6wvtVb5OAi6MiLMlvRz4iqSDI+Lpojs2V8y1Pfksl0mYnEfSDiQf8da1pXf5yHRpCEnHAB8CjouIx9vUt7zMVvMC4GBgRNIqkrHLZXP84GuW7bwaWBYRmyPi18AvSUJ/rspS81uBrwNExDXAfJILeZVVyy8FM9dCPstlEpYBf54+PgH4YaRHNOaoWWuWdCjwRZKAn+vjtDBLzRGxISIWRURvRPSSHIc4LiJGi+luS2T52/4WyV48khaRDN/c2c5OtliWmu8GjgaQ9HskIb+2rb1sr2XAW9KzbI4ANkTEb5pZ4Zwarokal0mQdBYwGhHLgPNJPtKNkxzgOLG4HjcvY83/AnQB30iPMd8dEccV1ukmZay5VDLW/H3gNZJWAk8B742IOfspNWPNQ8D/k/QekoOwp87lnTZJl5C8US9KjzOcCcwDiIgvkBx3eB0wDjwKnNZ0m3P492VmZrOYa8M1ZmZWB4e8mVmJOeTNzErMIW9mVmIOeTOzEnPIm5mVmEPezKzE/gelJ47GEXtJbQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "num_bins = 10\n",
        "for i in num_cols:\n",
        "    plt.hist(df[i],num_bins,density=True, stacked= True,facecolor='blue',alpha=0.7)\n",
        "    plt.ylabel(i)\n",
        "    plt.title(\"Data Distribution for \" + i)\n",
        "    plt.legend()\n",
        "    plt.grid()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "JC0Po3G5rgUv",
        "outputId": "52808979-b989-4666-fa92-af6b0779bcc8"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(df[num_cols[0]], color='g', bins=100, kde_kws={\"color\": \"k\", \"lw\": 3, \"label\": num_cols[0]},\n",
        "             hist_kws={'alpha': 0.4});"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "tr0evfg6rgUv",
        "outputId": "3496b9f7-c667-46fb-fc7e-3471cd4a3e2e"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(df[num_cols[1]], color='r', bins=100, kde_kws={\"color\": \"k\", \"lw\": 3, \"label\": num_cols[1]},\n",
        "             hist_kws={'alpha': 0.4});"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "rCm-lMgargUw",
        "outputId": "0acb297b-6096-4426-ef28-9c71e3dd7fb5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(df[num_cols[2]], color='b', bins=100, kde_kws={\"color\": \"k\", \"lw\": 3, \"label\": num_cols[2]},\n",
        "             hist_kws={'alpha': 0.4});"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "NpvytTtIrgUw",
        "outputId": "94ae8f45-5d1b-437f-c6b0-4ff4a3008884"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(df[num_cols[3]], color='r', bins=100, kde_kws={\"color\": \"k\", \"lw\": 3, \"label\": num_cols[3]},\n",
        "             hist_kws={'alpha': 0.4});"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "9K9R-Bu3rgUx",
        "outputId": "348b368c-61b3-41a2-f816-b3f450c0b1d1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(df[num_cols[4]], color='b', bins=100, kde_kws={\"color\": \"k\", \"lw\": 3, \"label\": num_cols[4]},\n",
        "             hist_kws={'alpha': 0.4});"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "pCSrcBG7rgUy"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import GroupKFold\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.neural_network import MLPRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "8IBJokabrgUy"
      },
      "outputs": [],
      "source": [
        "Y = pd.DataFrame(df['y'])\n",
        "df = df.drop(['y'], axis=1)\n",
        "X = df\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "ptIA3dKyrgUz"
      },
      "outputs": [],
      "source": [
        "X_train = X_train.drop(['Player1_id'], axis=1)\n",
        "X_train = X_train.drop(['Player1_name'], axis=1)\n",
        "X_train = X_train.drop(['Player2_id'], axis=1)\n",
        "X_train = X_train.drop(['Player2_name'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "V9QzlbzYrgUz"
      },
      "outputs": [],
      "source": [
        "test_names = []\n",
        "for index, row in X_test.iterrows():\n",
        "    v = []\n",
        "    v.append(row['Player1_name'])\n",
        "    v.append(row['Player2_name'])\n",
        "    test_names.append(v)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "jAxt2fpvrgUz"
      },
      "outputs": [],
      "source": [
        "X_test = X_test.drop(['Player1_id'], axis=1)\n",
        "X_test = X_test.drop(['Player1_name'], axis=1)\n",
        "X_test = X_test.drop(['Player2_id'], axis=1)\n",
        "X_test = X_test.drop(['Player2_name'], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_q6L76F5rgU1"
      },
      "source": [
        "## Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKK6oHBmrgU1",
        "outputId": "6e895e30-263f-4f82-b3c1-0210187737da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Score: 0.9037418991692354\n",
            "Best Hyperparameters: {'solver': 'sag', 'penalty': 'l2', 'C': 1291.5496650148827}\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Define model\n",
        "model = LogisticRegression()\n",
        "\n",
        "# define evaluation\n",
        "#gkf = GroupKFold(n_splits=3) if not spec as cv= in the randomized search default is 5 fold cv\n",
        "\n",
        "# define search space\n",
        "param_grid = {\n",
        "    'C' : np.logspace(0, 4, num=10),\n",
        "    'penalty' : ['l1', 'l2'],\n",
        "    'solver' : ['liblinear', 'sag']\n",
        "}\n",
        "\n",
        "search = RandomizedSearchCV(model, param_grid, n_iter=5, cv=10, scoring='accuracy', n_jobs=-1, random_state=1)\n",
        "\n",
        "# execute search\n",
        "result = search.fit(X_train, y_train)\n",
        "# summarize result\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKIXJQrafP8o",
        "outputId": "ecee1063-d759-411c-b005-ff47c1c772d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.92      0.91     16419\n",
            "           1       0.92      0.89      0.90     16370\n",
            "\n",
            "    accuracy                           0.91     32789\n",
            "   macro avg       0.91      0.91      0.91     32789\n",
            "weighted avg       0.91      0.91      0.91     32789\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Prediction with best model from random search \n",
        "best_random = result.best_estimator_\n",
        "\n",
        "y_pred_test = best_random.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKwouzat9KyK"
      },
      "source": [
        "## Decision tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2zeBF6c-9Pm7",
        "outputId": "c06353d2-d6f3-46f8-a98a-02986917b022"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Score: 0.7380541865308181\n",
            "Best Hyperparameters: {'max_features': 'auto', 'max_depth': 85}\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Define model\n",
        "model = DecisionTreeClassifier()\n",
        "\n",
        "\n",
        "# define search space\n",
        "param_grid = {\n",
        "    'max_depth': list(range(25, 100, 10)),\n",
        "    'max_features': ['auto', 'sqrt', 'log2']\n",
        "}\n",
        "\n",
        "search = RandomizedSearchCV(model, param_grid, n_iter=5, cv=10, scoring='accuracy', n_jobs=-1, random_state=1)\n",
        "\n",
        "# execute search\n",
        "result = search.fit(X_train, y_train)\n",
        "# summarize result\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TV-Z5xwr-Zz0",
        "outputId": "074c6e02-a034-4622-8a0e-54d21f613161"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.72      0.72     16419\n",
            "           1       0.72      0.73      0.72     16370\n",
            "\n",
            "    accuracy                           0.72     32789\n",
            "   macro avg       0.72      0.72      0.72     32789\n",
            "weighted avg       0.72      0.72      0.72     32789\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Prediction with best model from random search \n",
        "best_random = result.best_estimator_\n",
        "\n",
        "y_pred_test = best_random.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUYsI2pUrgU0"
      },
      "source": [
        "## RandomForestClassifier\n",
        "\n",
        "Random forest is an ensemble method. We use also random search grid for hyperparameter tuning."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhqvPVnmrgU0",
        "outputId": "52dc4ebf-7087-4d89-c1b8-047ec9e00cd1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Score: 0.8977032287083618\n",
            "Best Hyperparameters: {'n_estimators': 190, 'max_depth': 65}\n"
          ]
        }
      ],
      "source": [
        "# Define model\n",
        "model = RandomForestClassifier()\n",
        "\n",
        "# define evaluation\n",
        "#gkf = GroupKFold(n_splits=3) if not spec as cv= in the randomized search default is 5 fold cv\n",
        "\n",
        "# define search space\n",
        "param_grid = {\n",
        "    'max_depth': list(range(25, 100, 10)),\n",
        "    'n_estimators': list(range(80, 200, 10))\n",
        "}\n",
        "\n",
        "search = RandomizedSearchCV(model, param_grid, n_iter=5, cv=10, scoring='accuracy', n_jobs=-1, random_state=1)\n",
        "\n",
        "# execute search\n",
        "result = search.fit(X_train, y_train)\n",
        "# summarize result\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4kraVuS9MbG4",
        "outputId": "63075566-c3a2-4492-f3ac-0df51692831b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.93      0.90     16419\n",
            "           1       0.93      0.86      0.90     16370\n",
            "\n",
            "    accuracy                           0.90     32789\n",
            "   macro avg       0.90      0.90      0.90     32789\n",
            "weighted avg       0.90      0.90      0.90     32789\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Prediction with best model from random search \n",
        "best_random = result.best_estimator_\n",
        "\n",
        "y_pred_test = best_random.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJ-IaCP-PEyZ"
      },
      "source": [
        "# ADABoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKYe2z9LPr3K",
        "outputId": "5fb4f835-b56c-45af-a676-dba6ea43df1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Score: 0.8707094551087058\n",
            "Best Hyperparameters: {'n_estimators': 180}\n"
          ]
        }
      ],
      "source": [
        "# Define model\n",
        "model = AdaBoostClassifier()\n",
        "\n",
        "# define evaluation\n",
        "#gkf = GroupKFold(n_splits=3) if not spec as cv= in the randomized search default is 5 fold cv\n",
        "\n",
        "# define search space\n",
        "param_grid = {\n",
        "    'n_estimators': list(range(80, 200, 10))\n",
        "}\n",
        "\n",
        "search = RandomizedSearchCV(model, param_grid, n_iter=5, cv=10, scoring='accuracy', n_jobs=-1, random_state=1)\n",
        "\n",
        "# execute search\n",
        "result = search.fit(X_train, y_train)\n",
        "# summarize result\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CGOhgUhYQMbg",
        "outputId": "a8f47df3-6852-454f-c438-6611d6e9e383"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.90      0.87     16419\n",
            "           1       0.89      0.85      0.87     16370\n",
            "\n",
            "    accuracy                           0.87     32789\n",
            "   macro avg       0.87      0.87      0.87     32789\n",
            "weighted avg       0.87      0.87      0.87     32789\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Prediction with best model from random search \n",
        "best_random = result.best_estimator_\n",
        "\n",
        "y_pred_test = best_random.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8LN_8yNhe5pj"
      },
      "source": [
        "# Logit Boost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kaz3vACJe8bH",
        "outputId": "b116f051-1554-4cf9-f611-dd7e0434df9d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting logitboost\n",
            "  Downloading logitboost-0.7-py3-none-any.whl (9.1 kB)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from logitboost) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from logitboost) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from logitboost) (1.21.6)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->logitboost) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->logitboost) (1.1.0)\n",
            "Installing collected packages: logitboost\n",
            "Successfully installed logitboost-0.7\n"
          ]
        }
      ],
      "source": [
        "! pip install logitboost\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FeCjG8UAfLQg",
        "outputId": "35df48cc-52fb-4e63-b5cb-404958f19449"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Score: 0.8647910130598117\n",
            "Best Hyperparameters: {'n_estimators': 180}\n"
          ]
        }
      ],
      "source": [
        "import logitboost as lb\n",
        "# Define model\n",
        "model = lb.LogitBoost()\n",
        "\n",
        "# define search space\n",
        "param_grid = {\n",
        "    'n_estimators': list(range(80, 200, 10))\n",
        "}\n",
        "\n",
        "search = RandomizedSearchCV(model, param_grid, n_iter=5, cv=10, scoring='accuracy', n_jobs=-1, random_state=1)\n",
        "\n",
        "# execute search\n",
        "result = search.fit(X_train, y_train)\n",
        "# summarize result\n",
        "print('Best Score: %s' % result.best_score_)\n",
        "print('Best Hyperparameters: %s' % result.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "812zJuJWyVCO",
        "outputId": "5252d029-5bcf-45ca-cd59-51023ef8e888"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.91      0.87     16419\n",
            "           1       0.90      0.83      0.86     16370\n",
            "\n",
            "    accuracy                           0.87     32789\n",
            "   macro avg       0.87      0.87      0.87     32789\n",
            "weighted avg       0.87      0.87      0.87     32789\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Prediction with best model from random search \n",
        "best_random = result.best_estimator_\n",
        "\n",
        "y_pred_test = best_random.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "anm44oKOrgU2"
      },
      "source": [
        "## Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9c05hKy_1Lth"
      },
      "outputs": [],
      "source": [
        "# import tensorflow\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras import Input\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "\n",
        "# use keras API\n",
        "model = tf.keras.Sequential()\n",
        "\n",
        "# determine the number of input features\n",
        "n_features = X_train.shape[1]\n",
        "# define model\n",
        "model = Sequential()\n",
        "model.add(Input(shape=X_train.shape[1]))\n",
        "model.add(Dense(100, activation='relu', kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(100, activation='relu', kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.6))\n",
        "model.add(Dense(50, activation='relu', kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.6))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkhKwd7p4ZZW",
        "outputId": "f67fae36-5d87-4b67-e802-649db9478832"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "521/521 [==============================] - 6s 10ms/step - loss: 0.2465 - accuracy: 0.8929\n",
            "Epoch 2/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.2275 - accuracy: 0.9005\n",
            "Epoch 3/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.2184 - accuracy: 0.9047\n",
            "Epoch 4/50\n",
            "521/521 [==============================] - 5s 9ms/step - loss: 0.2104 - accuracy: 0.9080\n",
            "Epoch 5/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.2041 - accuracy: 0.9098\n",
            "Epoch 6/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.2004 - accuracy: 0.9105\n",
            "Epoch 7/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1969 - accuracy: 0.9130\n",
            "Epoch 8/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1943 - accuracy: 0.9136\n",
            "Epoch 9/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1900 - accuracy: 0.9158\n",
            "Epoch 10/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1850 - accuracy: 0.9175\n",
            "Epoch 11/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1844 - accuracy: 0.9184\n",
            "Epoch 12/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1803 - accuracy: 0.9215\n",
            "Epoch 13/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1770 - accuracy: 0.9216\n",
            "Epoch 14/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1773 - accuracy: 0.9209\n",
            "Epoch 15/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1744 - accuracy: 0.9229\n",
            "Epoch 16/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1733 - accuracy: 0.9236\n",
            "Epoch 17/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1701 - accuracy: 0.9247\n",
            "Epoch 18/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1719 - accuracy: 0.9241\n",
            "Epoch 19/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1688 - accuracy: 0.9261\n",
            "Epoch 20/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1682 - accuracy: 0.9256\n",
            "Epoch 21/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1658 - accuracy: 0.9261\n",
            "Epoch 22/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1639 - accuracy: 0.9266\n",
            "Epoch 23/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1649 - accuracy: 0.9268\n",
            "Epoch 24/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1614 - accuracy: 0.9289\n",
            "Epoch 25/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1613 - accuracy: 0.9287\n",
            "Epoch 26/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1597 - accuracy: 0.9287\n",
            "Epoch 27/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1579 - accuracy: 0.9299\n",
            "Epoch 28/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1572 - accuracy: 0.9301\n",
            "Epoch 29/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1550 - accuracy: 0.9323\n",
            "Epoch 30/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1568 - accuracy: 0.9298\n",
            "Epoch 31/50\n",
            "521/521 [==============================] - 6s 12ms/step - loss: 0.1542 - accuracy: 0.9320\n",
            "Epoch 32/50\n",
            "521/521 [==============================] - 6s 12ms/step - loss: 0.1526 - accuracy: 0.9321\n",
            "Epoch 33/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1520 - accuracy: 0.9329\n",
            "Epoch 34/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1509 - accuracy: 0.9338\n",
            "Epoch 35/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1513 - accuracy: 0.9323\n",
            "Epoch 36/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1489 - accuracy: 0.9339\n",
            "Epoch 37/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1470 - accuracy: 0.9352\n",
            "Epoch 38/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1480 - accuracy: 0.9353\n",
            "Epoch 39/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1471 - accuracy: 0.9354\n",
            "Epoch 40/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1478 - accuracy: 0.9342\n",
            "Epoch 41/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1456 - accuracy: 0.9364\n",
            "Epoch 42/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1465 - accuracy: 0.9355\n",
            "Epoch 43/50\n",
            "521/521 [==============================] - 7s 13ms/step - loss: 0.1447 - accuracy: 0.9361\n",
            "Epoch 44/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1433 - accuracy: 0.9364\n",
            "Epoch 45/50\n",
            "521/521 [==============================] - 6s 11ms/step - loss: 0.1439 - accuracy: 0.9362\n",
            "Epoch 46/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1454 - accuracy: 0.9359\n",
            "Epoch 47/50\n",
            "521/521 [==============================] - 6s 11ms/step - loss: 0.1431 - accuracy: 0.9371\n",
            "Epoch 48/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1424 - accuracy: 0.9377\n",
            "Epoch 49/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1406 - accuracy: 0.9384\n",
            "Epoch 50/50\n",
            "521/521 [==============================] - 5s 10ms/step - loss: 0.1412 - accuracy: 0.9377\n",
            "1025/1025 [==============================] - 3s 3ms/step - loss: 0.1856 - accuracy: 0.9250\n"
          ]
        }
      ],
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train, y_train, epochs=50, batch_size=128)\n",
        "test_loss, test_acc = model.evaluate(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Swdg5ucyJXIW",
        "outputId": "4b93091d-f721-4191-dfa6-85096818df6f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.94      0.93     16419\n",
            "           1       0.94      0.91      0.92     16370\n",
            "\n",
            "    accuracy                           0.93     32789\n",
            "   macro avg       0.93      0.92      0.92     32789\n",
            "weighted avg       0.93      0.93      0.92     32789\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_test = model.predict(X_test)\n",
        "y_pred_test = [int(i > .5) for i in y_pred_test]\n",
        "\n",
        "print(classification_report(y_test, y_pred_test))\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "ModelsTrainingAndTest.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}