6 homework auc.ipynb

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    "colab": {
      "name": "Untitled17.ipynb",
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      "authorship_tag": "ABX9TyOU5UH40mAeew2lJWbaIfU6",
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/StetskoSergey/pida5_stetsko/blob/master/6%20homework%20auc.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9u9eL5hYl26a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZAD6P5Mmg6s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "2ec721c6-c64d-465a-806d-d4caf23f8ded"
      },
      "source": [
        "# Загружаем данные и переводим целевой столбец в 0 и 1\n",
        "data = pd.read_csv('adult.csv')\n",
        "data['target'] = data['income'].apply(lambda x: 0 if x=='<=50K' else 1) \n",
        "data = data.drop(columns=['income'])\n",
        "data.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>educational-num</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>gender</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25</td>\n",
              "      <td>Private</td>\n",
              "      <td>226802</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>89814</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Farming-fishing</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>United-States</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>Local-gov</td>\n",
              "      <td>336951</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Protective-serv</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44</td>\n",
              "      <td>Private</td>\n",
              "      <td>160323</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>7688</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18</td>\n",
              "      <td>?</td>\n",
              "      <td>103497</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>?</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>United-States</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  workclass  fnlwgt  ... hours-per-week  native-country target\n",
              "0   25    Private  226802  ...             40   United-States      0\n",
              "1   38    Private   89814  ...             50   United-States      0\n",
              "2   28  Local-gov  336951  ...             40   United-States      1\n",
              "3   44    Private  160323  ...             40   United-States      1\n",
              "4   18          ?  103497  ...             30   United-States      0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTC-GSleqvVy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Нужно привести список стран к количеству не более 20 например,\n",
        "# для этого выведем список самых частых стран, а остальные приведем к значению \"прочие\"\n",
        "from collections import Counter\n",
        "spisok_osn_stran = [i[0] for i in Counter(data['native-country']).most_common()[:20]]\n",
        "data['native-country'] = data['native-country'].apply(lambda x: (x if x!='?' else 'norhing') if x in spisok_osn_stran else 'another')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jNfznEjoKVB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "7b991091-84aa-4fc6-ce65-45f4dde43225"
      },
      "source": [
        "#Находим категориальные признаки\n",
        "cat_feat = list(data.dtypes[data.dtypes == object].index)\n",
        "print('Категориальные признаки \\n',cat_feat)\n",
        "\n",
        "#закодируем пропущенные значений строкой, факт пропущенного значения тоже может нести в себе информацию\n",
        "data[cat_feat] = data[cat_feat].fillna('nan')\n",
        "\n",
        "#отфильтруем непрерывные признаки\n",
        "num_feat = [f for f in data if f not in (cat_feat + ['index', 'target'])]\n",
        "print('Непрерывные признаки \\n', num_feat)\n",
        "\n",
        "# Смотрим сколько у нас значений по каждому категориальному признаку\n",
        "cat_nunique = data[cat_feat].nunique()\n",
        "print(cat_nunique)\n",
        "\n",
        "#Чтобы в разы не увеличивать число признаков при построении dummy,\n",
        "#будем использовать категориальные признаки с < 30 уникальных значений\n",
        "cat_feat = list(cat_nunique[cat_nunique < 30].index)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Категориальные признаки \n",
            " ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'native-country']\n",
            "Непрерывные признаки \n",
            " ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
            "workclass          9\n",
            "education         16\n",
            "marital-status     7\n",
            "occupation        15\n",
            "relationship       6\n",
            "race               5\n",
            "gender             2\n",
            "native-country    21\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jBYGL-MoyHm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Создаем дамми-переменные для категорий\n",
        "dummy_train = pd.get_dummies(data[cat_feat], columns=cat_feat)\n",
        "\n",
        "dummy_cols = list(set(dummy_train))\n",
        "\n",
        "dummy_train = dummy_train[dummy_cols]\n",
        "\n",
        "# Заменяем пропуски на специальное значение -999, чтобы деревья могли их отличить\n",
        "X = pd.concat([data[num_feat].fillna(-999),\n",
        "                     dummy_train], axis=1)\n",
        "train_median = data[num_feat].median()\n",
        "X = pd.concat([data[num_feat].fillna(train_median),\n",
        "                     data[num_feat + cat_feat].isnull().astype(np.int8).add_suffix('_NaN'),\n",
        "                     dummy_train], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yG3_BwNTuQde",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "# Разбиваем на train/test\n",
        "splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=777)\n",
        "\n",
        "for train_index, test_index in splitter.split(X, np.zeros(X.shape[0])):\n",
        "    X_train = X.iloc[train_index]\n",
        "    X_test = X.iloc[test_index]\n",
        "        \n",
        "    y_train = data.iloc[train_index]['target']\n",
        "    y_test = data.iloc[test_index]['target']\n",
        "\n",
        "X_train.reset_index(drop=True, inplace=True)\n",
        "X_test.reset_index(drop=True, inplace=True)\n",
        "y_train.reset_index(drop=True, inplace=True)\n",
        "y_test.reset_index(drop=True, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scKo-MLypTea",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# делаем нормализацию данных\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train[num_feat])\n",
        "X_train = pd.concat([pd.DataFrame(scaler.transform(X_train[num_feat]), columns = num_feat), X_train.drop(columns = num_feat)], axis = 1)\n",
        "X_test = pd.concat([pd.DataFrame(scaler.transform(X_test[num_feat]), columns = num_feat), X_test.drop(columns = num_feat)], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-EJ9cmcjyLk5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "7977f479-256a-4bbe-e5c6-183377995599"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>educational-num</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>age_NaN</th>\n",
              "      <th>fnlwgt_NaN</th>\n",
              "      <th>educational-num_NaN</th>\n",
              "      <th>capital-gain_NaN</th>\n",
              "      <th>capital-loss_NaN</th>\n",
              "      <th>hours-per-week_NaN</th>\n",
              "      <th>workclass_NaN</th>\n",
              "      <th>education_NaN</th>\n",
              "      <th>marital-status_NaN</th>\n",
              "      <th>occupation_NaN</th>\n",
              "      <th>relationship_NaN</th>\n",
              "      <th>race_NaN</th>\n",
              "      <th>gender_NaN</th>\n",
              "      <th>native-country_NaN</th>\n",
              "      <th>occupation_Other-service</th>\n",
              "      <th>native-country_Jamaica</th>\n",
              "      <th>workclass_Self-emp-not-inc</th>\n",
              "      <th>occupation_Craft-repair</th>\n",
              "      <th>relationship_Husband</th>\n",
              "      <th>marital-status_Separated</th>\n",
              "      <th>relationship_Not-in-family</th>\n",
              "      <th>gender_Female</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>native-country_Italy</th>\n",
              "      <th>native-country_South</th>\n",
              "      <th>native-country_Philippines</th>\n",
              "      <th>education_Some-college</th>\n",
              "      <th>workclass_Without-pay</th>\n",
              "      <th>native-country_Cuba</th>\n",
              "      <th>marital-status_Married-civ-spouse</th>\n",
              "      <th>workclass_Private</th>\n",
              "      <th>marital-status_Widowed</th>\n",
              "      <th>native-country_India</th>\n",
              "      <th>education_Prof-school</th>\n",
              "      <th>...</th>\n",
              "      <th>workclass_Never-worked</th>\n",
              "      <th>education_12th</th>\n",
              "      <th>race_Black</th>\n",
              "      <th>native-country_United-States</th>\n",
              "      <th>occupation_Protective-serv</th>\n",
              "      <th>relationship_Other-relative</th>\n",
              "      <th>workclass_Federal-gov</th>\n",
              "      <th>occupation_Adm-clerical</th>\n",
              "      <th>education_1st-4th</th>\n",
              "      <th>native-country_Canada</th>\n",
              "      <th>education_7th-8th</th>\n",
              "      <th>occupation_Farming-fishing</th>\n",
              "      <th>race_Amer-Indian-Eskimo</th>\n",
              "      <th>relationship_Wife</th>\n",
              "      <th>native-country_Dominican-Republic</th>\n",
              "      <th>education_Assoc-acdm</th>\n",
              "      <th>race_White</th>\n",
              "      <th>education_10th</th>\n",
              "      <th>occupation_Exec-managerial</th>\n",
              "      <th>workclass_State-gov</th>\n",
              "      <th>marital-status_Married-AF-spouse</th>\n",
              "      <th>occupation_?</th>\n",
              "      <th>native-country_El-Salvador</th>\n",
              "      <th>native-country_Japan</th>\n",
              "      <th>education_9th</th>\n",
              "      <th>education_Doctorate</th>\n",
              "      <th>native-country_Mexico</th>\n",
              "      <th>occupation_Handlers-cleaners</th>\n",
              "      <th>occupation_Sales</th>\n",
              "      <th>race_Other</th>\n",
              "      <th>native-country_norhing</th>\n",
              "      <th>native-country_Germany</th>\n",
              "      <th>occupation_Prof-specialty</th>\n",
              "      <th>workclass_?</th>\n",
              "      <th>education_Masters</th>\n",
              "      <th>education_HS-grad</th>\n",
              "      <th>education_Preschool</th>\n",
              "      <th>education_Assoc-voc</th>\n",
              "      <th>education_5th-6th</th>\n",
              "      <th>workclass_Self-emp-inc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.215942</td>\n",
              "      <td>0.100829</td>\n",
              "      <td>1.132487</td>\n",
              "      <td>-0.144946</td>\n",
              "      <td>-0.218907</td>\n",
              "      <td>-2.046860</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <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",
              "      <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>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <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",
              "      <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>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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.070105</td>\n",
              "      <td>-0.482521</td>\n",
              "      <td>0.744015</td>\n",
              "      <td>-0.144946</td>\n",
              "      <td>-0.218907</td>\n",
              "      <td>-0.840062</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>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>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",
              "      <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>...</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",
              "      <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>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",
              "      <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",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.971603</td>\n",
              "      <td>0.082542</td>\n",
              "      <td>-1.586816</td>\n",
              "      <td>-0.144946</td>\n",
              "      <td>-0.218907</td>\n",
              "      <td>-0.035529</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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <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>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <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",
              "      <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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.752849</td>\n",
              "      <td>0.061335</td>\n",
              "      <td>1.132487</td>\n",
              "      <td>-0.144946</td>\n",
              "      <td>-0.218907</td>\n",
              "      <td>-0.437795</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>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>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",
              "      <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>...</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",
              "      <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",
              "      <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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.534094</td>\n",
              "      <td>-0.716923</td>\n",
              "      <td>-0.032929</td>\n",
              "      <td>-0.144946</td>\n",
              "      <td>-0.218907</td>\n",
              "      <td>-0.196435</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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <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",
              "      <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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 101 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        age    fnlwgt  ...  education_5th-6th  workclass_Self-emp-inc\n",
              "0 -1.215942  0.100829  ...                  0                       0\n",
              "1 -1.070105 -0.482521  ...                  0                       0\n",
              "2  0.971603  0.082542  ...                  0                       0\n",
              "3  0.752849  0.061335  ...                  0                       0\n",
              "4  0.534094 -0.716923  ...                  0                       0\n",
              "\n",
              "[5 rows x 101 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VV6oEABXxoBM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "3c1b7761-814f-478d-f465-863c3fefab76"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression()\n",
        "lr.fit(X_train, y_train)\n",
        "predictions = lr.predict_proba(X_test)\n",
        "fpr, tpr, thresholds = roc_curve(y_test, predictions[:, 1])\n",
        "roc_auc = auc(fpr, tpr)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30vH3DiV2pck",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "c9664fbb-debf-4401-9387-b1eed07f17bb"
      },
      "source": [
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr, tpr, color='darkorange',\n",
        "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver operating characteristic example')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DqVKBDkenCooniWIwUBKr6Y7XgDLAQ94MqtBxv8Op0yfOxaFUAdq9+wT9+89l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\nD9aLBqq7jVezp6UKA5oAS+0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kISGBkJAQPvvsMxo0aMDUqVP5/vvviYuLIyUlhV9//ZW3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\nRaEIIYRQUxSKEEIINUWhCCGEUNPHjeViQFt0UgkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjoAVEm75XKr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train, y_train)\n",
        "predictions = rf.predict_proba(X_test)\n",
        "fpr, tpr, thresholds = roc_curve(y_test, predictions[:, 1])\n",
        "roc_auc = auc(fpr, tpr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HqRz91m95qwt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "2e200669-932b-48bd-9d88-9fc73dce3b8c"
      },
      "source": [
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr, tpr, color='darkorange',\n",
        "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver operating characteristic example')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iCHDniiJZRPyAbSLSC9gLRHg2LC/19xjYOQtCIqHbEihR2emIlPIoYwzffbeO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\n/gPYH1liEr/0GQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}