Groupe2.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "Groupe2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/D-kn/AdultDataset-Income-prediction/blob/main/Groupe2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_-D-iMdK-Pkg"
      },
      "source": [
        "##**Importation des librairies nécessaires**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZoQG2booXW_"
      },
      "source": [
        "import pandas as pd # Analyse, nettoyage et préparation des données\n",
        "import numpy as np # Manipulation des matrices ou tableaux \n",
        "import seaborn as sns # Création de tracées statistiques\n",
        "from matplotlib import pyplot as plt # Manipulation des données sous formes de graphiques\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn import preprocessing \n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6nIi2XfoucT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cad3abb8-7bd7-4462-b9cc-7a25a87e4a57"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CscFfIl9vg0"
      },
      "source": [
        "##**Importation de la dataset et ajout des noms de chaque colonne**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "UYk8OA-eoXXG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "fb06afe7-b76b-4755-9115-55524f8d9818"
      },
      "source": [
        "# Importation dataset\n",
        "file_path = '/content/drive/MyDrive/TP_APPRENTISSAGE/dat/adult.xls'\n",
        "fichier = pd.read_csv(file_path,\n",
        "                  names=[\"age\", \"workclass\", \"fnlwgt\", \"education\",\"education-num\", \"marital-status\", \n",
        "                         \"occupation\", \"relationship\", \"race\", \"sex\", \"capital-gain\", \"capital-loss\",\n",
        "                         \"hours-per-week\", \"native-country\",\"classification\"])\n",
        "dataset = fichier.rename(columns={'education-num': 'education_num','marital-status': 'marital_status',\n",
        "                                  'capital-gain': 'capital_gain', 'capital-loss': 'capital_loss',\n",
        "                                  'hours-per-week': 'hours_per_week', 'native-country': 'native_country'})\n",
        "dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education_num</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>native_country</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</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>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</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>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32556</th>\n",
              "      <td>27</td>\n",
              "      <td>Private</td>\n",
              "      <td>257302</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Tech-support</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32557</th>\n",
              "      <td>40</td>\n",
              "      <td>Private</td>\n",
              "      <td>154374</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Machine-op-inspct</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>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32558</th>\n",
              "      <td>58</td>\n",
              "      <td>Private</td>\n",
              "      <td>151910</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32559</th>\n",
              "      <td>22</td>\n",
              "      <td>Private</td>\n",
              "      <td>201490</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32560</th>\n",
              "      <td>52</td>\n",
              "      <td>Self-emp-inc</td>\n",
              "      <td>287927</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>15024</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32561 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       age          workclass  ...  native_country classification\n",
              "0       39          State-gov  ...   United-States          <=50K\n",
              "1       50   Self-emp-not-inc  ...   United-States          <=50K\n",
              "2       38            Private  ...   United-States          <=50K\n",
              "3       53            Private  ...   United-States          <=50K\n",
              "4       28            Private  ...            Cuba          <=50K\n",
              "...    ...                ...  ...             ...            ...\n",
              "32556   27            Private  ...   United-States          <=50K\n",
              "32557   40            Private  ...   United-States           >50K\n",
              "32558   58            Private  ...   United-States          <=50K\n",
              "32559   22            Private  ...   United-States          <=50K\n",
              "32560   52       Self-emp-inc  ...   United-States           >50K\n",
              "\n",
              "[32561 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 486
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bJuOtFsf-1io"
      },
      "source": [
        "##**Taille de la dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2U-rRh_U-82x",
        "outputId": "e8c9cc09-7a81-4f60-e421-fbefe347114e"
      },
      "source": [
        "dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32561, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 487
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z32hzjTo_ABa"
      },
      "source": [
        "##**Liste des colonnes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7iPRsa59_HtG",
        "outputId": "39c18dcc-3b85-4cde-e6e4-0597bb8d9db1"
      },
      "source": [
        "dataset.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'workclass', 'fnlwgt', 'education', 'education_num',\n",
              "       'marital_status', 'occupation', 'relationship', 'race', 'sex',\n",
              "       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',\n",
              "       'classification'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 488
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9RpPVh1AnlK"
      },
      "source": [
        "##**Affichage du types de chaque variables**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2aDAqO7-oXXb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b4b0dd7-3aa8-4e35-a646-0957b44b946f"
      },
      "source": [
        "dataset.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                int64\n",
              "workclass         object\n",
              "fnlwgt             int64\n",
              "education         object\n",
              "education_num      int64\n",
              "marital_status    object\n",
              "occupation        object\n",
              "relationship      object\n",
              "race              object\n",
              "sex               object\n",
              "capital_gain       int64\n",
              "capital_loss       int64\n",
              "hours_per_week     int64\n",
              "native_country    object\n",
              "classification    object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 489
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWpC_eoQO1F9"
      },
      "source": [
        "##**Vérification des données manqantes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMxMKQ34O9Qh",
        "outputId": "7d9b0775-1e1b-4871-e9b9-8affc4e44f3a"
      },
      "source": [
        "dataset[dataset.isnull().any(axis=1)].count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "workclass         0\n",
              "fnlwgt            0\n",
              "education         0\n",
              "education_num     0\n",
              "marital_status    0\n",
              "occupation        0\n",
              "relationship      0\n",
              "race              0\n",
              "sex               0\n",
              "capital_gain      0\n",
              "capital_loss      0\n",
              "hours_per_week    0\n",
              "native_country    0\n",
              "classification    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 490
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6jajbFQ_N_z"
      },
      "source": [
        "##**Affichage des 5 premieres lignes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Z6OzaX4X_KT7",
        "outputId": "79fd0655-de60-4365-93a9-5630230aa1e3"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education_num</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>native_country</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</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>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</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>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age          workclass  ...  native_country classification\n",
              "0   39          State-gov  ...   United-States          <=50K\n",
              "1   50   Self-emp-not-inc  ...   United-States          <=50K\n",
              "2   38            Private  ...   United-States          <=50K\n",
              "3   53            Private  ...   United-States          <=50K\n",
              "4   28            Private  ...            Cuba          <=50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 491
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BpTfjhVV_ZlP"
      },
      "source": [
        "##**Affichage des 5 dernieres lignes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Im6sx1ljoXXM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "156a599f-f598-4a61-8313-d22a0f88da92"
      },
      "source": [
        "dataset.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education_num</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>native_country</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>32556</th>\n",
              "      <td>27</td>\n",
              "      <td>Private</td>\n",
              "      <td>257302</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Tech-support</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32557</th>\n",
              "      <td>40</td>\n",
              "      <td>Private</td>\n",
              "      <td>154374</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Machine-op-inspct</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>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32558</th>\n",
              "      <td>58</td>\n",
              "      <td>Private</td>\n",
              "      <td>151910</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32559</th>\n",
              "      <td>22</td>\n",
              "      <td>Private</td>\n",
              "      <td>201490</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32560</th>\n",
              "      <td>52</td>\n",
              "      <td>Self-emp-inc</td>\n",
              "      <td>287927</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>15024</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age      workclass  ...  native_country classification\n",
              "32556   27        Private  ...   United-States          <=50K\n",
              "32557   40        Private  ...   United-States           >50K\n",
              "32558   58        Private  ...   United-States          <=50K\n",
              "32559   22        Private  ...   United-States          <=50K\n",
              "32560   52   Self-emp-inc  ...   United-States           >50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 492
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BOktZueu_rom"
      },
      "source": [
        "##**Visualissation de certains détails statistiques de base de notre dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "w4hIOTEPoXXV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "52dff5a3-ebd2-4968-8e5d-3d296a9dcba3"
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education_num</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>32561.000000</td>\n",
              "      <td>3.256100e+04</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.581647</td>\n",
              "      <td>1.897784e+05</td>\n",
              "      <td>10.080679</td>\n",
              "      <td>1077.648844</td>\n",
              "      <td>87.303830</td>\n",
              "      <td>40.437456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.640433</td>\n",
              "      <td>1.055500e+05</td>\n",
              "      <td>2.572720</td>\n",
              "      <td>7385.292085</td>\n",
              "      <td>402.960219</td>\n",
              "      <td>12.347429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>1.228500e+04</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>1.178270e+05</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>1.783560e+05</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>2.370510e+05</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>1.484705e+06</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>99999.000000</td>\n",
              "      <td>4356.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age        fnlwgt  ...  capital_loss  hours_per_week\n",
              "count  32561.000000  3.256100e+04  ...  32561.000000    32561.000000\n",
              "mean      38.581647  1.897784e+05  ...     87.303830       40.437456\n",
              "std       13.640433  1.055500e+05  ...    402.960219       12.347429\n",
              "min       17.000000  1.228500e+04  ...      0.000000        1.000000\n",
              "25%       28.000000  1.178270e+05  ...      0.000000       40.000000\n",
              "50%       37.000000  1.783560e+05  ...      0.000000       40.000000\n",
              "75%       48.000000  2.370510e+05  ...      0.000000       45.000000\n",
              "max       90.000000  1.484705e+06  ...   4356.000000       99.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 493
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQcr3VL4BaHV"
      },
      "source": [
        "##**Visualisation de certaines variables de notre dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96_QSHJDoXXd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9945df24-7dcc-4a0f-858c-72ad730cd3d7"
      },
      "source": [
        "dataset['sex'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Male      21790\n",
              " Female    10771\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 494
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ExUcNJEyBtbA",
        "outputId": "ce23bfca-9f4f-4039-fbe2-52f59434d9cf"
      },
      "source": [
        "x = dataset['sex']\n",
        "plt.hist(x)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_iopSgaoXXh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "853b2249-be8f-424d-81a0-6ef78f9bc915"
      },
      "source": [
        "dataset['marital_status'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Married-civ-spouse       14976\n",
              " Never-married            10683\n",
              " Divorced                  4443\n",
              " Separated                 1025\n",
              " Widowed                    993\n",
              " Married-spouse-absent      418\n",
              " Married-AF-spouse           23\n",
              "Name: marital_status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 496
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvB9y7D8oXXl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "516fa5e8-fcf4-45af-86ee-c540f3e91a92"
      },
      "source": [
        "x = dataset['marital_status']\n",
        "plt.hist(x)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZzwaRouPCQeZ"
      },
      "source": [
        "##**Corrélation entre les variables**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xLvYFHroXXv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "027343ad-0644-49a1-ff0a-1b59c43ab356"
      },
      "source": [
        "dataset.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education_num</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.076646</td>\n",
              "      <td>0.036527</td>\n",
              "      <td>0.077674</td>\n",
              "      <td>0.057775</td>\n",
              "      <td>0.068756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fnlwgt</th>\n",
              "      <td>-0.076646</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.043195</td>\n",
              "      <td>0.000432</td>\n",
              "      <td>-0.010252</td>\n",
              "      <td>-0.018768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_num</th>\n",
              "      <td>0.036527</td>\n",
              "      <td>-0.043195</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.122630</td>\n",
              "      <td>0.079923</td>\n",
              "      <td>0.148123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital_gain</th>\n",
              "      <td>0.077674</td>\n",
              "      <td>0.000432</td>\n",
              "      <td>0.122630</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.031615</td>\n",
              "      <td>0.078409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital_loss</th>\n",
              "      <td>0.057775</td>\n",
              "      <td>-0.010252</td>\n",
              "      <td>0.079923</td>\n",
              "      <td>-0.031615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.054256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hours_per_week</th>\n",
              "      <td>0.068756</td>\n",
              "      <td>-0.018768</td>\n",
              "      <td>0.148123</td>\n",
              "      <td>0.078409</td>\n",
              "      <td>0.054256</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     age    fnlwgt  ...  capital_loss  hours_per_week\n",
              "age             1.000000 -0.076646  ...      0.057775        0.068756\n",
              "fnlwgt         -0.076646  1.000000  ...     -0.010252       -0.018768\n",
              "education_num   0.036527 -0.043195  ...      0.079923        0.148123\n",
              "capital_gain    0.077674  0.000432  ...     -0.031615        0.078409\n",
              "capital_loss    0.057775 -0.010252  ...      1.000000        0.054256\n",
              "hours_per_week  0.068756 -0.018768  ...      0.054256        1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 498
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6HaBHEUEoXX3"
      },
      "source": [
        "## **Analyse des composantes principales**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SBt9c6QlHkql",
        "outputId": "0aee2d6e-7a28-47fb-d638-ba7468cb47f6"
      },
      "source": [
        "# PCA pourcentage de perte\n",
        "x = dataset.select_dtypes(include=\"int\")\n",
        "print(\"Nombre de variales quantitative:\", len(x.columns))\n",
        "x = StandardScaler().fit_transform(x)\n",
        "component_var = {}\n",
        "for i in range(2, 7):\n",
        "    pca = PCA(n_components=i)\n",
        "    res = pca.fit_transform(x)\n",
        "    print('Nombre de composants: ', i)\n",
        "    principalComponents = pca.fit_transform(x)\n",
        "    principalDf = pd.DataFrame(data = principalComponents)\n",
        "    pca.explained_variance_ratio_\n",
        "    print(\"Pourcentage de perte :\" +str(sum(pca.explained_variance_ratio_)*100))\n",
        "    component_var[i] = sum(pca.explained_variance_ratio_)\n",
        "print(component_var)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nombre de variales quantitative: 6\n",
            "Nombre de composants:  2\n",
            "Pourcentage de perte :39.193318016776956\n",
            "Nombre de composants:  3\n",
            "Pourcentage de perte :56.16997477923884\n",
            "Nombre de composants:  4\n",
            "Pourcentage de perte :71.86650763979159\n",
            "Nombre de composants:  5\n",
            "Pourcentage de perte :86.64056569927166\n",
            "Nombre de composants:  6\n",
            "Pourcentage de perte :100.0\n",
            "{2: 0.39193318016776957, 3: 0.5616997477923884, 4: 0.718665076397916, 5: 0.8664056569927167, 6: 1.0}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5h0ji50NNxr"
      },
      "source": [
        "###**Graphe de perte en fonction du nombre de composant**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "jAKPY8GcImcE",
        "outputId": "a692f48e-b6f2-4bfd-834d-49f22b691824"
      },
      "source": [
        "# Variables quantitatives\n",
        "x = dataset.select_dtypes(include=\"int\")\n",
        "# Instanciation pour la standardisation \n",
        "sc = StandardScaler()\n",
        "# Normalisation: centrage-réduction\n",
        "data_acp_standardise = sc.fit_transform(x)\n",
        "pca = PCA().fit(data_acp_standardise)\n",
        "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
        "plt.xlim(0,6,1)\n",
        "plt.xlabel('Nombre de composants')\n",
        "plt.ylabel('Variance cumulative expliquée')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Variance cumulative expliquée')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 500
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjfWH8RAoXYF"
      },
      "source": [
        "##**Normalisation de certaines variables de notre datatset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqyZOhXgK-tq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff9ad68f-6131-423c-f4aa-e5bd0da57a5e"
      },
      "source": [
        "# Reduction du nombre de column de la variable native_country\n",
        "dataset.native_country.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " United-States                 29170\n",
              " Mexico                          643\n",
              " ?                               583\n",
              " Philippines                     198\n",
              " Germany                         137\n",
              " Canada                          121\n",
              " Puerto-Rico                     114\n",
              " El-Salvador                     106\n",
              " India                           100\n",
              " Cuba                             95\n",
              " England                          90\n",
              " Jamaica                          81\n",
              " South                            80\n",
              " China                            75\n",
              " Italy                            73\n",
              " Dominican-Republic               70\n",
              " Vietnam                          67\n",
              " Guatemala                        64\n",
              " Japan                            62\n",
              " Poland                           60\n",
              " Columbia                         59\n",
              " Taiwan                           51\n",
              " Haiti                            44\n",
              " Iran                             43\n",
              " Portugal                         37\n",
              " Nicaragua                        34\n",
              " Peru                             31\n",
              " Greece                           29\n",
              " France                           29\n",
              " Ecuador                          28\n",
              " Ireland                          24\n",
              " Hong                             20\n",
              " Cambodia                         19\n",
              " Trinadad&Tobago                  19\n",
              " Laos                             18\n",
              " Thailand                         18\n",
              " Yugoslavia                       16\n",
              " Outlying-US(Guam-USVI-etc)       14\n",
              " Hungary                          13\n",
              " Honduras                         13\n",
              " Scotland                         12\n",
              " Holand-Netherlands                1\n",
              "Name: native_country, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 501
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyYylY5SQGgF",
        "outputId": "ae09e858-6909-4d7f-a940-c6a104cbdbef"
      },
      "source": [
        "taille_dataset = dataset.shape[0]\n",
        "for i in range(taille_dataset):\n",
        "  if ((dataset.iloc[i,13] == ' United-States') or (dataset.iloc[i,13] == 1) ):\n",
        "    dataset.at[i,'native_country'] = 1 \n",
        "  else:\n",
        "    dataset.at[i,'native_country'] = 0\n",
        "dataset.native_country.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    29170\n",
              "0     3391\n",
              "Name: native_country, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 502
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Py-aeOKoXYQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ae34849-9bd8-460f-ce91-552dd154c5fc"
      },
      "source": [
        "dataset.occupation.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Prof-specialty       4140\n",
              " Craft-repair         4099\n",
              " Exec-managerial      4066\n",
              " Adm-clerical         3770\n",
              " Sales                3650\n",
              " Other-service        3295\n",
              " Machine-op-inspct    2002\n",
              " ?                    1843\n",
              " Transport-moving     1597\n",
              " Handlers-cleaners    1370\n",
              " Farming-fishing       994\n",
              " Tech-support          928\n",
              " Protective-serv       649\n",
              " Priv-house-serv       149\n",
              " Armed-Forces            9\n",
              "Name: occupation, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 503
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVaMkwmmoXYW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6400f50e-958d-4a1e-bc5a-0d717f4a069d"
      },
      "source": [
        "#Ajout des occupations Protective-serv, Priv-house-serv Armed-Forces sur la nouvelle variable Other\n",
        "dataset['occupation'] = dataset.occupation.str.replace('Priv-house-serv', 'Other').str.replace('Armed-Forces', 'Other').str.replace('Protective-serv', 'Other').str.replace('?', 'Other')\n",
        "dataset.occupation.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Prof-specialty       4140\n",
              " Craft-repair         4099\n",
              " Exec-managerial      4066\n",
              " Adm-clerical         3770\n",
              " Sales                3650\n",
              " Other-service        3295\n",
              " Other                2650\n",
              " Machine-op-inspct    2002\n",
              " Transport-moving     1597\n",
              " Handlers-cleaners    1370\n",
              " Farming-fishing       994\n",
              " Tech-support          928\n",
              "Name: occupation, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 504
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfZ3j7U0oXYa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f56e7697-617a-4fc9-da10-ebc0aa0ca0a5"
      },
      "source": [
        "dataset.race.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " White                 27816\n",
              " Black                  3124\n",
              " Asian-Pac-Islander     1039\n",
              " Amer-Indian-Eskimo      311\n",
              " Other                   271\n",
              "Name: race, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 505
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGfB-NccoXYd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73b1e25c-459a-4fd3-8472-bcfd748d3479"
      },
      "source": [
        "#Ajout des amérindiens sur Other\n",
        "dataset['race'] = dataset.race.str.replace('Other', 'Other').str.replace('Amer-Indian-Eskimo', 'Other')\n",
        "dataset.race.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " White                 27816\n",
              " Black                  3124\n",
              " Asian-Pac-Islander     1039\n",
              " Other                   582\n",
              "Name: race, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 506
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yItCB6QloXYf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43085701-b402-4fa0-d8c4-092e6c5f6fbe"
      },
      "source": [
        "dataset.marital_status.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Married-civ-spouse       14976\n",
              " Never-married            10683\n",
              " Divorced                  4443\n",
              " Separated                 1025\n",
              " Widowed                    993\n",
              " Married-spouse-absent      418\n",
              " Married-AF-spouse           23\n",
              "Name: marital_status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 507
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYIXDUAVoXYk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c02eb3c-9674-40f0-84d2-11afc81c714b"
      },
      "source": [
        "#Ajout des statuts Married-spouse-absent Married-AF-spouse sur Other\n",
        "dataset['marital_status'] = dataset.marital_status.str.replace('Married-spouse-absent', 'Other').str.replace('Married-AF-spouse', 'Other')\n",
        "dataset.marital_status.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Married-civ-spouse    14976\n",
              " Never-married         10683\n",
              " Divorced               4443\n",
              " Separated              1025\n",
              " Widowed                 993\n",
              " Other                   441\n",
              "Name: marital_status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 508
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cLFmmlD4oXYm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73726257-0ddc-4b7a-dc46-12f1a1e3f077"
      },
      "source": [
        "dataset.workclass.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Private             22696\n",
              " Self-emp-not-inc     2541\n",
              " Local-gov            2093\n",
              " ?                    1836\n",
              " State-gov            1298\n",
              " Self-emp-inc         1116\n",
              " Federal-gov           960\n",
              " Without-pay            14\n",
              " Never-worked            7\n",
              "Name: workclass, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 509
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IibOd2WKoXYp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d4ccfa4-95fe-4085-bb5a-2a4bb2a9125d"
      },
      "source": [
        "#Ajout des workclass Without-pay Never-worked sur Self-emp-inc\n",
        "dataset['workclass'] = dataset.workclass.str.replace('Self-emp-inc', 'Self-emp-inc').str.replace('Without-pay', 'Self-emp-inc').str.replace('Never-worked', 'Self-emp-inc')\n",
        "dataset.workclass.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Private             22696\n",
              " Self-emp-not-inc     2541\n",
              " Local-gov            2093\n",
              " ?                    1836\n",
              " State-gov            1298\n",
              " Self-emp-inc         1137\n",
              " Federal-gov           960\n",
              "Name: workclass, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 510
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyddT80coXYr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f74d7031-9b99-438c-81a3-83c227f23235"
      },
      "source": [
        "dataset.sex.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Male      21790\n",
              " Female    10771\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 511
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lt2k4EzoXYu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac932f4b-264f-4f39-8455-3185d4ddac37"
      },
      "source": [
        "#Ajout des sex Female = 0  et Male = 1\n",
        "dataset['sex'] = dataset.sex.str.replace('Female', '0').str.replace('Male', '1')\n",
        "dataset.sex.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 1    21790\n",
              " 0    10771\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 512
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJiLMpIPoXYw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2fa764d-5f2e-48c3-8195-e1d00c36f7c5"
      },
      "source": [
        "dataset.education_num.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9     10501\n",
              "10     7291\n",
              "13     5355\n",
              "14     1723\n",
              "11     1382\n",
              "7      1175\n",
              "12     1067\n",
              "6       933\n",
              "4       646\n",
              "15      576\n",
              "5       514\n",
              "8       433\n",
              "16      413\n",
              "3       333\n",
              "2       168\n",
              "1        51\n",
              "Name: education_num, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 513
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTsv34FIoXYz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "3b7cc9c3-9c0c-4dc7-be88-f6df54baf094"
      },
      "source": [
        "#Suppression de la colonne education_num car elle ne donne pas d'information\n",
        "dataset.drop('education_num', axis=1,inplace = True)\n",
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>native_country</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age          workclass  fnlwgt  ... hours_per_week native_country classification\n",
              "0   39          State-gov   77516  ...             40              1          <=50K\n",
              "1   50   Self-emp-not-inc   83311  ...             13              1          <=50K\n",
              "2   38            Private  215646  ...             40              1          <=50K\n",
              "3   53            Private  234721  ...             40              1          <=50K\n",
              "4   28            Private  338409  ...             40              0          <=50K\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 514
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yFpaNxRmJFx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P4DBQPR1TxJu"
      },
      "source": [
        "##**Transformation de nos variables qualitative en numérique**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4of0_rlaQR5"
      },
      "source": [
        "#Transformation des variables qui n'ont que pour valeur 1 ou 0\n",
        "categories = ['sex', 'native_country', 'classification'] \n",
        "#objList = dataset.select_dtypes(include = \"object\").columns\n",
        "le = preprocessing.LabelEncoder()\n",
        "for cat in categories:\n",
        "    dataset[cat] = le.fit_transform(dataset[cat].astype(str))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0MZdt4uDVwzx",
        "outputId": "b94ffc68-920b-42e4-c9bb-1cf5f8786dcc"
      },
      "source": [
        "dataset.head"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of        age          workclass  ...  native_country classification\n",
              "0       39          State-gov  ...               1              0\n",
              "1       50   Self-emp-not-inc  ...               1              0\n",
              "2       38            Private  ...               1              0\n",
              "3       53            Private  ...               1              0\n",
              "4       28            Private  ...               0              0\n",
              "...    ...                ...  ...             ...            ...\n",
              "32556   27            Private  ...               1              0\n",
              "32557   40            Private  ...               1              1\n",
              "32558   58            Private  ...               1              0\n",
              "32559   22            Private  ...               1              0\n",
              "32560   52       Self-emp-inc  ...               1              1\n",
              "\n",
              "[32561 rows x 14 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 516
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "dfQSsQinVw93",
        "outputId": "c93f3f39-159f-4523-9276-0e8dc2ec284a"
      },
      "source": [
        "dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>native_country</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32556</th>\n",
              "      <td>27</td>\n",
              "      <td>Private</td>\n",
              "      <td>257302</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Tech-support</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32557</th>\n",
              "      <td>40</td>\n",
              "      <td>Private</td>\n",
              "      <td>154374</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32558</th>\n",
              "      <td>58</td>\n",
              "      <td>Private</td>\n",
              "      <td>151910</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32559</th>\n",
              "      <td>22</td>\n",
              "      <td>Private</td>\n",
              "      <td>201490</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32560</th>\n",
              "      <td>52</td>\n",
              "      <td>Self-emp-inc</td>\n",
              "      <td>287927</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>0</td>\n",
              "      <td>15024</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32561 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       age          workclass  ...  native_country classification\n",
              "0       39          State-gov  ...               1              0\n",
              "1       50   Self-emp-not-inc  ...               1              0\n",
              "2       38            Private  ...               1              0\n",
              "3       53            Private  ...               1              0\n",
              "4       28            Private  ...               0              0\n",
              "...    ...                ...  ...             ...            ...\n",
              "32556   27            Private  ...               1              0\n",
              "32557   40            Private  ...               1              1\n",
              "32558   58            Private  ...               1              0\n",
              "32559   22            Private  ...               1              0\n",
              "32560   52       Self-emp-inc  ...               1              1\n",
              "\n",
              "[32561 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 517
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qOpLDu_4BB0i",
        "outputId": "6d3df34d-b31e-429f-bf0f-d247d64c6813"
      },
      "source": [
        "dataset.classification.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    24720\n",
              "1     7841\n",
              "Name: classification, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 518
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "-dx73c7lVxF5",
        "outputId": "a3578666-2853-4cdf-d310-1a496fb0c115"
      },
      "source": [
        "#Dummy\n",
        "dataset_with_dummies = pd.get_dummies(data=dataset, columns = [ 'workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race',], \n",
        "                                     prefix = ['workclass','education', 'marital_status', 'occupation', 'relationship', 'race',])\n",
        "print(dataset_with_dummies.shape)\n",
        "dataset_with_dummies.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32561, 59)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>native_country</th>\n",
              "      <th>classification</th>\n",
              "      <th>workclass_ ?</th>\n",
              "      <th>workclass_ Federal-gov</th>\n",
              "      <th>workclass_ Local-gov</th>\n",
              "      <th>workclass_ Private</th>\n",
              "      <th>workclass_ Self-emp-inc</th>\n",
              "      <th>workclass_ Self-emp-not-inc</th>\n",
              "      <th>workclass_ State-gov</th>\n",
              "      <th>education_ 10th</th>\n",
              "      <th>education_ 11th</th>\n",
              "      <th>education_ 12th</th>\n",
              "      <th>education_ 1st-4th</th>\n",
              "      <th>education_ 5th-6th</th>\n",
              "      <th>education_ 7th-8th</th>\n",
              "      <th>education_ 9th</th>\n",
              "      <th>education_ Assoc-acdm</th>\n",
              "      <th>education_ Assoc-voc</th>\n",
              "      <th>education_ Bachelors</th>\n",
              "      <th>education_ Doctorate</th>\n",
              "      <th>education_ HS-grad</th>\n",
              "      <th>education_ Masters</th>\n",
              "      <th>education_ Preschool</th>\n",
              "      <th>education_ Prof-school</th>\n",
              "      <th>education_ Some-college</th>\n",
              "      <th>marital_status_ Divorced</th>\n",
              "      <th>marital_status_ Married-civ-spouse</th>\n",
              "      <th>marital_status_ Never-married</th>\n",
              "      <th>marital_status_ Other</th>\n",
              "      <th>marital_status_ Separated</th>\n",
              "      <th>marital_status_ Widowed</th>\n",
              "      <th>occupation_ Adm-clerical</th>\n",
              "      <th>occupation_ Craft-repair</th>\n",
              "      <th>occupation_ Exec-managerial</th>\n",
              "      <th>occupation_ Farming-fishing</th>\n",
              "      <th>occupation_ Handlers-cleaners</th>\n",
              "      <th>occupation_ Machine-op-inspct</th>\n",
              "      <th>occupation_ Other</th>\n",
              "      <th>occupation_ Other-service</th>\n",
              "      <th>occupation_ Prof-specialty</th>\n",
              "      <th>occupation_ Sales</th>\n",
              "      <th>occupation_ Tech-support</th>\n",
              "      <th>occupation_ Transport-moving</th>\n",
              "      <th>relationship_ Husband</th>\n",
              "      <th>relationship_ Not-in-family</th>\n",
              "      <th>relationship_ Other-relative</th>\n",
              "      <th>relationship_ Own-child</th>\n",
              "      <th>relationship_ Unmarried</th>\n",
              "      <th>relationship_ Wife</th>\n",
              "      <th>race_ Asian-Pac-Islander</th>\n",
              "      <th>race_ Black</th>\n",
              "      <th>race_ Other</th>\n",
              "      <th>race_ White</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>83311</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>215646</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>234721</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <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>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",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>338409</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>40</td>\n",
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              "      <td>1</td>\n",
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              "      <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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  fnlwgt  sex  ...  race_ Black  race_ Other  race_ White\n",
              "0   39   77516    1  ...            0            0            1\n",
              "1   50   83311    1  ...            0            0            1\n",
              "2   38  215646    1  ...            0            0            1\n",
              "3   53  234721    1  ...            1            0            0\n",
              "4   28  338409    0  ...            1            0            0\n",
              "\n",
              "[5 rows x 59 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 519
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nB4isCAsvx--",
        "outputId": "4c995926-d6a8-411c-f05a-8503f0cd3ebc"
      },
      "source": [
        "# The Correltion matrix\n",
        "corr = dataset_with_dummies.corr()\n",
        "corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>native_country</th>\n",
              "      <th>classification</th>\n",
              "      <th>workclass_ ?</th>\n",
              "      <th>workclass_ Federal-gov</th>\n",
              "      <th>workclass_ Local-gov</th>\n",
              "      <th>workclass_ Private</th>\n",
              "      <th>workclass_ Self-emp-inc</th>\n",
              "      <th>workclass_ Self-emp-not-inc</th>\n",
              "      <th>workclass_ State-gov</th>\n",
              "      <th>education_ 10th</th>\n",
              "      <th>education_ 11th</th>\n",
              "      <th>education_ 12th</th>\n",
              "      <th>education_ 1st-4th</th>\n",
              "      <th>education_ 5th-6th</th>\n",
              "      <th>education_ 7th-8th</th>\n",
              "      <th>education_ 9th</th>\n",
              "      <th>education_ Assoc-acdm</th>\n",
              "      <th>education_ Assoc-voc</th>\n",
              "      <th>education_ Bachelors</th>\n",
              "      <th>education_ Doctorate</th>\n",
              "      <th>education_ HS-grad</th>\n",
              "      <th>education_ Masters</th>\n",
              "      <th>education_ Preschool</th>\n",
              "      <th>education_ Prof-school</th>\n",
              "      <th>education_ Some-college</th>\n",
              "      <th>marital_status_ Divorced</th>\n",
              "      <th>marital_status_ Married-civ-spouse</th>\n",
              "      <th>marital_status_ Never-married</th>\n",
              "      <th>marital_status_ Other</th>\n",
              "      <th>marital_status_ Separated</th>\n",
              "      <th>marital_status_ Widowed</th>\n",
              "      <th>occupation_ Adm-clerical</th>\n",
              "      <th>occupation_ Craft-repair</th>\n",
              "      <th>occupation_ Exec-managerial</th>\n",
              "      <th>occupation_ Farming-fishing</th>\n",
              "      <th>occupation_ Handlers-cleaners</th>\n",
              "      <th>occupation_ Machine-op-inspct</th>\n",
              "      <th>occupation_ Other</th>\n",
              "      <th>occupation_ Other-service</th>\n",
              "      <th>occupation_ Prof-specialty</th>\n",
              "      <th>occupation_ Sales</th>\n",
              "      <th>occupation_ Tech-support</th>\n",
              "      <th>occupation_ Transport-moving</th>\n",
              "      <th>relationship_ Husband</th>\n",
              "      <th>relationship_ Not-in-family</th>\n",
              "      <th>relationship_ Other-relative</th>\n",
              "      <th>relationship_ Own-child</th>\n",
              "      <th>relationship_ Unmarried</th>\n",
              "      <th>relationship_ Wife</th>\n",
              "      <th>race_ Asian-Pac-Islander</th>\n",
              "      <th>race_ Black</th>\n",
              "      <th>race_ Other</th>\n",
              "      <th>race_ White</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.076646</td>\n",
              "      <td>0.088832</td>\n",
              "      <td>0.077674</td>\n",
              "      <td>0.057775</td>\n",
              "      <td>0.068756</td>\n",
              "      <td>0.015917</td>\n",
              "      <td>0.234037</td>\n",
              "      <td>0.042627</td>\n",
              "      <td>0.051227</td>\n",
              "      <td>0.060901</td>\n",
              "      <td>-0.198388</td>\n",
              "      <td>0.101808</td>\n",
              "      <td>0.136252</td>\n",
              "      <td>0.012763</td>\n",
              "      <td>-0.014504</td>\n",
              "      <td>-0.088314</td>\n",
              "      <td>-0.056016</td>\n",
              "      <td>0.039921</td>\n",
              "      <td>0.032076</td>\n",
              "      <td>0.102886</td>\n",
              "      <td>0.023014</td>\n",
              "      <td>-0.016196</td>\n",
              "      <td>-0.000434</td>\n",
              "      <td>0.010516</td>\n",
              "      <td>0.075787</td>\n",
              "      <td>0.019870</td>\n",
              "      <td>0.094761</td>\n",
              "      <td>0.012146</td>\n",
              "      <td>0.060651</td>\n",
              "      <td>-0.111262</td>\n",
              "      <td>0.129975</td>\n",
              "      <td>0.315679</td>\n",
              "      <td>-0.534359</td>\n",
              "      <td>0.013528</td>\n",
              "      <td>0.010210</td>\n",
              "      <td>0.265177</td>\n",
              "      <td>-0.042902</td>\n",
              "      <td>0.012515</td>\n",
              "      <td>0.099352</td>\n",
              "      <td>0.034210</td>\n",
              "      <td>-0.098579</td>\n",
              "      <td>-0.016257</td>\n",
              "      <td>0.040149</td>\n",
              "      <td>-0.089346</td>\n",
              "      <td>0.054170</td>\n",
              "      <td>-0.031980</td>\n",
              "      <td>-0.019576</td>\n",
              "      <td>0.026909</td>\n",
              "      <td>0.316875</td>\n",
              "      <td>-0.010106</td>\n",
              "      <td>-0.070002</td>\n",
              "      <td>-0.432918</td>\n",
              "      <td>0.043188</td>\n",
              "      <td>0.020854</td>\n",
              "      <td>-0.011111</td>\n",
              "      <td>-0.019434</td>\n",
              "      <td>-0.031039</td>\n",
              "      <td>0.033412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fnlwgt</th>\n",
              "      <td>-0.076646</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.026858</td>\n",
              "      <td>0.000432</td>\n",
              "      <td>-0.010252</td>\n",
              "      <td>-0.018768</td>\n",
              "      <td>-0.075287</td>\n",
              "      <td>-0.009463</td>\n",
              "      <td>-0.002923</td>\n",
              "      <td>-0.007525</td>\n",
              "      <td>-0.002828</td>\n",
              "      <td>0.042907</td>\n",
              "      <td>-0.024348</td>\n",
              "      <td>-0.039058</td>\n",
              "      <td>-0.010891</td>\n",
              "      <td>0.011479</td>\n",
              "      <td>0.009440</td>\n",
              "      <td>0.010250</td>\n",
              "      <td>0.033791</td>\n",
              "      <td>0.041094</td>\n",
              "      <td>-0.002290</td>\n",
              "      <td>0.015246</td>\n",
              "      <td>0.006358</td>\n",
              "      <td>-0.015643</td>\n",
              "      <td>-0.007240</td>\n",
              "      <td>-0.003307</td>\n",
              "      <td>-0.001566</td>\n",
              "      <td>-0.022229</td>\n",
              "      <td>0.017303</td>\n",
              "      <td>-0.005231</td>\n",
              "      <td>-0.005269</td>\n",
              "      <td>-0.014973</td>\n",
              "      <td>-0.026363</td>\n",
              "      <td>0.037108</td>\n",
              "      <td>0.003446</td>\n",
              "      <td>0.028467</td>\n",
              "      <td>-0.026246</td>\n",
              "      <td>0.007765</td>\n",
              "      <td>0.008465</td>\n",
              "      <td>-0.019198</td>\n",
              "      <td>-0.032932</td>\n",
              "      <td>0.029015</td>\n",
              "      <td>0.012762</td>\n",
              "      <td>0.008314</td>\n",
              "      <td>-0.003719</td>\n",
              "      <td>-0.016206</td>\n",
              "      <td>0.003728</td>\n",
              "      <td>0.003765</td>\n",
              "      <td>0.001265</td>\n",
              "      <td>-0.021140</td>\n",
              "      <td>0.007503</td>\n",
              "      <td>0.025518</td>\n",
              "      <td>0.013818</td>\n",
              "      <td>0.004400</td>\n",
              "      <td>-0.016897</td>\n",
              "      <td>-0.051323</td>\n",
              "      <td>0.118009</td>\n",
              "      <td>-0.042718</td>\n",
              "      <td>-0.056896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>0.088832</td>\n",
              "      <td>0.026858</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.048480</td>\n",
              "      <td>0.045567</td>\n",
              "      <td>0.229309</td>\n",
              "      <td>-0.006993</td>\n",
              "      <td>0.215980</td>\n",
              "      <td>-0.065556</td>\n",
              "      <td>0.000989</td>\n",
              "      <td>-0.037966</td>\n",
              "      <td>-0.034699</td>\n",
              "      <td>0.083245</td>\n",
              "      <td>0.107451</td>\n",
              "      <td>-0.019895</td>\n",
              "      <td>0.005333</td>\n",
              "      <td>-0.015160</td>\n",
              "      <td>-0.000436</td>\n",
              "      <td>0.008722</td>\n",
              "      <td>0.016969</td>\n",
              "      <td>0.025133</td>\n",
              "      <td>0.013631</td>\n",
              "      <td>-0.024947</td>\n",
              "      <td>-0.013872</td>\n",
              "      <td>0.026837</td>\n",
              "      <td>0.029525</td>\n",
              "      <td>0.011684</td>\n",
              "      <td>0.009902</td>\n",
              "      <td>0.001437</td>\n",
              "      <td>0.048793</td>\n",
              "      <td>-0.061722</td>\n",
              "      <td>-0.228621</td>\n",
              "      <td>0.431805</td>\n",
              "      <td>-0.171434</td>\n",
              "      <td>-0.041292</td>\n",
              "      <td>-0.109135</td>\n",
              "      <td>-0.188487</td>\n",
              "      <td>-0.263148</td>\n",
              "      <td>0.223128</td>\n",
              "      <td>0.036729</td>\n",
              "      <td>0.100097</td>\n",
              "      <td>0.094026</td>\n",
              "      <td>0.030502</td>\n",
              "      <td>-0.043304</td>\n",
              "      <td>-0.153677</td>\n",
              "      <td>-0.028511</td>\n",
              "      <td>-0.011504</td>\n",
              "      <td>-0.016092</td>\n",
              "      <td>0.132468</td>\n",
              "      <td>0.580135</td>\n",
              "      <td>-0.168879</td>\n",
              "      <td>-0.040282</td>\n",
              "      <td>-0.102369</td>\n",
              "      <td>-0.321273</td>\n",
              "      <td>-0.319311</td>\n",
              "      <td>-0.000856</td>\n",
              "      <td>-0.115604</td>\n",
              "      <td>-0.017478</td>\n",
              "      <td>0.103486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital_gain</th>\n",
              "      <td>0.077674</td>\n",
              "      <td>0.000432</td>\n",
              "      <td>0.048480</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.031615</td>\n",
              "      <td>0.078409</td>\n",
              "      <td>0.004599</td>\n",
              "      <td>0.223329</td>\n",
              "      <td>-0.015585</td>\n",
              "      <td>-0.005768</td>\n",
              "      <td>-0.007007</td>\n",
              "      <td>-0.038701</td>\n",
              "      <td>0.095660</td>\n",
              "      <td>0.031847</td>\n",
              "      <td>-0.010373</td>\n",
              "      <td>-0.015653</td>\n",
              "      <td>-0.022598</td>\n",
              "      <td>-0.012474</td>\n",
              "      <td>-0.009281</td>\n",
              "      <td>-0.012410</td>\n",
              "      <td>-0.016254</td>\n",
              "      <td>-0.012614</td>\n",
              "      <td>-0.010898</td>\n",
              "      <td>-0.010337</td>\n",
              "      <td>0.040769</td>\n",
              "      <td>0.056671</td>\n",
              "      <td>-0.046791</td>\n",
              "      <td>0.047527</td>\n",
              "      <td>-0.000961</td>\n",
              "      <td>0.169658</td>\n",
              "      <td>-0.034826</td>\n",
              "      <td>-0.018798</td>\n",
              "      <td>0.085873</td>\n",
              "      <td>-0.066334</td>\n",
              "      <td>-0.006905</td>\n",
              "      <td>-0.013233</td>\n",
              "      <td>-0.012166</td>\n",
              "      <td>-0.028502</td>\n",
              "      <td>-0.022000</td>\n",
              "      <td>0.060618</td>\n",
              "      <td>-0.011724</td>\n",
              "      <td>-0.023272</td>\n",
              "      <td>-0.025957</td>\n",
              "      <td>-0.018866</td>\n",
              "      <td>-0.040271</td>\n",
              "      <td>0.085222</td>\n",
              "      <td>0.011652</td>\n",
              "      <td>-0.009372</td>\n",
              "      <td>-0.018061</td>\n",
              "      <td>0.080175</td>\n",
              "      <td>-0.026489</td>\n",
              "      <td>-0.019046</td>\n",
              "      <td>-0.053601</td>\n",
              "      <td>-0.029004</td>\n",
              "      <td>0.017730</td>\n",
              "      <td>0.009851</td>\n",
              "      <td>-0.020631</td>\n",
              "      <td>-0.005632</td>\n",
              "      <td>0.014429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital_loss</th>\n",
              "      <td>0.057775</td>\n",
              "      <td>-0.010252</td>\n",
              "      <td>0.045567</td>\n",
              "      <td>-0.031615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.054256</td>\n",
              "      <td>0.008784</td>\n",
              "      <td>0.150526</td>\n",
              "      <td>-0.016102</td>\n",
              "      <td>0.010798</td>\n",
              "      <td>0.014668</td>\n",
              "      <td>-0.027460</td>\n",
              "      <td>0.030669</td>\n",
              "      <td>0.021175</td>\n",
              "      <td>-0.002047</td>\n",
              "      <td>-0.012982</td>\n",
              "      <td>-0.017874</td>\n",
              "      <td>-0.015836</td>\n",
              "      <td>-0.006966</td>\n",
              "      <td>-0.004806</td>\n",
              "      <td>-0.007639</td>\n",
              "      <td>-0.018325</td>\n",
              "      <td>0.002793</td>\n",
              "      <td>-0.007602</td>\n",
              "      <td>0.034183</td>\n",
              "      <td>0.049377</td>\n",
              "      <td>-0.028829</td>\n",
              "      <td>0.046586</td>\n",
              "      <td>-0.002046</td>\n",
              "      <td>0.047923</td>\n",
              "      <td>-0.020884</td>\n",
              "      <td>-0.022396</td>\n",
              "      <td>0.080466</td>\n",
              "      <td>-0.060623</td>\n",
              "      <td>-0.009461</td>\n",
              "      <td>-0.013061</td>\n",
              "      <td>-0.003361</td>\n",
              "      <td>-0.023806</td>\n",
              "      <td>0.001094</td>\n",
              "      <td>0.048311</td>\n",
              "      <td>-0.010670</td>\n",
              "      <td>-0.021672</td>\n",
              "      <td>-0.016259</td>\n",
              "      <td>-0.017805</td>\n",
              "      <td>-0.040847</td>\n",
              "      <td>0.046255</td>\n",
              "      <td>0.009697</td>\n",
              "      <td>0.004830</td>\n",
              "      <td>-0.003282</td>\n",
              "      <td>0.075491</td>\n",
              "      <td>-0.017299</td>\n",
              "      <td>-0.015734</td>\n",
              "      <td>-0.050922</td>\n",
              "      <td>-0.039136</td>\n",
              "      <td>0.017138</td>\n",
              "      <td>0.004469</td>\n",
              "      <td>-0.021762</td>\n",
              "      <td>-0.013594</td>\n",
              "      <td>0.021044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hours_per_week</th>\n",
              "      <td>0.068756</td>\n",
              "      <td>-0.018768</td>\n",
              "      <td>0.229309</td>\n",
              "      <td>0.078409</td>\n",
              "      <td>0.054256</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.002446</td>\n",
              "      <td>0.229689</td>\n",
              "      <td>-0.168640</td>\n",
              "      <td>0.013293</td>\n",
              "      <td>0.011576</td>\n",
              "      <td>-0.020928</td>\n",
              "      <td>0.124121</td>\n",
              "      <td>0.093884</td>\n",
              "      <td>-0.023200</td>\n",
              "      <td>-0.047085</td>\n",
              "      <td>-0.102038</td>\n",
              "      <td>-0.043785</td>\n",
              "      <td>-0.012724</td>\n",
              "      <td>-0.012675</td>\n",
              "      <td>-0.012336</td>\n",
              "      <td>-0.024542</td>\n",
              "      <td>0.000995</td>\n",
              "      <td>0.020005</td>\n",
              "      <td>0.078207</td>\n",
              "      <td>0.059998</td>\n",
              "      <td>0.007707</td>\n",
              "      <td>0.065068</td>\n",
              "      <td>-0.012159</td>\n",
              "      <td>0.075948</td>\n",
              "      <td>-0.068960</td>\n",
              "      <td>0.026052</td>\n",
              "      <td>0.212817</td>\n",
              "      <td>-0.197936</td>\n",
              "      <td>-0.006562</td>\n",
              "      <td>-0.016587</td>\n",
              "      <td>-0.107122</td>\n",
              "      <td>-0.084378</td>\n",
              "      <td>0.057375</td>\n",
              "      <td>0.139208</td>\n",
              "      <td>0.094170</td>\n",
              "      <td>-0.042265</td>\n",
              "      <td>0.006598</td>\n",
              "      <td>-0.138884</td>\n",
              "      <td>-0.155872</td>\n",
              "      <td>0.060253</td>\n",
              "      <td>0.009889</td>\n",
              "      <td>-0.013946</td>\n",
              "      <td>0.077596</td>\n",
              "      <td>0.246164</td>\n",
              "      <td>0.007498</td>\n",
              "      <td>-0.048980</td>\n",
              "      <td>-0.249254</td>\n",
              "      <td>-0.037182</td>\n",
              "      <td>-0.065140</td>\n",
              "      <td>-0.004564</td>\n",
              "      <td>-0.053153</td>\n",
              "      <td>-0.007201</td>\n",
              "      <td>0.049345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>native_country</th>\n",
              "      <td>0.015917</td>\n",
              "      <td>-0.075287</td>\n",
              "      <td>-0.006993</td>\n",
              "      <td>0.004599</td>\n",
              "      <td>0.008784</td>\n",
              "      <td>0.002446</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.034470</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>0.015441</td>\n",
              "      <td>0.033196</td>\n",
              "      <td>-0.043184</td>\n",
              "      <td>-0.004157</td>\n",
              "      <td>0.013730</td>\n",
              "      <td>0.024246</td>\n",
              "      <td>0.007332</td>\n",
              "      <td>0.007746</td>\n",
              "      <td>-0.020106</td>\n",
              "      <td>-0.146662</td>\n",
              "      <td>-0.201194</td>\n",
              "      <td>-0.057483</td>\n",
              "      <td>-0.052812</td>\n",
              "      <td>0.014752</td>\n",
              "      <td>0.025399</td>\n",
              "      <td>-0.008494</td>\n",
              "      <td>-0.037727</td>\n",
              "      <td>0.063371</td>\n",
              "      <td>-0.007438</td>\n",
              "      <td>-0.072943</td>\n",
              "      <td>-0.010689</td>\n",
              "      <td>0.050243</td>\n",
              "      <td>0.053224</td>\n",
              "      <td>-0.009755</td>\n",
              "      <td>0.001833</td>\n",
              "      <td>-0.103579</td>\n",
              "      <td>-0.020300</td>\n",
              "      <td>0.007259</td>\n",
              "      <td>0.022506</td>\n",
              "      <td>0.003905</td>\n",
              "      <td>0.028118</td>\n",
              "      <td>-0.006711</td>\n",
              "      <td>-0.019194</td>\n",
              "      <td>-0.044580</td>\n",
              "      <td>-0.001111</td>\n",
              "      <td>-0.058293</td>\n",
              "      <td>-0.004783</td>\n",
              "      <td>0.029997</td>\n",
              "      <td>0.011266</td>\n",
              "      <td>0.028081</td>\n",
              "      <td>0.008594</td>\n",
              "      <td>0.020277</td>\n",
              "      <td>-0.107544</td>\n",
              "      <td>0.041824</td>\n",
              "      <td>-0.017690</td>\n",
              "      <td>-0.020525</td>\n",
              "      <td>-0.365437</td>\n",
              "      <td>0.011383</td>\n",
              "      <td>-0.073147</td>\n",
              "      <td>0.200004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>classification</th>\n",
              "      <td>0.234037</td>\n",
              "      <td>-0.009463</td>\n",
              "      <td>0.215980</td>\n",
              "      <td>0.223329</td>\n",
              "      <td>0.150526</td>\n",
              "      <td>0.229689</td>\n",
              "      <td>0.034470</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.078198</td>\n",
              "      <td>0.059372</td>\n",
              "      <td>0.033091</td>\n",
              "      <td>-0.078528</td>\n",
              "      <td>0.136240</td>\n",
              "      <td>0.030019</td>\n",
              "      <td>0.014843</td>\n",
              "      <td>-0.070038</td>\n",
              "      <td>-0.085864</td>\n",
              "      <td>-0.044690</td>\n",
              "      <td>-0.034544</td>\n",
              "      <td>-0.045826</td>\n",
              "      <td>-0.059524</td>\n",
              "      <td>-0.055767</td>\n",
              "      <td>0.003250</td>\n",
              "      <td>0.010048</td>\n",
              "      <td>0.180485</td>\n",
              "      <td>0.132572</td>\n",
              "      <td>-0.131189</td>\n",
              "      <td>0.174570</td>\n",
              "      <td>-0.022307</td>\n",
              "      <td>0.154907</td>\n",
              "      <td>-0.063535</td>\n",
              "      <td>-0.126995</td>\n",
              "      <td>0.444696</td>\n",
              "      <td>-0.318440</td>\n",
              "      <td>-0.038650</td>\n",
              "      <td>-0.074386</td>\n",
              "      <td>-0.064381</td>\n",
              "      <td>-0.089986</td>\n",
              "      <td>-0.012576</td>\n",
              "      <td>0.214861</td>\n",
              "      <td>-0.051925</td>\n",
              "      <td>-0.087265</td>\n",
              "      <td>-0.069401</td>\n",
              "      <td>-0.061508</td>\n",
              "      <td>-0.156348</td>\n",
              "      <td>0.185866</td>\n",
              "      <td>0.023688</td>\n",
              "      <td>0.025696</td>\n",
              "      <td>-0.021476</td>\n",
              "      <td>0.401035</td>\n",
              "      <td>-0.188497</td>\n",
              "      <td>-0.083716</td>\n",
              "      <td>-0.228532</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>0.123264</td>\n",
              "      <td>0.010543</td>\n",
              "      <td>-0.089089</td>\n",
              "      <td>-0.042909</td>\n",
              "      <td>0.085224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>workclass_ ?</th>\n",
              "      <td>0.042627</td>\n",
              "      <td>-0.002923</td>\n",
              "      <td>-0.065556</td>\n",
              "      <td>-0.015585</td>\n",
              "      <td>-0.016102</td>\n",
              "      <td>-0.168640</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>-0.078198</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.042606</td>\n",
              "      <td>-0.064070</td>\n",
              "      <td>-0.370780</td>\n",
              "      <td>-0.046499</td>\n",
              "      <td>-0.071119</td>\n",
              "      <td>-0.049810</td>\n",
              "      <td>0.037821</td>\n",
              "      <td>0.036941</td>\n",
              "      <td>0.018115</td>\n",
              "      <td>0.004696</td>\n",
              "      <td>0.014852</td>\n",
              "      <td>0.033966</td>\n",
              "      <td>0.023518</td>\n",
              "      <td>-0.009845</td>\n",
              "      <td>-0.011179</td>\n",
              "      <td>-0.046315</td>\n",
              "      <td>-0.009860</td>\n",
              "      <td>-0.017123</td>\n",
              "      <td>-0.029234</td>\n",
              "      <td>0.007152</td>\n",
              "      <td>-0.014624</td>\n",
              "      <td>0.032861</td>\n",
              "      <td>-0.025803</td>\n",
              "      <td>-0.055685</td>\n",
              "      <td>0.046399</td>\n",
              "      <td>0.007065</td>\n",
              "      <td>0.006256</td>\n",
              "      <td>0.075115</td>\n",
              "      <td>-0.088457</td>\n",
              "      <td>-0.092768</td>\n",
              "      <td>-0.092340</td>\n",
              "      <td>-0.043378</td>\n",
              "      <td>-0.051231</td>\n",
              "      <td>-0.062568</td>\n",
              "      <td>0.821264</td>\n",
              "      <td>-0.082023</td>\n",
              "      <td>-0.093298</td>\n",
              "      <td>-0.086857</td>\n",
              "      <td>-0.041869</td>\n",
              "      <td>-0.055516</td>\n",
              "      <td>-0.069133</td>\n",
              "      <td>-0.008946</td>\n",
              "      <td>0.005986</td>\n",
              "      <td>0.092638</td>\n",
              "      <td>-0.008357</td>\n",
              "      <td>0.027106</td>\n",
              "      <td>0.004859</td>\n",
              "      <td>0.016658</td>\n",
              "      <td>0.015257</td>\n",
              "      <td>-0.022055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>workclass_ Federal-gov</th>\n",
              "      <td>0.051227</td>\n",
              "      <td>-0.007525</td>\n",
              "      <td>0.000989</td>\n",
              "      <td>-0.005768</td>\n",
              "      <td>0.010798</td>\n",
              "      <td>0.013293</td>\n",
              "      <td>0.015441</td>\n",
              "      <td>0.059372</td>\n",
              "      <td>-0.042606</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.045682</td>\n",
              "      <td>-0.264369</td>\n",
              "      <td>-0.033154</td>\n",
              "      <td>-0.050709</td>\n",
              "      <td>-0.035515</td>\n",
              "      <td>-0.023406</td>\n",
              "      <td>-0.024962</td>\n",
              "      <td>-0.012309</td>\n",
              "      <td>-0.012552</td>\n",
              "      <td>-0.015912</td>\n",
              "      <td>-0.022193</td>\n",
              "      <td>-0.017704</td>\n",
              "      <td>0.024008</td>\n",
              "      <td>-0.002473</td>\n",
              "      <td>0.026506</td>\n",
              "      <td>0.006203</td>\n",
              "      <td>-0.018101</td>\n",
              "      <td>0.013139</td>\n",
              "      <td>-0.006903</td>\n",
              "      <td>0.016552</td>\n",
              "      <td>0.017002</td>\n",
              "      <td>0.019573</td>\n",
              "      <td>0.010732</td>\n",
              "      <td>-0.027056</td>\n",
              "      <td>0.001568</td>\n",
              "      <td>-0.004388</td>\n",
              "      <td>0.007099</td>\n",
              "      <td>0.116805</td>\n",
              "      <td>-0.031116</td>\n",
              "      <td>0.033020</td>\n",
              "      <td>-0.022486</td>\n",
              "      <td>-0.015728</td>\n",
              "      <td>-0.034030</td>\n",
              "      <td>-0.027311</td>\n",
              "      <td>-0.037413</td>\n",
              "      <td>0.028852</td>\n",
              "      <td>-0.053873</td>\n",
              "      <td>0.044342</td>\n",
              "      <td>-0.018566</td>\n",
              "      <td>0.014804</td>\n",
              "      <td>0.014221</td>\n",
              "      <td>-0.008415</td>\n",
              "      <td>-0.042780</td>\n",
              "      <td>0.014401</td>\n",
              "      <td>-0.004435</td>\n",
              "      <td>0.013808</td>\n",
              "      <td>0.047403</td>\n",
              "      <td>0.012115</td>\n",
              "      <td>-0.050995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>workclass_ Local-gov</th>\n",
              "      <td>0.060901</td>\n",
              "      <td>-0.002828</td>\n",
              "      <td>-0.037966</td>\n",
              "      <td>-0.007007</td>\n",
              "      <td>0.014668</td>\n",
              "      <td>0.011576</td>\n",
              "      <td>0.033196</td>\n",
              "      <td>0.033091</td>\n",
              "      <td>-0.064070</td>\n",
              "      <td>-0.045682</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.397547</td>\n",
              "      <td>-0.049855</td>\n",
              "      <td>-0.076253</td>\n",
              "      <td>-0.053405</td>\n",
              "      <td>-0.021747</td>\n",
              "      <td>-0.026541</td>\n",
              "      <td>-0.009656</td>\n",
              "      <td>-0.011884</td>\n",
              "      <td>-0.015440</td>\n",
              "      <td>-0.012145</td>\n",
              "      <td>-0.010086</td>\n",
              "      <td>0.013656</td>\n",
              "      <td>-0.001760</td>\n",
              "      <td>0.044856</td>\n",
              "      <td>0.000507</td>\n",
              "      <td>-0.046078</td>\n",
              "      <td>0.129354</td>\n",
              "      <td>0.002286</td>\n",
              "      <td>-0.007623</td>\n",
              "      <td>-0.024531</td>\n",
              "      <td>0.030427</td>\n",
              "      <td>0.015164</td>\n",
              "      <td>-0.041793</td>\n",
              "      <td>-0.006876</td>\n",
              "      <td>-0.002070</td>\n",
              "      <td>0.016146</td>\n",
              "      <td>0.015916</td>\n",
              "      <td>-0.044349</td>\n",
              "      <td>-0.017940</td>\n",
              "      <td>-0.025400</td>\n",
              "      <td>-0.025613</td>\n",
              "      <td>-0.060829</td>\n",
              "      <td>0.061214</td>\n",
              "      <td>-0.007806</td>\n",
              "      <td>0.164976</td>\n",
              "      <td>-0.090349</td>\n",
              "      <td>-0.016294</td>\n",
              "      <td>0.007159</td>\n",
              "      <td>0.002796</td>\n",
              "      <td>-0.003401</td>\n",
              "      <td>-0.014694</td>\n",
              "      <td>-0.038263</td>\n",
              "      <td>0.031953</td>\n",
              "      <td>0.031123</td>\n",
              "      <td>-0.019797</td>\n",
              "      <td>0.037073</td>\n",
              "      <td>0.008118</td>\n",
              "      <td>-0.024132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>workclass_ Private</th>\n",
              "      <td>-0.198388</td>\n",
              "      <td>0.042907</td>\n",
              "      <td>-0.034699</td>\n",
              "      <td>-0.038701</td>\n",
              "      <td>-0.027460</td>\n",
              "      <td>-0.020928</td>\n",
              "      <td>-0.043184</td>\n",
              "      <td>-0.078528</td>\n",
              "      <td>-0.370780</td>\n",
              "      <td>-0.264369</td>\n",
              "      <td>-0.397547</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.288520</td>\n",
              "      <td>-0.441289</td>\n",
              "      <td>-0.309064</td>\n",
              "      <td>0.017895</td>\n",
              "      <td>0.037263</td>\n",
              "      <td>0.018195</td>\n",
              "      <td>0.017629</td>\n",
              "      <td>0.022511</td>\n",
              "      <td>-0.012595</td>\n",
              "      <td>0.015402</td>\n",
              "      <td>-0.005530</td>\n",
              "      <td>0.013825</td>\n",
              "      <td>-0.032739</td>\n",
              "      <td>-0.063825</td>\n",
              "      <td>0.065837</td>\n",
              "      <td>-0.091644</td>\n",
              "      <td>0.009213</td>\n",
              "      <td>-0.073253</td>\n",
              "      <td>0.001916</td>\n",
              "      <td>0.004302</td>\n",
              "      <td>-0.094767</td>\n",
              "      <td>0.105278</td>\n",
              "      <td>0.005556</td>\n",
              "      <td>0.015135</td>\n",
              "      <td>-0.040480</td>\n",
              "      <td>0.042859</td>\n",
              "      <td>0.068070</td>\n",
              "      <td>-0.028935</td>\n",
              "      <td>-0.092398</td>\n",
              "      <td>0.105882</td>\n",
              "      <td>0.143986</td>\n",
              "      <td>-0.368617</td>\n",
              "      <td>0.098231</td>\n",
              "      <td>-0.114891</td>\n",
              "      <td>0.084276</td>\n",
              "      <td>0.035808</td>\n",
              "      <td>0.047298</td>\n",
              "      <td>-0.084935</td>\n",
              "      <td>0.033295</td>\n",
              "      <td>0.035270</td>\n",
              "      <td>0.061656</td>\n",
              "      <td>0.016518</td>\n",
              "      <td>-0.029325</td>\n",
              "      <td>-0.004264</td>\n",
              "      <td>-0.000345</td>\n",
              "      <td>-0.001348</td>\n",
              "      <td>0.002919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>workclass_ Self-emp-inc</th>\n",
              "      <td>0.101808</td>\n",
              "      <td>-0.024348</td>\n",
              "      <td>0.083245</td>\n",
              "      <td>0.095660</td>\n",
              "      <td>0.030669</td>\n",
              "      <td>0.124121</td>\n",
              "      <td>-0.004157</td>\n",
              "      <td>0.136240</td>\n",
              "      <td>-0.046499</td>\n",
              "      <td>-0.033154</td>\n",
              "      <td>-0.049855</td>\n",
              "      <td>-0.288520</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.055341</td>\n",
              "      <td>-0.038759</td>\n",
              "      <td>-0.011612</td>\n",
              "      <td>-0.023349</td>\n",
              "      <td>-0.011859</td>\n",
              "      <td>-0.009028</td>\n",
              "      <td>-0.012684</td>\n",
              "      <td>-0.007867</td>\n",
              "      <td>-0.010668</td>\n",
              "      <td>-0.001183</td>\n",
              "      <td>-0.008513</td>\n",
              "      <td>0.038816</td>\n",
              "      <td>0.030764</td>\n",
              "      <td>-0.027804</td>\n",
              "      <td>0.014075</td>\n",
              "      <td>-0.007534</td>\n",
              "      <td>0.077272</td>\n",
              "      <td>-0.009469</td>\n",
              "      <td>-0.026389</td>\n",
              "      <td>0.108440</td>\n",
              "      <td>-0.085174</td>\n",
              "      <td>-0.013604</td>\n",
              "      <td>-0.015131</td>\n",
              "      <td>-0.004548</td>\n",
              "      <td>-0.051055</td>\n",
              "      <td>-0.018223</td>\n",
              "      <td>0.130578</td>\n",
              "      <td>0.021677</td>\n",
              "      <td>-0.037365</td>\n",
              "      <td>-0.038937</td>\n",
              "      <td>-0.049276</td>\n",
              "      <td>-0.048293</td>\n",
              "      <td>0.007751</td>\n",
              "      <td>0.086726</td>\n",
              "      <td>-0.029564</td>\n",
              "      <td>-0.021509</td>\n",
              "      <td>0.112223</td>\n",
              "      <td>-0.046441</td>\n",
              "      <td>-0.025696</td>\n",
              "      <td>-0.051211</td>\n",
              "      <td>-0.036617</td>\n",
              "      <td>-0.002933</td>\n",
              "      <td>0.010203</td>\n",
              "      <td>-0.047197</td>\n",
              "      <td>-0.016823</td>\n",
              "      <td>0.040631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>workclass_ Self-emp-not-inc</th>\n",
              "      <td>0.136252</td>\n",
              "      <td>-0.039058</td>\n",
              "      <td>0.107451</td>\n",
              "      <td>0.031847</td>\n",
              "      <td>0.021175</td>\n",
              "      <td>0.093884</td>\n",
              "      <td>0.013730</td>\n",
              "      <td>0.030019</td>\n",
              "      <td>-0.071119</td>\n",
              "      <td>-0.050709</td>\n",
              "      <td>-0.076253</td>\n",
              "      <td>-0.441289</td>\n",
              "      <td>-0.055341</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.059281</td>\n",
              "      <td>-0.003987</td>\n",
              "      <td>-0.019458</td>\n",
              "      <td>-0.014784</td>\n",
              "      <td>-0.000176</td>\n",
              "      <td>-0.007951</td>\n",
              "      <td>0.035788</td>\n",
              "      <td>-0.005614</td>\n",
              "      <td>-0.007889</td>\n",
              "      <td>0.000086</td>\n",
              "      <td>-0.005836</td>\n",
              "      <td>0.018182</td>\n",
              "      <td>0.011395</td>\n",
              "      <td>-0.005350</td>\n",
              "      <td>-0.011523</td>\n",
              "      <td>0.074741</td>\n",
              "      <td>-0.022790</td>\n",
              "      <td>-0.018253</td>\n",
              "      <td>0.117462</td>\n",
              "      <td>-0.103563</td>\n",
              "      <td>-0.001401</td>\n",
              "      <td>-0.017698</td>\n",
              "      <td>-0.002325</td>\n",
              "      <td>-0.087386</td>\n",
              "      <td>0.072870</td>\n",
              "      <td>0.025872</td>\n",
              "      <td>0.234560</td>\n",
              "      <td>-0.052419</td>\n",
              "      <td>-0.057307</td>\n",
              "      <td>-0.084085</td>\n",
              "      <td>-0.031183</td>\n",
              "      <td>0.017158</td>\n",
              "      <td>0.036350</td>\n",
              "      <td>-0.031941</td>\n",
              "      <td>-0.001393</td>\n",
              "      <td>0.119515</td>\n",
              "      <td>-0.034701</td>\n",
              "      <td>-0.017787</td>\n",
              "      <td>-0.075958</td>\n",
              "      <td>-0.042401</td>\n",
              "      <td>0.000340</td>\n",
              "      <td>-0.005265</td>\n",
              "      <td>-0.058622</td>\n",
              "      <td>-0.010731</td>\n",
              "      <td>0.055585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>workclass_ State-gov</th>\n",
              "      <td>0.012763</td>\n",
              "      <td>-0.010891</td>\n",
              "      <td>-0.019895</td>\n",
              "      <td>-0.010373</td>\n",
              "      <td>-0.002047</td>\n",
              "      <td>-0.023200</td>\n",
              "      <td>0.024246</td>\n",
              "      <td>0.014843</td>\n",
              "      <td>-0.049810</td>\n",
              "      <td>-0.035515</td>\n",
              "      <td>-0.053405</td>\n",
              "      <td>-0.309064</td>\n",
              "      <td>-0.038759</td>\n",
              "      <td>-0.059281</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.022764</td>\n",
              "      <td>-0.027641</td>\n",
              "      <td>-0.009951</td>\n",
              "      <td>-0.012483</td>\n",
              "      <td>-0.014471</td>\n",
              "      <td>-0.017732</td>\n",
              "      <td>-0.018249</td>\n",
              "      <td>-0.001353</td>\n",
              "      <td>-0.007079</td>\n",
              "      <td>0.023939</td>\n",
              "      <td>0.101753</td>\n",
              "      <td>-0.050580</td>\n",
              "      <td>0.070344</td>\n",
              "      <td>-0.004101</td>\n",
              "      <td>0.009573</td>\n",
              "      <td>0.012937</td>\n",
              "      <td>0.015039</td>\n",
              "      <td>-0.002834</td>\n",
              "      <td>-0.004301</td>\n",
              "      <td>0.000571</td>\n",
              "      <td>0.001924</td>\n",
              "      <td>-0.012402</td>\n",
              "      <td>0.050394</td>\n",
              "      <td>-0.050826</td>\n",
              "      <td>0.012781</td>\n",
              "      <td>-0.022470</td>\n",
              "      <td>-0.035667</td>\n",
              "      <td>-0.043658</td>\n",
              "      <td>0.005949</td>\n",
              "      <td>-0.003826</td>\n",
              "      <td>0.117317</td>\n",
              "      <td>-0.066926</td>\n",
              "      <td>0.018874</td>\n",
              "      <td>-0.016473</td>\n",
              "      <td>-0.004451</td>\n",
              "      <td>0.015461</td>\n",
              "      <td>-0.015709</td>\n",
              "      <td>-0.016901</td>\n",
              "      <td>0.011038</td>\n",
              "      <td>0.004028</td>\n",
              "      <td>0.014810</td>\n",
              "      <td>0.018371</td>\n",
              "      <td>-0.004977</td>\n",
              "      <td>-0.020843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ 10th</th>\n",
              "      <td>-0.014504</td>\n",
              "      <td>0.011479</td>\n",
              "      <td>0.005333</td>\n",
              "      <td>-0.015653</td>\n",
              "      <td>-0.012982</td>\n",
              "      <td>-0.047085</td>\n",
              "      <td>0.007332</td>\n",
              "      <td>-0.070038</td>\n",
              "      <td>0.037821</td>\n",
              "      <td>-0.023406</td>\n",
              "      <td>-0.021747</td>\n",
              "      <td>0.017895</td>\n",
              "      <td>-0.011612</td>\n",
              "      <td>-0.003987</td>\n",
              "      <td>-0.022764</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.033232</td>\n",
              "      <td>-0.019939</td>\n",
              "      <td>-0.012369</td>\n",
              "      <td>-0.017459</td>\n",
              "      <td>-0.024436</td>\n",
              "      <td>-0.021752</td>\n",
              "      <td>-0.031614</td>\n",
              "      <td>-0.036160</td>\n",
              "      <td>-0.076200</td>\n",
              "      <td>-0.019467</td>\n",
              "      <td>-0.118500</td>\n",
              "      <td>-0.040598</td>\n",
              "      <td>-0.006803</td>\n",
              "      <td>-0.023049</td>\n",
              "      <td>-0.092256</td>\n",
              "      <td>-0.003920</td>\n",
              "      <td>-0.029594</td>\n",
              "      <td>0.021521</td>\n",
              "      <td>0.003764</td>\n",
              "      <td>0.020695</td>\n",
              "      <td>0.011291</td>\n",
              "      <td>-0.040288</td>\n",
              "      <td>0.029161</td>\n",
              "      <td>-0.051514</td>\n",
              "      <td>0.016605</td>\n",
              "      <td>0.029108</td>\n",
              "      <td>0.033439</td>\n",
              "      <td>0.025629</td>\n",
              "      <td>0.060787</td>\n",
              "      <td>-0.060579</td>\n",
              "      <td>-0.013763</td>\n",
              "      <td>-0.026099</td>\n",
              "      <td>0.032595</td>\n",
              "      <td>-0.024385</td>\n",
              "      <td>-0.011813</td>\n",
              "      <td>0.016036</td>\n",
              "      <td>0.040513</td>\n",
              "      <td>0.008533</td>\n",
              "      <td>-0.013697</td>\n",
              "      <td>-0.017566</td>\n",
              "      <td>0.027181</td>\n",
              "      <td>0.011565</td>\n",
              "      <td>-0.018280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ 11th</th>\n",
              "      <td>-0.088314</td>\n",
              "      <td>0.009440</td>\n",
              "      <td>-0.015160</td>\n",
              "      <td>-0.022598</td>\n",
              "      <td>-0.017874</td>\n",
              "      <td>-0.102038</td>\n",
              "      <td>0.007746</td>\n",
              "      <td>-0.085864</td>\n",
              "      <td>0.036941</td>\n",
              "      <td>-0.024962</td>\n",
              "      <td>-0.026541</td>\n",
              "      <td>0.037263</td>\n",
              "      <td>-0.023349</td>\n",
              "      <td>-0.019458</td>\n",
              "      <td>-0.027641</td>\n",
              "      <td>-0.033232</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.022462</td>\n",
              "      <td>-0.013934</td>\n",
              "      <td>-0.019668</td>\n",
              "      <td>-0.027528</td>\n",
              "      <td>-0.024504</td>\n",
              "      <td>-0.035614</td>\n",
              "      <td>-0.040736</td>\n",
              "      <td>-0.085842</td>\n",
              "      <td>-0.021931</td>\n",
              "      <td>-0.133495</td>\n",
              "      <td>-0.045735</td>\n",
              "      <td>-0.007664</td>\n",
              "      <td>-0.025965</td>\n",
              "      <td>-0.103930</td>\n",
              "      <td>-0.014550</td>\n",
              "      <td>-0.061595</td>\n",
              "      <td>0.070317</td>\n",
              "      <td>0.004396</td>\n",
              "      <td>0.010385</td>\n",
              "      <td>0.002075</td>\n",
              "      <td>-0.035534</td>\n",
              "      <td>0.013444</td>\n",
              "      <td>-0.056152</td>\n",
              "      <td>0.001082</td>\n",
              "      <td>0.060338</td>\n",
              "      <td>0.018341</td>\n",
              "      <td>0.026723</td>\n",
              "      <td>0.065028</td>\n",
              "      <td>-0.063961</td>\n",
              "      <td>0.006413</td>\n",
              "      <td>-0.027202</td>\n",
              "      <td>0.026207</td>\n",
              "      <td>-0.055373</td>\n",
              "      <td>-0.033507</td>\n",
              "      <td>0.022734</td>\n",
              "      <td>0.110432</td>\n",
              "      <td>0.007305</td>\n",
              "      <td>-0.020446</td>\n",
              "      <td>-0.015453</td>\n",
              "      <td>0.022514</td>\n",
              "      <td>0.003726</td>\n",
              "      <td>-0.012494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ 12th</th>\n",
              "      <td>-0.056016</td>\n",
              "      <td>0.010250</td>\n",
              "      <td>-0.000436</td>\n",
              "      <td>-0.012474</td>\n",
              "      <td>-0.015836</td>\n",
              "      <td>-0.043785</td>\n",
              "      <td>-0.020106</td>\n",
              "      <td>-0.044690</td>\n",
              "      <td>0.018115</td>\n",
              "      <td>-0.012309</td>\n",
              "      <td>-0.009656</td>\n",
              "      <td>0.018195</td>\n",
              "      <td>-0.011859</td>\n",
              "      <td>-0.014784</td>\n",
              "      <td>-0.009951</td>\n",
              "      <td>-0.019939</td>\n",
              "      <td>-0.022462</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.008360</td>\n",
              "      <td>-0.011801</td>\n",
              "      <td>-0.016517</td>\n",
              "      <td>-0.014702</td>\n",
              "      <td>-0.021368</td>\n",
              "      <td>-0.024441</td>\n",
              "      <td>-0.051505</td>\n",
              "      <td>-0.013158</td>\n",
              "      <td>-0.080097</td>\n",
              "      <td>-0.027441</td>\n",
              "      <td>-0.004598</td>\n",
              "      <td>-0.015579</td>\n",
              "      <td>-0.062358</td>\n",
              "      <td>-0.015686</td>\n",
              "      <td>-0.037201</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.004953</td>\n",
              "      <td>0.000567</td>\n",
              "      <td>-0.004997</td>\n",
              "      <td>-0.010167</td>\n",
              "      <td>0.002822</td>\n",
              "      <td>-0.033310</td>\n",
              "      <td>0.004335</td>\n",
              "      <td>0.026418</td>\n",
              "      <td>0.009350</td>\n",
              "      <td>0.015454</td>\n",
              "      <td>0.036611</td>\n",
              "      <td>-0.036260</td>\n",
              "      <td>-0.001307</td>\n",
              "      <td>-0.015050</td>\n",
              "      <td>0.022052</td>\n",
              "      <td>-0.039563</td>\n",
              "      <td>-0.008267</td>\n",
              "      <td>0.015613</td>\n",
              "      <td>0.064051</td>\n",
              "      <td>-0.006820</td>\n",
              "      <td>-0.003571</td>\n",
              "      <td>-0.007348</td>\n",
              "      <td>0.025906</td>\n",
              "      <td>0.022786</td>\n",
              "      <td>-0.026520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ 1st-4th</th>\n",
              "      <td>0.039921</td>\n",
              "      <td>0.033791</td>\n",
              "      <td>0.008722</td>\n",
              "      <td>-0.009281</td>\n",
              "      <td>-0.006966</td>\n",
              "      <td>-0.012724</td>\n",
              "      <td>-0.146662</td>\n",
              "      <td>-0.034544</td>\n",
              "      <td>0.004696</td>\n",
              "      <td>-0.012552</td>\n",
              "      <td>-0.011884</td>\n",
              "      <td>0.017629</td>\n",
              "      <td>-0.009028</td>\n",
              "      <td>-0.000176</td>\n",
              "      <td>-0.012483</td>\n",
              "      <td>-0.012369</td>\n",
              "      <td>-0.013934</td>\n",
              "      <td>-0.008360</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.007320</td>\n",
              "      <td>-0.010246</td>\n",
              "      <td>-0.009120</td>\n",
              "      <td>-0.013256</td>\n",
              "      <td>-0.015162</td>\n",
              "      <td>-0.031950</td>\n",
              "      <td>-0.008163</td>\n",
              "      <td>-0.049687</td>\n",
              "      <td>-0.017023</td>\n",
              "      <td>-0.002852</td>\n",
              "      <td>-0.009664</td>\n",
              "      <td>-0.038683</td>\n",
              "      <td>-0.016139</td>\n",
              "      <td>0.003209</td>\n",
              "      <td>-0.014717</td>\n",
              "      <td>0.036065</td>\n",
              "      <td>0.009112</td>\n",
              "      <td>0.029608</td>\n",
              "      <td>-0.026060</td>\n",
              "      <td>0.002392</td>\n",
              "      <td>-0.022017</td>\n",
              "      <td>0.032073</td>\n",
              "      <td>0.019071</td>\n",
              "      <td>0.022611</td>\n",
              "      <td>0.016191</td>\n",
              "      <td>0.032691</td>\n",
              "      <td>-0.022339</td>\n",
              "      <td>-0.014718</td>\n",
              "      <td>-0.012335</td>\n",
              "      <td>-0.000476</td>\n",
              "      <td>0.001685</td>\n",
              "      <td>0.007031</td>\n",
              "      <td>0.027431</td>\n",
              "      <td>-0.020278</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>-0.008189</td>\n",
              "      <td>-0.000880</td>\n",
              "      <td>-0.000172</td>\n",
              "      <td>0.032344</td>\n",
              "      <td>-0.011564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ 5th-6th</th>\n",
              "      <td>0.032076</td>\n",
              "      <td>0.041094</td>\n",
              "      <td>0.016969</td>\n",
              "      <td>-0.012410</td>\n",
              "      <td>-0.004806</td>\n",
              "      <td>-0.012675</td>\n",
              "      <td>-0.201194</td>\n",
              "      <td>-0.045826</td>\n",
              "      <td>0.014852</td>\n",
              "      <td>-0.015912</td>\n",
              "      <td>-0.015440</td>\n",
              "      <td>0.022511</td>\n",
              "      <td>-0.012684</td>\n",
              "      <td>-0.007951</td>\n",
              "      <td>-0.014471</td>\n",
              "      <td>-0.017459</td>\n",
              "      <td>-0.019668</td>\n",
              "      <td>-0.011801</td>\n",
              "      <td>-0.007320</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.014462</td>\n",
              "      <td>-0.012873</td>\n",
              "      <td>-0.018710</td>\n",
              "      <td>-0.021401</td>\n",
              "      <td>-0.045098</td>\n",
              "      <td>-0.011521</td>\n",
              "      <td>-0.070132</td>\n",
              "      <td>-0.024027</td>\n",
              "      <td>-0.004026</td>\n",
              "      <td>-0.013641</td>\n",
              "      <td>-0.054600</td>\n",
              "      <td>-0.022621</td>\n",
              "      <td>0.011540</td>\n",
              "      <td>-0.013168</td>\n",
              "      <td>0.040907</td>\n",
              "      <td>0.013142</td>\n",
              "      <td>0.006825</td>\n",
              "      <td>-0.031059</td>\n",
              "      <td>0.000994</td>\n",
              "      <td>-0.037474</td>\n",
              "      <td>0.045840</td>\n",
              "      <td>0.039516</td>\n",
              "      <td>0.045144</td>\n",
              "      <td>0.019982</td>\n",
              "      <td>0.030671</td>\n",
              "      <td>-0.037880</td>\n",
              "      <td>-0.024507</td>\n",
              "      <td>-0.015576</td>\n",
              "      <td>0.016491</td>\n",
              "      <td>0.008752</td>\n",
              "      <td>-0.010459</td>\n",
              "      <td>0.053514</td>\n",
              "      <td>-0.031012</td>\n",
              "      <td>0.009683</td>\n",
              "      <td>-0.002903</td>\n",
              "      <td>0.012807</td>\n",
              "      <td>-0.011348</td>\n",
              "      <td>0.020846</td>\n",
              "      <td>-0.004735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ 7th-8th</th>\n",
              "      <td>0.102886</td>\n",
              "      <td>-0.002290</td>\n",
              "      <td>0.025133</td>\n",
              "      <td>-0.016254</td>\n",
              "      <td>-0.007639</td>\n",
              "      <td>-0.012336</td>\n",
              "      <td>-0.057483</td>\n",
              "      <td>-0.059524</td>\n",
              "      <td>0.033966</td>\n",
              "      <td>-0.022193</td>\n",
              "      <td>-0.012145</td>\n",
              "      <td>-0.012595</td>\n",
              "      <td>-0.007867</td>\n",
              "      <td>0.035788</td>\n",
              "      <td>-0.017732</td>\n",
              "      <td>-0.024436</td>\n",
              "      <td>-0.027528</td>\n",
              "      <td>-0.016517</td>\n",
              "      <td>-0.010246</td>\n",
              "      <td>-0.014462</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.018018</td>\n",
              "      <td>-0.026187</td>\n",
              "      <td>-0.029953</td>\n",
              "      <td>-0.063120</td>\n",
              "      <td>-0.016126</td>\n",
              "      <td>-0.098159</td>\n",
              "      <td>-0.033629</td>\n",
              "      <td>-0.005635</td>\n",
              "      <td>-0.019092</td>\n",
              "      <td>-0.076420</td>\n",
              "      <td>-0.009719</td>\n",
              "      <td>0.027345</td>\n",
              "      <td>-0.046413</td>\n",
              "      <td>0.010004</td>\n",
              "      <td>0.003361</td>\n",
              "      <td>0.056739</td>\n",
              "      <td>-0.043911</td>\n",
              "      <td>0.023023</td>\n",
              "      <td>-0.041084</td>\n",
              "      <td>0.064367</td>\n",
              "      <td>0.020645</td>\n",
              "      <td>0.048849</td>\n",
              "      <td>0.030144</td>\n",
              "      <td>0.023827</td>\n",
              "      <td>-0.048350</td>\n",
              "      <td>-0.030307</td>\n",
              "      <td>-0.017750</td>\n",
              "      <td>0.028876</td>\n",
              "      <td>0.031966</td>\n",
              "      <td>-0.013525</td>\n",
              "      <td>0.017441</td>\n",
              "      <td>-0.031316</td>\n",
              "      <td>0.003316</td>\n",
              "      <td>-0.011427</td>\n",
              "      <td>-0.012046</td>\n",
              "      <td>-0.004471</td>\n",
              "      <td>0.024025</td>\n",
              "      <td>0.000711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ 9th</th>\n",
              "      <td>0.023014</td>\n",
              "      <td>0.015246</td>\n",
              "      <td>0.013631</td>\n",
              "      <td>-0.012614</td>\n",
              "      <td>-0.018325</td>\n",
              "      <td>-0.024542</td>\n",
              "      <td>-0.052812</td>\n",
              "      <td>-0.055767</td>\n",
              "      <td>0.023518</td>\n",
              "      <td>-0.017704</td>\n",
              "      <td>-0.010086</td>\n",
              "      <td>0.015402</td>\n",
              "      <td>-0.010668</td>\n",
              "      <td>-0.005614</td>\n",
              "      <td>-0.018249</td>\n",
              "      <td>-0.021752</td>\n",
              "      <td>-0.024504</td>\n",
              "      <td>-0.014702</td>\n",
              "      <td>-0.009120</td>\n",
              "      <td>-0.012873</td>\n",
              "      <td>-0.018018</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.023311</td>\n",
              "      <td>-0.026663</td>\n",
              "      <td>-0.056187</td>\n",
              "      <td>-0.014354</td>\n",
              "      <td>-0.087378</td>\n",
              "      <td>-0.029936</td>\n",
              "      <td>-0.005016</td>\n",
              "      <td>-0.016995</td>\n",
              "      <td>-0.068027</td>\n",
              "      <td>-0.004404</td>\n",
              "      <td>-0.003168</td>\n",
              "      <td>-0.007158</td>\n",
              "      <td>0.004345</td>\n",
              "      <td>0.023734</td>\n",
              "      <td>0.010496</td>\n",
              "      <td>-0.035047</td>\n",
              "      <td>0.023244</td>\n",
              "      <td>-0.038150</td>\n",
              "      <td>0.017629</td>\n",
              "      <td>0.033595</td>\n",
              "      <td>0.045538</td>\n",
              "      <td>0.020877</td>\n",
              "      <td>0.040021</td>\n",
              "      <td>-0.046117</td>\n",
              "      <td>-0.020007</td>\n",
              "      <td>-0.018730</td>\n",
              "      <td>0.011169</td>\n",
              "      <td>-0.003143</td>\n",
              "      <td>-0.011362</td>\n",
              "      <td>0.020921</td>\n",
              "      <td>-0.000002</td>\n",
              "      <td>0.012497</td>\n",
              "      <td>-0.004318</td>\n",
              "      <td>-0.010376</td>\n",
              "      <td>0.033201</td>\n",
              "      <td>0.007090</td>\n",
              "      <td>-0.025207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ Assoc-acdm</th>\n",
              "      <td>-0.016196</td>\n",
              "      <td>0.006358</td>\n",
              "      <td>-0.024947</td>\n",
              "      <td>-0.010898</td>\n",
              "      <td>0.002793</td>\n",
              "      <td>0.000995</td>\n",
              "      <td>0.014752</td>\n",
              "      <td>0.003250</td>\n",
              "      <td>-0.009845</td>\n",
              "      <td>0.024008</td>\n",
              "      <td>0.013656</td>\n",
              "      <td>-0.005530</td>\n",
              "      <td>-0.001183</td>\n",
              "      <td>-0.007889</td>\n",
              "      <td>-0.001353</td>\n",
              "      <td>-0.031614</td>\n",
              "      <td>-0.035614</td>\n",
              "      <td>-0.021368</td>\n",
              "      <td>-0.013256</td>\n",
              "      <td>-0.018710</td>\n",
              "      <td>-0.026187</td>\n",
              "      <td>-0.023311</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.038752</td>\n",
              "      <td>-0.081661</td>\n",
              "      <td>-0.020862</td>\n",
              "      <td>-0.126993</td>\n",
              "      <td>-0.043508</td>\n",
              "      <td>-0.007290</td>\n",
              "      <td>-0.024701</td>\n",
              "      <td>-0.098869</td>\n",
              "      <td>0.028849</td>\n",
              "      <td>-0.010644</td>\n",
              "      <td>-0.004804</td>\n",
              "      <td>-0.000673</td>\n",
              "      <td>-0.003545</td>\n",
              "      <td>-0.009571</td>\n",
              "      <td>0.037449</td>\n",
              "      <td>-0.010048</td>\n",
              "      <td>0.006137</td>\n",
              "      <td>-0.018624</td>\n",
              "      <td>-0.017953</td>\n",
              "      <td>-0.023414</td>\n",
              "      <td>-0.002422</td>\n",
              "      <td>-0.017145</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.013337</td>\n",
              "      <td>0.044154</td>\n",
              "      <td>-0.020235</td>\n",
              "      <td>-0.018386</td>\n",
              "      <td>0.023290</td>\n",
              "      <td>-0.017304</td>\n",
              "      <td>-0.016690</td>\n",
              "      <td>0.011819</td>\n",
              "      <td>0.019836</td>\n",
              "      <td>-0.004954</td>\n",
              "      <td>0.002711</td>\n",
              "      <td>-0.003999</td>\n",
              "      <td>0.001706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ Assoc-voc</th>\n",
              "      <td>-0.000434</td>\n",
              "      <td>-0.015643</td>\n",
              "      <td>-0.013872</td>\n",
              "      <td>-0.010337</td>\n",
              "      <td>-0.007602</td>\n",
              "      <td>0.020005</td>\n",
              "      <td>0.025399</td>\n",
              "      <td>0.010048</td>\n",
              "      <td>-0.011179</td>\n",
              "      <td>-0.002473</td>\n",
              "      <td>-0.001760</td>\n",
              "      <td>0.013825</td>\n",
              "      <td>-0.008513</td>\n",
              "      <td>0.000086</td>\n",
              "      <td>-0.007079</td>\n",
              "      <td>-0.036160</td>\n",
              "      <td>-0.040736</td>\n",
              "      <td>-0.024441</td>\n",
              "      <td>-0.015162</td>\n",
              "      <td>-0.021401</td>\n",
              "      <td>-0.029953</td>\n",
              "      <td>-0.026663</td>\n",
              "      <td>-0.038752</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.093405</td>\n",
              "      <td>-0.023863</td>\n",
              "      <td>-0.145256</td>\n",
              "      <td>-0.049765</td>\n",
              "      <td>-0.008339</td>\n",
              "      <td>-0.028253</td>\n",
              "      <td>-0.113087</td>\n",
              "      <td>0.020159</td>\n",
              "      <td>0.016312</td>\n",
              "      <td>-0.029663</td>\n",
              "      <td>-0.006218</td>\n",
              "      <td>-0.001313</td>\n",
              "      <td>-0.001016</td>\n",
              "      <td>0.003327</td>\n",
              "      <td>0.035832</td>\n",
              "      <td>-0.010403</td>\n",
              "      <td>0.008688</td>\n",
              "      <td>-0.022876</td>\n",
              "      <td>-0.013934</td>\n",
              "      <td>0.000292</td>\n",
              "      <td>-0.012553</td>\n",
              "      <td>-0.002614</td>\n",
              "      <td>-0.023621</td>\n",
              "      <td>0.079296</td>\n",
              "      <td>-0.019597</td>\n",
              "      <td>0.011185</td>\n",
              "      <td>-0.000522</td>\n",
              "      <td>-0.015718</td>\n",
              "      <td>-0.029879</td>\n",
              "      <td>0.018690</td>\n",
              "      <td>0.011704</td>\n",
              "      <td>-0.005286</td>\n",
              "      <td>-0.010652</td>\n",
              "      <td>0.000343</td>\n",
              "      <td>0.011396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ Bachelors</th>\n",
              "      <td>0.010516</td>\n",
              "      <td>-0.007240</td>\n",
              "      <td>0.026837</td>\n",
              "      <td>0.040769</td>\n",
              "      <td>0.034183</td>\n",
              "      <td>0.078207</td>\n",
              "      <td>-0.008494</td>\n",
              "      <td>0.180485</td>\n",
              "      <td>-0.046315</td>\n",
              "      <td>0.026506</td>\n",
              "      <td>0.044856</td>\n",
              "      <td>-0.032739</td>\n",
              "      <td>0.038816</td>\n",
              "      <td>-0.005836</td>\n",
              "      <td>0.023939</td>\n",
              "      <td>-0.076200</td>\n",
              "      <td>-0.085842</td>\n",
              "      <td>-0.051505</td>\n",
              "      <td>-0.031950</td>\n",
              "      <td>-0.045098</td>\n",
              "      <td>-0.063120</td>\n",
              "      <td>-0.056187</td>\n",
              "      <td>-0.081661</td>\n",
              "      <td>-0.093405</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.050286</td>\n",
              "      <td>-0.306098</td>\n",
              "      <td>-0.104869</td>\n",
              "      <td>-0.017572</td>\n",
              "      <td>-0.059537</td>\n",
              "      <td>-0.238308</td>\n",
              "      <td>-0.044578</td>\n",
              "      <td>0.050707</td>\n",
              "      <td>0.006717</td>\n",
              "      <td>-0.000378</td>\n",
              "      <td>-0.036332</td>\n",
              "      <td>-0.039177</td>\n",
              "      <td>-0.029523</td>\n",
              "      <td>-0.111921</td>\n",
              "      <td>0.175511</td>\n",
              "      <td>-0.041645</td>\n",
              "      <td>-0.072347</td>\n",
              "      <td>-0.089758</td>\n",
              "      <td>-0.046911</td>\n",
              "      <td>-0.099143</td>\n",
              "      <td>0.202470</td>\n",
              "      <td>0.054811</td>\n",
              "      <td>0.038528</td>\n",
              "      <td>-0.076971</td>\n",
              "      <td>0.044430</td>\n",
              "      <td>0.056859</td>\n",
              "      <td>-0.036028</td>\n",
              "      <td>-0.070958</td>\n",
              "      <td>-0.060525</td>\n",
              "      <td>0.018236</td>\n",
              "      <td>0.055682</td>\n",
              "      <td>-0.051697</td>\n",
              "      <td>-0.026085</td>\n",
              "      <td>0.025211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ Doctorate</th>\n",
              "      <td>0.075787</td>\n",
              "      <td>-0.003307</td>\n",
              "      <td>0.029525</td>\n",
              "      <td>0.056671</td>\n",
              "      <td>0.049377</td>\n",
              "      <td>0.059998</td>\n",
              "      <td>-0.037727</td>\n",
              "      <td>0.132572</td>\n",
              "      <td>-0.009860</td>\n",
              "      <td>0.006203</td>\n",
              "      <td>0.000507</td>\n",
              "      <td>-0.063825</td>\n",
              "      <td>0.030764</td>\n",
              "      <td>0.018182</td>\n",
              "      <td>0.101753</td>\n",
              "      <td>-0.019467</td>\n",
              "      <td>-0.021931</td>\n",
              "      <td>-0.013158</td>\n",
              "      <td>-0.008163</td>\n",
              "      <td>-0.011521</td>\n",
              "      <td>-0.016126</td>\n",
              "      <td>-0.014354</td>\n",
              "      <td>-0.020862</td>\n",
              "      <td>-0.023863</td>\n",
              "      <td>-0.050286</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.078201</td>\n",
              "      <td>-0.026792</td>\n",
              "      <td>-0.004489</td>\n",
              "      <td>-0.015210</td>\n",
              "      <td>-0.060882</td>\n",
              "      <td>-0.018672</td>\n",
              "      <td>0.052888</td>\n",
              "      <td>-0.036533</td>\n",
              "      <td>0.003339</td>\n",
              "      <td>-0.009432</td>\n",
              "      <td>-0.008930</td>\n",
              "      <td>-0.036726</td>\n",
              "      <td>-0.041359</td>\n",
              "      <td>0.002845</td>\n",
              "      <td>-0.018518</td>\n",
              "      <td>-0.023754</td>\n",
              "      <td>-0.027868</td>\n",
              "      <td>-0.018681</td>\n",
              "      <td>-0.037122</td>\n",
              "      <td>0.221183</td>\n",
              "      <td>-0.033314</td>\n",
              "      <td>-0.014465</td>\n",
              "      <td>-0.024470</td>\n",
              "      <td>0.054595</td>\n",
              "      <td>-0.005880</td>\n",
              "      <td>-0.015160</td>\n",
              "      <td>-0.043364</td>\n",
              "      <td>-0.019367</td>\n",
              "      <td>0.000143</td>\n",
              "      <td>0.023143</td>\n",
              "      <td>-0.026674</td>\n",
              "      <td>-0.004934</td>\n",
              "      <td>0.012589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ HS-grad</th>\n",
              "      <td>0.019870</td>\n",
              "      <td>-0.001566</td>\n",
              "      <td>0.011684</td>\n",
              "      <td>-0.046791</td>\n",
              "      <td>-0.028829</td>\n",
              "      <td>0.007707</td>\n",
              "      <td>0.063371</td>\n",
              "      <td>-0.131189</td>\n",
              "      <td>-0.017123</td>\n",
              "      <td>-0.018101</td>\n",
              "      <td>-0.046078</td>\n",
              "      <td>0.065837</td>\n",
              "      <td>-0.027804</td>\n",
              "      <td>0.011395</td>\n",
              "      <td>-0.050580</td>\n",
              "      <td>-0.118500</td>\n",
              "      <td>-0.133495</td>\n",
              "      <td>-0.080097</td>\n",
              "      <td>-0.049687</td>\n",
              "      <td>-0.070132</td>\n",
              "      <td>-0.098159</td>\n",
              "      <td>-0.087378</td>\n",
              "      <td>-0.126993</td>\n",
              "      <td>-0.145256</td>\n",
              "      <td>-0.306098</td>\n",
              "      <td>-0.078201</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.163084</td>\n",
              "      <td>-0.027327</td>\n",
              "      <td>-0.092587</td>\n",
              "      <td>-0.370598</td>\n",
              "      <td>0.034476</td>\n",
              "      <td>0.002004</td>\n",
              "      <td>-0.049858</td>\n",
              "      <td>-0.004674</td>\n",
              "      <td>0.028385</td>\n",
              "      <td>0.035824</td>\n",
              "      <td>0.030630</td>\n",
              "      <td>0.118851</td>\n",
              "      <td>-0.100230</td>\n",
              "      <td>0.031864</td>\n",
              "      <td>0.055365</td>\n",
              "      <td>0.103210</td>\n",
              "      <td>-0.012647</td>\n",
              "      <td>0.047570</td>\n",
              "      <td>-0.217372</td>\n",
              "      <td>-0.022520</td>\n",
              "      <td>-0.055391</td>\n",
              "      <td>0.094300</td>\n",
              "      <td>0.003242</td>\n",
              "      <td>-0.024325</td>\n",
              "      <td>0.028683</td>\n",
              "      <td>-0.023641</td>\n",
              "      <td>0.042857</td>\n",
              "      <td>-0.002358</td>\n",
              "      <td>-0.040777</td>\n",
              "      <td>0.037145</td>\n",
              "      <td>0.004614</td>\n",
              "      <td>-0.012425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ Masters</th>\n",
              "      <td>0.094761</td>\n",
              "      <td>-0.022229</td>\n",
              "      <td>0.009902</td>\n",
              "      <td>0.047527</td>\n",
              "      <td>0.046586</td>\n",
              "      <td>0.065068</td>\n",
              "      <td>-0.007438</td>\n",
              "      <td>0.174570</td>\n",
              "      <td>-0.029234</td>\n",
              "      <td>0.013139</td>\n",
              "      <td>0.129354</td>\n",
              "      <td>-0.091644</td>\n",
              "      <td>0.014075</td>\n",
              "      <td>-0.005350</td>\n",
              "      <td>0.070344</td>\n",
              "      <td>-0.040598</td>\n",
              "      <td>-0.045735</td>\n",
              "      <td>-0.027441</td>\n",
              "      <td>-0.017023</td>\n",
              "      <td>-0.024027</td>\n",
              "      <td>-0.033629</td>\n",
              "      <td>-0.029936</td>\n",
              "      <td>-0.043508</td>\n",
              "      <td>-0.049765</td>\n",
              "      <td>-0.104869</td>\n",
              "      <td>-0.026792</td>\n",
              "      <td>-0.163084</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.009362</td>\n",
              "      <td>-0.031720</td>\n",
              "      <td>-0.126967</td>\n",
              "      <td>-0.000842</td>\n",
              "      <td>0.057950</td>\n",
              "      <td>-0.047131</td>\n",
              "      <td>-0.007520</td>\n",
              "      <td>-0.022973</td>\n",
              "      <td>-0.009212</td>\n",
              "      <td>-0.056379</td>\n",
              "      <td>-0.080604</td>\n",
              "      <td>0.118624</td>\n",
              "      <td>-0.033970</td>\n",
              "      <td>-0.046122</td>\n",
              "      <td>-0.055932</td>\n",
              "      <td>-0.037744</td>\n",
              "      <td>-0.070670</td>\n",
              "      <td>0.257349</td>\n",
              "      <td>-0.025718</td>\n",
              "      <td>-0.009981</td>\n",
              "      <td>-0.047329</td>\n",
              "      <td>0.052502</td>\n",
              "      <td>0.024401</td>\n",
              "      <td>-0.032833</td>\n",
              "      <td>-0.080294</td>\n",
              "      <td>-0.018440</td>\n",
              "      <td>0.018600</td>\n",
              "      <td>0.025774</td>\n",
              "      <td>-0.036943</td>\n",
              "      <td>-0.019463</td>\n",
              "      <td>0.025307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ Preschool</th>\n",
              "      <td>0.012146</td>\n",
              "      <td>0.017303</td>\n",
              "      <td>0.001437</td>\n",
              "      <td>-0.000961</td>\n",
              "      <td>-0.002046</td>\n",
              "      <td>-0.012159</td>\n",
              "      <td>-0.072943</td>\n",
              "      <td>-0.022307</td>\n",
              "      <td>0.007152</td>\n",
              "      <td>-0.006903</td>\n",
              "      <td>0.002286</td>\n",
              "      <td>0.009213</td>\n",
              "      <td>-0.007534</td>\n",
              "      <td>-0.011523</td>\n",
              "      <td>-0.004101</td>\n",
              "      <td>-0.006803</td>\n",
              "      <td>-0.007664</td>\n",
              "      <td>-0.004598</td>\n",
              "      <td>-0.002852</td>\n",
              "      <td>-0.004026</td>\n",
              "      <td>-0.005635</td>\n",
              "      <td>-0.005016</td>\n",
              "      <td>-0.007290</td>\n",
              "      <td>-0.008339</td>\n",
              "      <td>-0.017572</td>\n",
              "      <td>-0.004489</td>\n",
              "      <td>-0.027327</td>\n",
              "      <td>-0.009362</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005315</td>\n",
              "      <td>-0.021275</td>\n",
              "      <td>-0.013482</td>\n",
              "      <td>-0.005386</td>\n",
              "      <td>0.008713</td>\n",
              "      <td>0.022235</td>\n",
              "      <td>-0.002693</td>\n",
              "      <td>0.006525</td>\n",
              "      <td>-0.009478</td>\n",
              "      <td>-0.005666</td>\n",
              "      <td>-0.014962</td>\n",
              "      <td>0.033601</td>\n",
              "      <td>-0.000564</td>\n",
              "      <td>0.025425</td>\n",
              "      <td>0.008093</td>\n",
              "      <td>0.025337</td>\n",
              "      <td>-0.012785</td>\n",
              "      <td>-0.014073</td>\n",
              "      <td>-0.006784</td>\n",
              "      <td>-0.008995</td>\n",
              "      <td>-0.010542</td>\n",
              "      <td>0.014239</td>\n",
              "      <td>0.015735</td>\n",
              "      <td>-0.004152</td>\n",
              "      <td>-0.008577</td>\n",
              "      <td>0.001974</td>\n",
              "      <td>0.019321</td>\n",
              "      <td>0.000282</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>-0.012256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ Prof-school</th>\n",
              "      <td>0.060651</td>\n",
              "      <td>-0.005231</td>\n",
              "      <td>0.048793</td>\n",
              "      <td>0.169658</td>\n",
              "      <td>0.047923</td>\n",
              "      <td>0.075948</td>\n",
              "      <td>-0.010689</td>\n",
              "      <td>0.154907</td>\n",
              "      <td>-0.014624</td>\n",
              "      <td>0.016552</td>\n",
              "      <td>-0.007623</td>\n",
              "      <td>-0.073253</td>\n",
              "      <td>0.077272</td>\n",
              "      <td>0.074741</td>\n",
              "      <td>0.009573</td>\n",
              "      <td>-0.023049</td>\n",
              "      <td>-0.025965</td>\n",
              "      <td>-0.015579</td>\n",
              "      <td>-0.009664</td>\n",
              "      <td>-0.013641</td>\n",
              "      <td>-0.019092</td>\n",
              "      <td>-0.016995</td>\n",
              "      <td>-0.024701</td>\n",
              "      <td>-0.028253</td>\n",
              "      <td>-0.059537</td>\n",
              "      <td>-0.015210</td>\n",
              "      <td>-0.092587</td>\n",
              "      <td>-0.031720</td>\n",
              "      <td>-0.005315</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.072082</td>\n",
              "      <td>-0.016015</td>\n",
              "      <td>0.068752</td>\n",
              "      <td>-0.047626</td>\n",
              "      <td>-0.009677</td>\n",
              "      <td>-0.013519</td>\n",
              "      <td>-0.017026</td>\n",
              "      <td>-0.042007</td>\n",
              "      <td>-0.046010</td>\n",
              "      <td>-0.014044</td>\n",
              "      <td>-0.018396</td>\n",
              "      <td>-0.028124</td>\n",
              "      <td>-0.033378</td>\n",
              "      <td>-0.023754</td>\n",
              "      <td>-0.041938</td>\n",
              "      <td>0.264888</td>\n",
              "      <td>-0.034389</td>\n",
              "      <td>-0.013184</td>\n",
              "      <td>-0.027240</td>\n",
              "      <td>0.071953</td>\n",
              "      <td>-0.012782</td>\n",
              "      <td>-0.018200</td>\n",
              "      <td>-0.048619</td>\n",
              "      <td>-0.027991</td>\n",
              "      <td>-0.001891</td>\n",
              "      <td>0.029984</td>\n",
              "      <td>-0.031851</td>\n",
              "      <td>-0.007553</td>\n",
              "      <td>0.014486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_ Some-college</th>\n",
              "      <td>-0.111262</td>\n",
              "      <td>-0.005269</td>\n",
              "      <td>-0.061722</td>\n",
              "      <td>-0.034826</td>\n",
              "      <td>-0.020884</td>\n",
              "      <td>-0.068960</td>\n",
              "      <td>0.050243</td>\n",
              "      <td>-0.063535</td>\n",
              "      <td>0.032861</td>\n",
              "      <td>0.017002</td>\n",
              "      <td>-0.024531</td>\n",
              "      <td>0.001916</td>\n",
              "      <td>-0.009469</td>\n",
              "      <td>-0.022790</td>\n",
              "      <td>0.012937</td>\n",
              "      <td>-0.092256</td>\n",
              "      <td>-0.103930</td>\n",
              "      <td>-0.062358</td>\n",
              "      <td>-0.038683</td>\n",
              "      <td>-0.054600</td>\n",
              "      <td>-0.076420</td>\n",
              "      <td>-0.068027</td>\n",
              "      <td>-0.098869</td>\n",
              "      <td>-0.113087</td>\n",
              "      <td>-0.238308</td>\n",
              "      <td>-0.060882</td>\n",
              "      <td>-0.370598</td>\n",
              "      <td>-0.126967</td>\n",
              "      <td>-0.021275</td>\n",
              "      <td>-0.072082</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015910</td>\n",
              "      <td>-0.079143</td>\n",
              "      <td>0.084870</td>\n",
              "      <td>-0.012587</td>\n",
              "      <td>-0.004015</td>\n",
              "      <td>-0.021573</td>\n",
              "      <td>0.100580</td>\n",
              "      <td>-0.011069</td>\n",
              "      <td>-0.007009</td>\n",
              "      <td>-0.020802</td>\n",
              "      <td>-0.014593</td>\n",
              "      <td>-0.042410</td>\n",
              "      <td>0.038427</td>\n",
              "      <td>0.010550</td>\n",
              "      <td>-0.109915</td>\n",
              "      <td>0.044765</td>\n",
              "      <td>0.028869</td>\n",
              "      <td>-0.025447</td>\n",
              "      <td>-0.072055</td>\n",
              "      <td>-0.010587</td>\n",
              "      <td>-0.009768</td>\n",
              "      <td>0.119939</td>\n",
              "      <td>0.004162</td>\n",
              "      <td>-0.014488</td>\n",
              "      <td>-0.010333</td>\n",
              "      <td>0.011627</td>\n",
              "      <td>-0.000178</td>\n",
              "      <td>-0.004491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_status_ Divorced</th>\n",
              "      <td>0.129975</td>\n",
              "      <td>-0.014973</td>\n",
              "      <td>-0.228621</td>\n",
              "      <td>-0.018798</td>\n",
              "      <td>-0.022396</td>\n",
              "      <td>0.026052</td>\n",
              "      <td>0.053224</td>\n",
              "      <td>-0.126995</td>\n",
              "      <td>-0.025803</td>\n",
              "      <td>0.019573</td>\n",
              "      <td>0.030427</td>\n",
              "      <td>0.004302</td>\n",
              "      <td>-0.026389</td>\n",
              "      <td>-0.018253</td>\n",
              "      <td>0.015039</td>\n",
              "      <td>-0.003920</td>\n",
              "      <td>-0.014550</td>\n",
              "      <td>-0.015686</td>\n",
              "      <td>-0.016139</td>\n",
              "      <td>-0.022621</td>\n",
              "      <td>-0.009719</td>\n",
              "      <td>-0.004404</td>\n",
              "      <td>0.028849</td>\n",
              "      <td>0.020159</td>\n",
              "      <td>-0.044578</td>\n",
              "      <td>-0.018672</td>\n",
              "      <td>0.034476</td>\n",
              "      <td>-0.000842</td>\n",
              "      <td>-0.013482</td>\n",
              "      <td>-0.016015</td>\n",
              "      <td>0.015910</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.366837</td>\n",
              "      <td>-0.277772</td>\n",
              "      <td>-0.046578</td>\n",
              "      <td>-0.071665</td>\n",
              "      <td>-0.070501</td>\n",
              "      <td>0.085166</td>\n",
              "      <td>-0.025707</td>\n",
              "      <td>0.013312</td>\n",
              "      <td>-0.037254</td>\n",
              "      <td>-0.026266</td>\n",
              "      <td>0.001424</td>\n",
              "      <td>-0.022773</td>\n",
              "      <td>0.015246</td>\n",
              "      <td>-0.006958</td>\n",
              "      <td>-0.018163</td>\n",
              "      <td>0.007190</td>\n",
              "      <td>-0.015292</td>\n",
              "      <td>-0.328077</td>\n",
              "      <td>0.260828</td>\n",
              "      <td>-0.012488</td>\n",
              "      <td>-0.089719</td>\n",
              "      <td>0.328876</td>\n",
              "      <td>-0.089410</td>\n",
              "      <td>-0.033990</td>\n",
              "      <td>0.017840</td>\n",
              "      <td>0.004447</td>\n",
              "      <td>0.000371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_status_ Married-civ-spouse</th>\n",
              "      <td>0.315679</td>\n",
              "      <td>-0.026363</td>\n",
              "      <td>0.431805</td>\n",
              "      <td>0.085873</td>\n",
              "      <td>0.080466</td>\n",
              "      <td>0.212817</td>\n",
              "      <td>-0.009755</td>\n",
              "      <td>0.444696</td>\n",
              "      <td>-0.055685</td>\n",
              "      <td>0.010732</td>\n",
              "      <td>0.015164</td>\n",
              "      <td>-0.094767</td>\n",
              "      <td>0.108440</td>\n",
              "      <td>0.117462</td>\n",
              "      <td>-0.002834</td>\n",
              "      <td>-0.029594</td>\n",
              "      <td>-0.061595</td>\n",
              "      <td>-0.037201</td>\n",
              "      <td>0.003209</td>\n",
              "      <td>0.011540</td>\n",
              "      <td>0.027345</td>\n",
              "      <td>-0.003168</td>\n",
              "      <td>-0.010644</td>\n",
              "      <td>0.016312</td>\n",
              "      <td>0.050707</td>\n",
              "      <td>0.052888</td>\n",
              "      <td>0.002004</td>\n",
              "      <td>0.057950</td>\n",
              "      <td>-0.005386</td>\n",
              "      <td>0.068752</td>\n",
              "      <td>-0.079143</td>\n",
              "      <td>-0.366837</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.644866</td>\n",
              "      <td>-0.108133</td>\n",
              "      <td>-0.166374</td>\n",
              "      <td>-0.163673</td>\n",
              "      <td>-0.144049</td>\n",
              "      <td>0.126080</td>\n",
              "      <td>0.106978</td>\n",
              "      <td>0.042203</td>\n",
              "      <td>-0.050066</td>\n",
              "      <td>0.018010</td>\n",
              "      <td>-0.040528</td>\n",
              "      <td>-0.161925</td>\n",
              "      <td>0.041039</td>\n",
              "      <td>-0.003080</td>\n",
              "      <td>-0.008451</td>\n",
              "      <td>0.074038</td>\n",
              "      <td>0.893210</td>\n",
              "      <td>-0.537588</td>\n",
              "      <td>-0.117950</td>\n",
              "      <td>-0.380070</td>\n",
              "      <td>-0.317487</td>\n",
              "      <td>0.240279</td>\n",
              "      <td>0.010562</td>\n",
              "      <td>-0.125506</td>\n",
              "      <td>-0.021712</td>\n",
              "      <td>0.107653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_status_ Never-married</th>\n",
              "      <td>-0.534359</td>\n",
              "      <td>0.037108</td>\n",
              "      <td>-0.171434</td>\n",
              "      <td>-0.066334</td>\n",
              "      <td>-0.060623</td>\n",
              "      <td>-0.197936</td>\n",
              "      <td>0.001833</td>\n",
              "      <td>-0.318440</td>\n",
              "      <td>0.046399</td>\n",
              "      <td>-0.027056</td>\n",
              "      <td>-0.041793</td>\n",
              "      <td>0.105278</td>\n",
              "      <td>-0.085174</td>\n",
              "      <td>-0.103563</td>\n",
              "      <td>-0.004301</td>\n",
              "      <td>0.021521</td>\n",
              "      <td>0.070317</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>-0.014717</td>\n",
              "      <td>-0.013168</td>\n",
              "      <td>-0.046413</td>\n",
              "      <td>-0.007158</td>\n",
              "      <td>-0.004804</td>\n",
              "      <td>-0.029663</td>\n",
              "      <td>0.006717</td>\n",
              "      <td>-0.036533</td>\n",
              "      <td>-0.049858</td>\n",
              "      <td>-0.047131</td>\n",
              "      <td>0.008713</td>\n",
              "      <td>-0.047626</td>\n",
              "      <td>0.084870</td>\n",
              "      <td>-0.277772</td>\n",
              "      <td>-0.644866</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.081879</td>\n",
              "      <td>-0.125980</td>\n",
              "      <td>-0.123935</td>\n",
              "      <td>0.072388</td>\n",
              "      <td>-0.093239</td>\n",
              "      <td>-0.105865</td>\n",
              "      <td>-0.014115</td>\n",
              "      <td>0.080318</td>\n",
              "      <td>-0.023374</td>\n",
              "      <td>0.031233</td>\n",
              "      <td>0.121444</td>\n",
              "      <td>-0.024406</td>\n",
              "      <td>0.025184</td>\n",
              "      <td>0.010429</td>\n",
              "      <td>-0.055718</td>\n",
              "      <td>-0.576730</td>\n",
              "      <td>0.297300</td>\n",
              "      <td>0.110641</td>\n",
              "      <td>0.509226</td>\n",
              "      <td>-0.053074</td>\n",
              "      <td>-0.157175</td>\n",
              "      <td>0.011579</td>\n",
              "      <td>0.071303</td>\n",
              "      <td>0.008418</td>\n",
              "      <td>-0.068446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_status_ Other</th>\n",
              "      <td>0.013528</td>\n",
              "      <td>0.003446</td>\n",
              "      <td>-0.041292</td>\n",
              "      <td>-0.006905</td>\n",
              "      <td>-0.009461</td>\n",
              "      <td>-0.006562</td>\n",
              "      <td>-0.103579</td>\n",
              "      <td>-0.038650</td>\n",
              "      <td>0.007065</td>\n",
              "      <td>0.001568</td>\n",
              "      <td>-0.006876</td>\n",
              "      <td>0.005556</td>\n",
              "      <td>-0.013604</td>\n",
              "      <td>-0.001401</td>\n",
              "      <td>0.000571</td>\n",
              "      <td>0.003764</td>\n",
              "      <td>0.004396</td>\n",
              "      <td>0.004953</td>\n",
              "      <td>0.036065</td>\n",
              "      <td>0.040907</td>\n",
              "      <td>0.010004</td>\n",
              "      <td>0.004345</td>\n",
              "      <td>-0.000673</td>\n",
              "      <td>-0.006218</td>\n",
              "      <td>-0.000378</td>\n",
              "      <td>0.003339</td>\n",
              "      <td>-0.004674</td>\n",
              "      <td>-0.007520</td>\n",
              "      <td>0.022235</td>\n",
              "      <td>-0.009677</td>\n",
              "      <td>-0.012587</td>\n",
              "      <td>-0.046578</td>\n",
              "      <td>-0.108133</td>\n",
              "      <td>-0.081879</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.021125</td>\n",
              "      <td>-0.020782</td>\n",
              "      <td>0.010745</td>\n",
              "      <td>-0.000413</td>\n",
              "      <td>-0.018542</td>\n",
              "      <td>0.017819</td>\n",
              "      <td>0.005883</td>\n",
              "      <td>-0.001233</td>\n",
              "      <td>0.004965</td>\n",
              "      <td>0.016187</td>\n",
              "      <td>-0.004842</td>\n",
              "      <td>-0.011315</td>\n",
              "      <td>-0.010489</td>\n",
              "      <td>-0.002005</td>\n",
              "      <td>-0.091837</td>\n",
              "      <td>0.060052</td>\n",
              "      <td>0.030642</td>\n",
              "      <td>-0.016594</td>\n",
              "      <td>0.071972</td>\n",
              "      <td>-0.011463</td>\n",
              "      <td>0.040708</td>\n",
              "      <td>0.018665</td>\n",
              "      <td>0.032322</td>\n",
              "      <td>-0.047996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_status_ Separated</th>\n",
              "      <td>0.010210</td>\n",
              "      <td>0.028467</td>\n",
              "      <td>-0.109135</td>\n",
              "      <td>-0.013233</td>\n",
              "      <td>-0.013061</td>\n",
              "      <td>-0.016587</td>\n",
              "      <td>-0.020300</td>\n",
              "      <td>-0.074386</td>\n",
              "      <td>0.006256</td>\n",
              "      <td>-0.004388</td>\n",
              "      <td>-0.002070</td>\n",
              "      <td>0.015135</td>\n",
              "      <td>-0.015131</td>\n",
              "      <td>-0.017698</td>\n",
              "      <td>0.001924</td>\n",
              "      <td>0.020695</td>\n",
              "      <td>0.010385</td>\n",
              "      <td>0.000567</td>\n",
              "      <td>0.009112</td>\n",
              "      <td>0.013142</td>\n",
              "      <td>0.003361</td>\n",
              "      <td>0.023734</td>\n",
              "      <td>-0.003545</td>\n",
              "      <td>-0.001313</td>\n",
              "      <td>-0.036332</td>\n",
              "      <td>-0.009432</td>\n",
              "      <td>0.028385</td>\n",
              "      <td>-0.022973</td>\n",
              "      <td>-0.002693</td>\n",
              "      <td>-0.013519</td>\n",
              "      <td>-0.004015</td>\n",
              "      <td>-0.071665</td>\n",
              "      <td>-0.166374</td>\n",
              "      <td>-0.125980</td>\n",
              "      <td>-0.021125</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.031975</td>\n",
              "      <td>0.015569</td>\n",
              "      <td>-0.013804</td>\n",
              "      <td>-0.018088</td>\n",
              "      <td>-0.013588</td>\n",
              "      <td>-0.004492</td>\n",
              "      <td>0.015360</td>\n",
              "      <td>0.006806</td>\n",
              "      <td>0.050316</td>\n",
              "      <td>-0.016539</td>\n",
              "      <td>-0.012209</td>\n",
              "      <td>-0.001282</td>\n",
              "      <td>-0.010810</td>\n",
              "      <td>-0.148795</td>\n",
              "      <td>0.063982</td>\n",
              "      <td>0.024817</td>\n",
              "      <td>-0.029372</td>\n",
              "      <td>0.195842</td>\n",
              "      <td>-0.040551</td>\n",
              "      <td>-0.013717</td>\n",
              "      <td>0.099531</td>\n",
              "      <td>0.007539</td>\n",
              "      <td>-0.079078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_status_ Widowed</th>\n",
              "      <td>0.265177</td>\n",
              "      <td>-0.026246</td>\n",
              "      <td>-0.188487</td>\n",
              "      <td>-0.012166</td>\n",
              "      <td>-0.003361</td>\n",
              "      <td>-0.107122</td>\n",
              "      <td>0.007259</td>\n",
              "      <td>-0.064381</td>\n",
              "      <td>0.075115</td>\n",
              "      <td>0.007099</td>\n",
              "      <td>0.016146</td>\n",
              "      <td>-0.040480</td>\n",
              "      <td>-0.004548</td>\n",
              "      <td>-0.002325</td>\n",
              "      <td>-0.012402</td>\n",
              "      <td>0.011291</td>\n",
              "      <td>0.002075</td>\n",
              "      <td>-0.004997</td>\n",
              "      <td>0.029608</td>\n",
              "      <td>0.006825</td>\n",
              "      <td>0.056739</td>\n",
              "      <td>0.010496</td>\n",
              "      <td>-0.009571</td>\n",
              "      <td>-0.001016</td>\n",
              "      <td>-0.039177</td>\n",
              "      <td>-0.008930</td>\n",
              "      <td>0.035824</td>\n",
              "      <td>-0.009212</td>\n",
              "      <td>0.006525</td>\n",
              "      <td>-0.017026</td>\n",
              "      <td>-0.021573</td>\n",
              "      <td>-0.070501</td>\n",
              "      <td>-0.163673</td>\n",
              "      <td>-0.123935</td>\n",
              "      <td>-0.020782</td>\n",
              "      <td>-0.031975</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.026810</td>\n",
              "      <td>-0.045231</td>\n",
              "      <td>-0.016749</td>\n",
              "      <td>-0.007593</td>\n",
              "      <td>-0.021156</td>\n",
              "      <td>-0.005989</td>\n",
              "      <td>0.067402</td>\n",
              "      <td>0.045314</td>\n",
              "      <td>-0.018366</td>\n",
              "      <td>-0.003574</td>\n",
              "      <td>-0.009983</td>\n",
              "      <td>-0.019603</td>\n",
              "      <td>-0.146380</td>\n",
              "      <td>0.120354</td>\n",
              "      <td>0.018894</td>\n",
              "      <td>-0.068758</td>\n",
              "      <td>0.161356</td>\n",
              "      <td>-0.039893</td>\n",
              "      <td>-0.007811</td>\n",
              "      <td>0.019848</td>\n",
              "      <td>0.001686</td>\n",
              "      <td>-0.013310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Adm-clerical</th>\n",
              "      <td>-0.042902</td>\n",
              "      <td>0.007765</td>\n",
              "      <td>-0.263148</td>\n",
              "      <td>-0.028502</td>\n",
              "      <td>-0.023806</td>\n",
              "      <td>-0.084378</td>\n",
              "      <td>0.022506</td>\n",
              "      <td>-0.089986</td>\n",
              "      <td>-0.088457</td>\n",
              "      <td>0.116805</td>\n",
              "      <td>0.015916</td>\n",
              "      <td>0.042859</td>\n",
              "      <td>-0.051055</td>\n",
              "      <td>-0.087386</td>\n",
              "      <td>0.050394</td>\n",
              "      <td>-0.040288</td>\n",
              "      <td>-0.035534</td>\n",
              "      <td>-0.010167</td>\n",
              "      <td>-0.026060</td>\n",
              "      <td>-0.031059</td>\n",
              "      <td>-0.043911</td>\n",
              "      <td>-0.035047</td>\n",
              "      <td>0.037449</td>\n",
              "      <td>0.003327</td>\n",
              "      <td>-0.029523</td>\n",
              "      <td>-0.036726</td>\n",
              "      <td>0.030630</td>\n",
              "      <td>-0.056379</td>\n",
              "      <td>-0.009478</td>\n",
              "      <td>-0.042007</td>\n",
              "      <td>0.100580</td>\n",
              "      <td>0.085166</td>\n",
              "      <td>-0.144049</td>\n",
              "      <td>0.072388</td>\n",
              "      <td>0.010745</td>\n",
              "      <td>0.015569</td>\n",
              "      <td>0.026810</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.137325</td>\n",
              "      <td>-0.136692</td>\n",
              "      <td>-0.064212</td>\n",
              "      <td>-0.075838</td>\n",
              "      <td>-0.092620</td>\n",
              "      <td>-0.107708</td>\n",
              "      <td>-0.121419</td>\n",
              "      <td>-0.138109</td>\n",
              "      <td>-0.128575</td>\n",
              "      <td>-0.061979</td>\n",
              "      <td>-0.082180</td>\n",
              "      <td>-0.179782</td>\n",
              "      <td>0.044354</td>\n",
              "      <td>0.010903</td>\n",
              "      <td>0.049039</td>\n",
              "      <td>0.106715</td>\n",
              "      <td>0.076867</td>\n",
              "      <td>0.010213</td>\n",
              "      <td>0.041812</td>\n",
              "      <td>-0.007524</td>\n",
              "      <td>-0.037164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Craft-repair</th>\n",
              "      <td>0.012515</td>\n",
              "      <td>0.008465</td>\n",
              "      <td>0.223128</td>\n",
              "      <td>-0.022000</td>\n",
              "      <td>0.001094</td>\n",
              "      <td>0.057375</td>\n",
              "      <td>0.003905</td>\n",
              "      <td>-0.012576</td>\n",
              "      <td>-0.092768</td>\n",
              "      <td>-0.031116</td>\n",
              "      <td>-0.044349</td>\n",
              "      <td>0.068070</td>\n",
              "      <td>-0.018223</td>\n",
              "      <td>0.072870</td>\n",
              "      <td>-0.050826</td>\n",
              "      <td>0.029161</td>\n",
              "      <td>0.013444</td>\n",
              "      <td>0.002822</td>\n",
              "      <td>0.002392</td>\n",
              "      <td>0.000994</td>\n",
              "      <td>0.023023</td>\n",
              "      <td>0.023244</td>\n",
              "      <td>-0.010048</td>\n",
              "      <td>0.035832</td>\n",
              "      <td>-0.111921</td>\n",
              "      <td>-0.041359</td>\n",
              "      <td>0.118851</td>\n",
              "      <td>-0.080604</td>\n",
              "      <td>-0.005666</td>\n",
              "      <td>-0.046010</td>\n",
              "      <td>-0.011069</td>\n",
              "      <td>-0.025707</td>\n",
              "      <td>0.126080</td>\n",
              "      <td>-0.093239</td>\n",
              "      <td>-0.000413</td>\n",
              "      <td>-0.013804</td>\n",
              "      <td>-0.045231</td>\n",
              "      <td>-0.137325</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.143353</td>\n",
              "      <td>-0.067342</td>\n",
              "      <td>-0.079534</td>\n",
              "      <td>-0.097133</td>\n",
              "      <td>-0.112957</td>\n",
              "      <td>-0.127336</td>\n",
              "      <td>-0.144839</td>\n",
              "      <td>-0.134841</td>\n",
              "      <td>-0.065000</td>\n",
              "      <td>-0.086185</td>\n",
              "      <td>0.160332</td>\n",
              "      <td>-0.044495</td>\n",
              "      <td>-0.012725</td>\n",
              "      <td>-0.065888</td>\n",
              "      <td>-0.056221</td>\n",
              "      <td>-0.074548</td>\n",
              "      <td>-0.022017</td>\n",
              "      <td>-0.046924</td>\n",
              "      <td>-0.000885</td>\n",
              "      <td>0.050468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Exec-managerial</th>\n",
              "      <td>0.099352</td>\n",
              "      <td>-0.019198</td>\n",
              "      <td>0.036729</td>\n",
              "      <td>0.060618</td>\n",
              "      <td>0.048311</td>\n",
              "      <td>0.139208</td>\n",
              "      <td>0.028118</td>\n",
              "      <td>0.214861</td>\n",
              "      <td>-0.092340</td>\n",
              "      <td>0.033020</td>\n",
              "      <td>-0.017940</td>\n",
              "      <td>-0.028935</td>\n",
              "      <td>0.130578</td>\n",
              "      <td>0.025872</td>\n",
              "      <td>0.012781</td>\n",
              "      <td>-0.051514</td>\n",
              "      <td>-0.056152</td>\n",
              "      <td>-0.033310</td>\n",
              "      <td>-0.022017</td>\n",
              "      <td>-0.037474</td>\n",
              "      <td>-0.041084</td>\n",
              "      <td>-0.038150</td>\n",
              "      <td>0.006137</td>\n",
              "      <td>-0.010403</td>\n",
              "      <td>0.175511</td>\n",
              "      <td>0.002845</td>\n",
              "      <td>-0.100230</td>\n",
              "      <td>0.118624</td>\n",
              "      <td>-0.014962</td>\n",
              "      <td>-0.014044</td>\n",
              "      <td>-0.007009</td>\n",
              "      <td>0.013312</td>\n",
              "      <td>0.106978</td>\n",
              "      <td>-0.105865</td>\n",
              "      <td>-0.018542</td>\n",
              "      <td>-0.018088</td>\n",
              "      <td>-0.016749</td>\n",
              "      <td>-0.136692</td>\n",
              "      <td>-0.143353</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.067031</td>\n",
              "      <td>-0.079167</td>\n",
              "      <td>-0.096686</td>\n",
              "      <td>-0.112436</td>\n",
              "      <td>-0.126749</td>\n",
              "      <td>-0.144172</td>\n",
              "      <td>-0.134219</td>\n",
              "      <td>-0.064700</td>\n",
              "      <td>-0.085787</td>\n",
              "      <td>0.102104</td>\n",
              "      <td>-0.009820</td>\n",
              "      <td>-0.040490</td>\n",
              "      <td>-0.101447</td>\n",
              "      <td>-0.020329</td>\n",
              "      <td>0.019179</td>\n",
              "      <td>0.002779</td>\n",
              "      <td>-0.046088</td>\n",
              "      <td>-0.022211</td>\n",
              "      <td>0.045427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Farming-fishing</th>\n",
              "      <td>0.034210</td>\n",
              "      <td>-0.032932</td>\n",
              "      <td>0.100097</td>\n",
              "      <td>-0.011724</td>\n",
              "      <td>-0.010670</td>\n",
              "      <td>0.094170</td>\n",
              "      <td>-0.006711</td>\n",
              "      <td>-0.051925</td>\n",
              "      <td>-0.043378</td>\n",
              "      <td>-0.022486</td>\n",
              "      <td>-0.025400</td>\n",
              "      <td>-0.092398</td>\n",
              "      <td>0.021677</td>\n",
              "      <td>0.234560</td>\n",
              "      <td>-0.022470</td>\n",
              "      <td>0.016605</td>\n",
              "      <td>0.001082</td>\n",
              "      <td>0.004335</td>\n",
              "      <td>0.032073</td>\n",
              "      <td>0.045840</td>\n",
              "      <td>0.064367</td>\n",
              "      <td>0.017629</td>\n",
              "      <td>-0.018624</td>\n",
              "      <td>0.008688</td>\n",
              "      <td>-0.041645</td>\n",
              "      <td>-0.018518</td>\n",
              "      <td>0.031864</td>\n",
              "      <td>-0.033970</td>\n",
              "      <td>0.033601</td>\n",
              "      <td>-0.018396</td>\n",
              "      <td>-0.020802</td>\n",
              "      <td>-0.037254</td>\n",
              "      <td>0.042203</td>\n",
              "      <td>-0.014115</td>\n",
              "      <td>0.017819</td>\n",
              "      <td>-0.013588</td>\n",
              "      <td>-0.007593</td>\n",
              "      <td>-0.064212</td>\n",
              "      <td>-0.067342</td>\n",
              "      <td>-0.067031</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.037190</td>\n",
              "      <td>-0.045419</td>\n",
              "      <td>-0.052818</td>\n",
              "      <td>-0.059542</td>\n",
              "      <td>-0.067726</td>\n",
              "      <td>-0.063051</td>\n",
              "      <td>-0.030393</td>\n",
              "      <td>-0.040300</td>\n",
              "      <td>0.055002</td>\n",
              "      <td>-0.028885</td>\n",
              "      <td>0.001099</td>\n",
              "      <td>-0.003306</td>\n",
              "      <td>-0.024488</td>\n",
              "      <td>-0.027406</td>\n",
              "      <td>-0.015965</td>\n",
              "      <td>-0.032349</td>\n",
              "      <td>0.004356</td>\n",
              "      <td>0.033319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Handlers-cleaners</th>\n",
              "      <td>-0.098579</td>\n",
              "      <td>0.029015</td>\n",
              "      <td>0.094026</td>\n",
              "      <td>-0.023272</td>\n",
              "      <td>-0.021672</td>\n",
              "      <td>-0.042265</td>\n",
              "      <td>-0.019194</td>\n",
              "      <td>-0.087265</td>\n",
              "      <td>-0.051231</td>\n",
              "      <td>-0.015728</td>\n",
              "      <td>-0.025613</td>\n",
              "      <td>0.105882</td>\n",
              "      <td>-0.037365</td>\n",
              "      <td>-0.052419</td>\n",
              "      <td>-0.035667</td>\n",
              "      <td>0.029108</td>\n",
              "      <td>0.060338</td>\n",
              "      <td>0.026418</td>\n",
              "      <td>0.019071</td>\n",
              "      <td>0.039516</td>\n",
              "      <td>0.020645</td>\n",
              "      <td>0.033595</td>\n",
              "      <td>-0.017953</td>\n",
              "      <td>-0.022876</td>\n",
              "      <td>-0.072347</td>\n",
              "      <td>-0.023754</td>\n",
              "      <td>0.055365</td>\n",
              "      <td>-0.046122</td>\n",
              "      <td>-0.000564</td>\n",
              "      <td>-0.028124</td>\n",
              "      <td>-0.014593</td>\n",
              "      <td>-0.026266</td>\n",
              "      <td>-0.050066</td>\n",
              "      <td>0.080318</td>\n",
              "      <td>0.005883</td>\n",
              "      <td>-0.004492</td>\n",
              "      <td>-0.021156</td>\n",
              "      <td>-0.075838</td>\n",
              "      <td>-0.079534</td>\n",
              "      <td>-0.079167</td>\n",
              "      <td>-0.037190</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.053642</td>\n",
              "      <td>-0.062381</td>\n",
              "      <td>-0.070322</td>\n",
              "      <td>-0.079988</td>\n",
              "      <td>-0.074466</td>\n",
              "      <td>-0.035896</td>\n",
              "      <td>-0.047596</td>\n",
              "      <td>-0.036799</td>\n",
              "      <td>-0.014189</td>\n",
              "      <td>0.044499</td>\n",
              "      <td>0.086407</td>\n",
              "      <td>-0.022870</td>\n",
              "      <td>-0.035707</td>\n",
              "      <td>-0.018031</td>\n",
              "      <td>0.024703</td>\n",
              "      <td>0.010983</td>\n",
              "      <td>-0.015762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Machine-op-inspct</th>\n",
              "      <td>-0.016257</td>\n",
              "      <td>0.012762</td>\n",
              "      <td>0.030502</td>\n",
              "      <td>-0.025957</td>\n",
              "      <td>-0.016259</td>\n",
              "      <td>0.006598</td>\n",
              "      <td>-0.044580</td>\n",
              "      <td>-0.069401</td>\n",
              "      <td>-0.062568</td>\n",
              "      <td>-0.034030</td>\n",
              "      <td>-0.060829</td>\n",
              "      <td>0.143986</td>\n",
              "      <td>-0.038937</td>\n",
              "      <td>-0.057307</td>\n",
              "      <td>-0.043658</td>\n",
              "      <td>0.033439</td>\n",
              "      <td>0.018341</td>\n",
              "      <td>0.009350</td>\n",
              "      <td>0.022611</td>\n",
              "      <td>0.045144</td>\n",
              "      <td>0.048849</td>\n",
              "      <td>0.045538</td>\n",
              "      <td>-0.023414</td>\n",
              "      <td>-0.013934</td>\n",
              "      <td>-0.089758</td>\n",
              "      <td>-0.027868</td>\n",
              "      <td>0.103210</td>\n",
              "      <td>-0.055932</td>\n",
              "      <td>0.025425</td>\n",
              "      <td>-0.033378</td>\n",
              "      <td>-0.042410</td>\n",
              "      <td>0.001424</td>\n",
              "      <td>0.018010</td>\n",
              "      <td>-0.023374</td>\n",
              "      <td>-0.001233</td>\n",
              "      <td>0.015360</td>\n",
              "      <td>-0.005989</td>\n",
              "      <td>-0.092620</td>\n",
              "      <td>-0.097133</td>\n",
              "      <td>-0.096686</td>\n",
              "      <td>-0.045419</td>\n",
              "      <td>-0.053642</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.076185</td>\n",
              "      <td>-0.085883</td>\n",
              "      <td>-0.097688</td>\n",
              "      <td>-0.090945</td>\n",
              "      <td>-0.043840</td>\n",
              "      <td>-0.058128</td>\n",
              "      <td>0.020270</td>\n",
              "      <td>-0.014557</td>\n",
              "      <td>0.011730</td>\n",
              "      <td>-0.017141</td>\n",
              "      <td>0.007117</td>\n",
              "      <td>-0.007409</td>\n",
              "      <td>-0.003552</td>\n",
              "      <td>0.035563</td>\n",
              "      <td>0.021437</td>\n",
              "      <td>-0.035965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Other</th>\n",
              "      <td>0.040149</td>\n",
              "      <td>0.008314</td>\n",
              "      <td>-0.043304</td>\n",
              "      <td>-0.018866</td>\n",
              "      <td>-0.017805</td>\n",
              "      <td>-0.138884</td>\n",
              "      <td>-0.001111</td>\n",
              "      <td>-0.061508</td>\n",
              "      <td>0.821264</td>\n",
              "      <td>-0.027311</td>\n",
              "      <td>0.061214</td>\n",
              "      <td>-0.368617</td>\n",
              "      <td>-0.049276</td>\n",
              "      <td>-0.084085</td>\n",
              "      <td>0.005949</td>\n",
              "      <td>0.025629</td>\n",
              "      <td>0.026723</td>\n",
              "      <td>0.015454</td>\n",
              "      <td>0.016191</td>\n",
              "      <td>0.019982</td>\n",
              "      <td>0.030144</td>\n",
              "      <td>0.020877</td>\n",
              "      <td>-0.002422</td>\n",
              "      <td>0.000292</td>\n",
              "      <td>-0.046911</td>\n",
              "      <td>-0.018681</td>\n",
              "      <td>-0.012647</td>\n",
              "      <td>-0.037744</td>\n",
              "      <td>0.008093</td>\n",
              "      <td>-0.023754</td>\n",
              "      <td>0.038427</td>\n",
              "      <td>-0.022773</td>\n",
              "      <td>-0.040528</td>\n",
              "      <td>0.031233</td>\n",
              "      <td>0.004965</td>\n",
              "      <td>0.006806</td>\n",
              "      <td>0.067402</td>\n",
              "      <td>-0.107708</td>\n",
              "      <td>-0.112957</td>\n",
              "      <td>-0.112436</td>\n",
              "      <td>-0.052818</td>\n",
              "      <td>-0.062381</td>\n",
              "      <td>-0.076185</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.099874</td>\n",
              "      <td>-0.113603</td>\n",
              "      <td>-0.105760</td>\n",
              "      <td>-0.050981</td>\n",
              "      <td>-0.067598</td>\n",
              "      <td>-0.048212</td>\n",
              "      <td>-0.007191</td>\n",
              "      <td>0.011276</td>\n",
              "      <td>0.072358</td>\n",
              "      <td>-0.012215</td>\n",
              "      <td>0.011220</td>\n",
              "      <td>-0.000358</td>\n",
              "      <td>0.034992</td>\n",
              "      <td>0.014949</td>\n",
              "      <td>-0.034644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Other-service</th>\n",
              "      <td>-0.089346</td>\n",
              "      <td>-0.003719</td>\n",
              "      <td>-0.153677</td>\n",
              "      <td>-0.040271</td>\n",
              "      <td>-0.040847</td>\n",
              "      <td>-0.155872</td>\n",
              "      <td>-0.058293</td>\n",
              "      <td>-0.156348</td>\n",
              "      <td>-0.082023</td>\n",
              "      <td>-0.037413</td>\n",
              "      <td>-0.007806</td>\n",
              "      <td>0.098231</td>\n",
              "      <td>-0.048293</td>\n",
              "      <td>-0.031183</td>\n",
              "      <td>-0.003826</td>\n",
              "      <td>0.060787</td>\n",
              "      <td>0.065028</td>\n",
              "      <td>0.036611</td>\n",
              "      <td>0.032691</td>\n",
              "      <td>0.030671</td>\n",
              "      <td>0.023827</td>\n",
              "      <td>0.040021</td>\n",
              "      <td>-0.017145</td>\n",
              "      <td>-0.012553</td>\n",
              "      <td>-0.099143</td>\n",
              "      <td>-0.037122</td>\n",
              "      <td>0.047570</td>\n",
              "      <td>-0.070670</td>\n",
              "      <td>0.025337</td>\n",
              "      <td>-0.041938</td>\n",
              "      <td>0.010550</td>\n",
              "      <td>0.015246</td>\n",
              "      <td>-0.161925</td>\n",
              "      <td>0.121444</td>\n",
              "      <td>0.016187</td>\n",
              "      <td>0.050316</td>\n",
              "      <td>0.045314</td>\n",
              "      <td>-0.121419</td>\n",
              "      <td>-0.127336</td>\n",
              "      <td>-0.126749</td>\n",
              "      <td>-0.059542</td>\n",
              "      <td>-0.070322</td>\n",
              "      <td>-0.085883</td>\n",
              "      <td>-0.099874</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.128064</td>\n",
              "      <td>-0.119223</td>\n",
              "      <td>-0.057471</td>\n",
              "      <td>-0.076203</td>\n",
              "      <td>-0.169899</td>\n",
              "      <td>0.028637</td>\n",
              "      <td>0.054049</td>\n",
              "      <td>0.106223</td>\n",
              "      <td>0.067294</td>\n",
              "      <td>0.011571</td>\n",
              "      <td>0.013244</td>\n",
              "      <td>0.088125</td>\n",
              "      <td>0.010841</td>\n",
              "      <td>-0.084228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Prof-specialty</th>\n",
              "      <td>0.054170</td>\n",
              "      <td>-0.016206</td>\n",
              "      <td>-0.028511</td>\n",
              "      <td>0.085222</td>\n",
              "      <td>0.046255</td>\n",
              "      <td>0.060253</td>\n",
              "      <td>-0.004783</td>\n",
              "      <td>0.185866</td>\n",
              "      <td>-0.093298</td>\n",
              "      <td>0.028852</td>\n",
              "      <td>0.164976</td>\n",
              "      <td>-0.114891</td>\n",
              "      <td>0.007751</td>\n",
              "      <td>0.017158</td>\n",
              "      <td>0.117317</td>\n",
              "      <td>-0.060579</td>\n",
              "      <td>-0.063961</td>\n",
              "      <td>-0.036260</td>\n",
              "      <td>-0.022339</td>\n",
              "      <td>-0.037880</td>\n",
              "      <td>-0.048350</td>\n",
              "      <td>-0.046117</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>-0.002614</td>\n",
              "      <td>0.202470</td>\n",
              "      <td>0.221183</td>\n",
              "      <td>-0.217372</td>\n",
              "      <td>0.257349</td>\n",
              "      <td>-0.012785</td>\n",
              "      <td>0.264888</td>\n",
              "      <td>-0.109915</td>\n",
              "      <td>-0.006958</td>\n",
              "      <td>0.041039</td>\n",
              "      <td>-0.024406</td>\n",
              "      <td>-0.004842</td>\n",
              "      <td>-0.016539</td>\n",
              "      <td>-0.018366</td>\n",
              "      <td>-0.138109</td>\n",
              "      <td>-0.144839</td>\n",
              "      <td>-0.144172</td>\n",
              "      <td>-0.067726</td>\n",
              "      <td>-0.079988</td>\n",
              "      <td>-0.097688</td>\n",
              "      <td>-0.113603</td>\n",
              "      <td>-0.128064</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.135611</td>\n",
              "      <td>-0.065371</td>\n",
              "      <td>-0.086677</td>\n",
              "      <td>0.023767</td>\n",
              "      <td>0.045482</td>\n",
              "      <td>-0.035988</td>\n",
              "      <td>-0.083506</td>\n",
              "      <td>-0.016226</td>\n",
              "      <td>0.046348</td>\n",
              "      <td>0.028269</td>\n",
              "      <td>-0.049521</td>\n",
              "      <td>-0.006957</td>\n",
              "      <td>0.029867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Sales</th>\n",
              "      <td>-0.031980</td>\n",
              "      <td>0.003728</td>\n",
              "      <td>-0.011504</td>\n",
              "      <td>0.011652</td>\n",
              "      <td>0.009697</td>\n",
              "      <td>0.009889</td>\n",
              "      <td>0.029997</td>\n",
              "      <td>0.023688</td>\n",
              "      <td>-0.086857</td>\n",
              "      <td>-0.053873</td>\n",
              "      <td>-0.090349</td>\n",
              "      <td>0.084276</td>\n",
              "      <td>0.086726</td>\n",
              "      <td>0.036350</td>\n",
              "      <td>-0.066926</td>\n",
              "      <td>-0.013763</td>\n",
              "      <td>0.006413</td>\n",
              "      <td>-0.001307</td>\n",
              "      <td>-0.014718</td>\n",
              "      <td>-0.024507</td>\n",
              "      <td>-0.030307</td>\n",
              "      <td>-0.020007</td>\n",
              "      <td>0.013337</td>\n",
              "      <td>-0.023621</td>\n",
              "      <td>0.054811</td>\n",
              "      <td>-0.033314</td>\n",
              "      <td>-0.022520</td>\n",
              "      <td>-0.025718</td>\n",
              "      <td>-0.014073</td>\n",
              "      <td>-0.034389</td>\n",
              "      <td>0.044765</td>\n",
              "      <td>-0.018163</td>\n",
              "      <td>-0.003080</td>\n",
              "      <td>0.025184</td>\n",
              "      <td>-0.011315</td>\n",
              "      <td>-0.012209</td>\n",
              "      <td>-0.003574</td>\n",
              "      <td>-0.128575</td>\n",
              "      <td>-0.134841</td>\n",
              "      <td>-0.134219</td>\n",
              "      <td>-0.063051</td>\n",
              "      <td>-0.074466</td>\n",
              "      <td>-0.090945</td>\n",
              "      <td>-0.105760</td>\n",
              "      <td>-0.119223</td>\n",
              "      <td>-0.135611</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.060858</td>\n",
              "      <td>-0.080694</td>\n",
              "      <td>0.005374</td>\n",
              "      <td>-0.011159</td>\n",
              "      <td>-0.003398</td>\n",
              "      <td>0.038102</td>\n",
              "      <td>-0.021293</td>\n",
              "      <td>-0.020810</td>\n",
              "      <td>-0.004691</td>\n",
              "      <td>-0.031795</td>\n",
              "      <td>-0.010463</td>\n",
              "      <td>0.032805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Tech-support</th>\n",
              "      <td>-0.019576</td>\n",
              "      <td>0.003765</td>\n",
              "      <td>-0.016092</td>\n",
              "      <td>-0.009372</td>\n",
              "      <td>0.004830</td>\n",
              "      <td>-0.013946</td>\n",
              "      <td>0.011266</td>\n",
              "      <td>0.025696</td>\n",
              "      <td>-0.041869</td>\n",
              "      <td>0.044342</td>\n",
              "      <td>-0.016294</td>\n",
              "      <td>0.035808</td>\n",
              "      <td>-0.029564</td>\n",
              "      <td>-0.031941</td>\n",
              "      <td>0.018874</td>\n",
              "      <td>-0.026099</td>\n",
              "      <td>-0.027202</td>\n",
              "      <td>-0.015050</td>\n",
              "      <td>-0.012335</td>\n",
              "      <td>-0.015576</td>\n",
              "      <td>-0.017750</td>\n",
              "      <td>-0.018730</td>\n",
              "      <td>0.044154</td>\n",
              "      <td>0.079296</td>\n",
              "      <td>0.038528</td>\n",
              "      <td>-0.014465</td>\n",
              "      <td>-0.055391</td>\n",
              "      <td>-0.009981</td>\n",
              "      <td>-0.006784</td>\n",
              "      <td>-0.013184</td>\n",
              "      <td>0.028869</td>\n",
              "      <td>0.007190</td>\n",
              "      <td>-0.008451</td>\n",
              "      <td>0.010429</td>\n",
              "      <td>-0.010489</td>\n",
              "      <td>-0.001282</td>\n",
              "      <td>-0.009983</td>\n",
              "      <td>-0.061979</td>\n",
              "      <td>-0.065000</td>\n",
              "      <td>-0.064700</td>\n",
              "      <td>-0.030393</td>\n",
              "      <td>-0.035896</td>\n",
              "      <td>-0.043840</td>\n",
              "      <td>-0.050981</td>\n",
              "      <td>-0.057471</td>\n",
              "      <td>-0.065371</td>\n",
              "      <td>-0.060858</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.038898</td>\n",
              "      <td>-0.009777</td>\n",
              "      <td>0.020030</td>\n",
              "      <td>-0.005354</td>\n",
              "      <td>-0.012443</td>\n",
              "      <td>0.002273</td>\n",
              "      <td>0.003717</td>\n",
              "      <td>0.015109</td>\n",
              "      <td>-0.011302</td>\n",
              "      <td>-0.013355</td>\n",
              "      <td>0.006923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation_ Transport-moving</th>\n",
              "      <td>0.026909</td>\n",
              "      <td>0.001265</td>\n",
              "      <td>0.132468</td>\n",
              "      <td>-0.018061</td>\n",
              "      <td>-0.003282</td>\n",
              "      <td>0.077596</td>\n",
              "      <td>0.028081</td>\n",
              "      <td>-0.021476</td>\n",
              "      <td>-0.055516</td>\n",
              "      <td>-0.018566</td>\n",
              "      <td>0.007159</td>\n",
              "      <td>0.047298</td>\n",
              "      <td>-0.021509</td>\n",
              "      <td>-0.001393</td>\n",
              "      <td>-0.016473</td>\n",
              "      <td>0.032595</td>\n",
              "      <td>0.026207</td>\n",
              "      <td>0.022052</td>\n",
              "      <td>-0.000476</td>\n",
              "      <td>0.016491</td>\n",
              "      <td>0.028876</td>\n",
              "      <td>0.011169</td>\n",
              "      <td>-0.020235</td>\n",
              "      <td>-0.019597</td>\n",
              "      <td>-0.076971</td>\n",
              "      <td>-0.024470</td>\n",
              "      <td>0.094300</td>\n",
              "      <td>-0.047329</td>\n",
              "      <td>-0.008995</td>\n",
              "      <td>-0.027240</td>\n",
              "      <td>-0.025447</td>\n",
              "      <td>-0.015292</td>\n",
              "      <td>0.074038</td>\n",
              "      <td>-0.055718</td>\n",
              "      <td>-0.002005</td>\n",
              "      <td>-0.010810</td>\n",
              "      <td>-0.019603</td>\n",
              "      <td>-0.082180</td>\n",
              "      <td>-0.086185</td>\n",
              "      <td>-0.085787</td>\n",
              "      <td>-0.040300</td>\n",
              "      <td>-0.047596</td>\n",
              "      <td>-0.058128</td>\n",
              "      <td>-0.067598</td>\n",
              "      <td>-0.076203</td>\n",
              "      <td>-0.086677</td>\n",
              "      <td>-0.080694</td>\n",
              "      <td>-0.038898</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.092964</td>\n",
              "      <td>-0.030122</td>\n",
              "      <td>-0.015070</td>\n",
              "      <td>-0.031997</td>\n",
              "      <td>-0.031903</td>\n",
              "      <td>-0.039790</td>\n",
              "      <td>-0.018576</td>\n",
              "      <td>0.008102</td>\n",
              "      <td>0.011221</td>\n",
              "      <td>-0.001723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relationship_ Husband</th>\n",
              "      <td>0.316875</td>\n",
              "      <td>-0.021140</td>\n",
              "      <td>0.580135</td>\n",
              "      <td>0.080175</td>\n",
              "      <td>0.075491</td>\n",
              "      <td>0.246164</td>\n",
              "      <td>0.008594</td>\n",
              "      <td>0.401035</td>\n",
              "      <td>-0.069133</td>\n",
              "      <td>0.014804</td>\n",
              "      <td>0.002796</td>\n",
              "      <td>-0.084935</td>\n",
              "      <td>0.112223</td>\n",
              "      <td>0.119515</td>\n",
              "      <td>-0.004451</td>\n",
              "      <td>-0.024385</td>\n",
              "      <td>-0.055373</td>\n",
              "      <td>-0.039563</td>\n",
              "      <td>0.001685</td>\n",
              "      <td>0.008752</td>\n",
              "      <td>0.031966</td>\n",
              "      <td>-0.003143</td>\n",
              "      <td>-0.018386</td>\n",
              "      <td>0.011185</td>\n",
              "      <td>0.044430</td>\n",
              "      <td>0.054595</td>\n",
              "      <td>0.003242</td>\n",
              "      <td>0.052502</td>\n",
              "      <td>-0.010542</td>\n",
              "      <td>0.071953</td>\n",
              "      <td>-0.072055</td>\n",
              "      <td>-0.328077</td>\n",
              "      <td>0.893210</td>\n",
              "      <td>-0.576730</td>\n",
              "      <td>-0.091837</td>\n",
              "      <td>-0.148795</td>\n",
              "      <td>-0.146380</td>\n",
              "      <td>-0.179782</td>\n",
              "      <td>0.160332</td>\n",
              "      <td>0.102104</td>\n",
              "      <td>0.055002</td>\n",
              "      <td>-0.036799</td>\n",
              "      <td>0.020270</td>\n",
              "      <td>-0.048212</td>\n",
              "      <td>-0.169899</td>\n",
              "      <td>0.023767</td>\n",
              "      <td>0.005374</td>\n",
              "      <td>-0.009777</td>\n",
              "      <td>0.092964</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.482936</td>\n",
              "      <td>-0.145465</td>\n",
              "      <td>-0.354353</td>\n",
              "      <td>-0.283941</td>\n",
              "      <td>-0.185640</td>\n",
              "      <td>-0.003908</td>\n",
              "      <td>-0.126338</td>\n",
              "      <td>-0.030130</td>\n",
              "      <td>0.118717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relationship_ Not-in-family</th>\n",
              "      <td>-0.010106</td>\n",
              "      <td>0.007503</td>\n",
              "      <td>-0.168879</td>\n",
              "      <td>-0.026489</td>\n",
              "      <td>-0.017299</td>\n",
              "      <td>0.007498</td>\n",
              "      <td>0.020277</td>\n",
              "      <td>-0.188497</td>\n",
              "      <td>-0.008946</td>\n",
              "      <td>0.014221</td>\n",
              "      <td>-0.003401</td>\n",
              "      <td>0.033295</td>\n",
              "      <td>-0.046441</td>\n",
              "      <td>-0.034701</td>\n",
              "      <td>0.015461</td>\n",
              "      <td>-0.011813</td>\n",
              "      <td>-0.033507</td>\n",
              "      <td>-0.008267</td>\n",
              "      <td>0.007031</td>\n",
              "      <td>-0.010459</td>\n",
              "      <td>-0.013525</td>\n",
              "      <td>-0.011362</td>\n",
              "      <td>0.023290</td>\n",
              "      <td>-0.000522</td>\n",
              "      <td>0.056859</td>\n",
              "      <td>-0.005880</td>\n",
              "      <td>-0.024325</td>\n",
              "      <td>0.024401</td>\n",
              "      <td>0.014239</td>\n",
              "      <td>-0.012782</td>\n",
              "      <td>-0.010587</td>\n",
              "      <td>0.260828</td>\n",
              "      <td>-0.537588</td>\n",
              "      <td>0.297300</td>\n",
              "      <td>0.060052</td>\n",
              "      <td>0.063982</td>\n",
              "      <td>0.120354</td>\n",
              "      <td>0.044354</td>\n",
              "      <td>-0.044495</td>\n",
              "      <td>-0.009820</td>\n",
              "      <td>-0.028885</td>\n",
              "      <td>-0.014189</td>\n",
              "      <td>-0.014557</td>\n",
              "      <td>-0.007191</td>\n",
              "      <td>0.028637</td>\n",
              "      <td>0.045482</td>\n",
              "      <td>-0.011159</td>\n",
              "      <td>0.020030</td>\n",
              "      <td>-0.030122</td>\n",
              "      <td>-0.482936</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.103131</td>\n",
              "      <td>-0.251228</td>\n",
              "      <td>-0.201307</td>\n",
              "      <td>-0.131614</td>\n",
              "      <td>-0.020447</td>\n",
              "      <td>0.003635</td>\n",
              "      <td>0.002954</td>\n",
              "      <td>0.006042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relationship_ Other-relative</th>\n",
              "      <td>-0.070002</td>\n",
              "      <td>0.025518</td>\n",
              "      <td>-0.040282</td>\n",
              "      <td>-0.019046</td>\n",
              "      <td>-0.015734</td>\n",
              "      <td>-0.048980</td>\n",
              "      <td>-0.107544</td>\n",
              "      <td>-0.083716</td>\n",
              "      <td>0.005986</td>\n",
              "      <td>-0.008415</td>\n",
              "      <td>-0.014694</td>\n",
              "      <td>0.035270</td>\n",
              "      <td>-0.025696</td>\n",
              "      <td>-0.017787</td>\n",
              "      <td>-0.015709</td>\n",
              "      <td>0.016036</td>\n",
              "      <td>0.022734</td>\n",
              "      <td>0.015613</td>\n",
              "      <td>0.027431</td>\n",
              "      <td>0.053514</td>\n",
              "      <td>0.017441</td>\n",
              "      <td>0.020921</td>\n",
              "      <td>-0.017304</td>\n",
              "      <td>-0.015718</td>\n",
              "      <td>-0.036028</td>\n",
              "      <td>-0.015160</td>\n",
              "      <td>0.028683</td>\n",
              "      <td>-0.032833</td>\n",
              "      <td>0.015735</td>\n",
              "      <td>-0.018200</td>\n",
              "      <td>-0.009768</td>\n",
              "      <td>-0.012488</td>\n",
              "      <td>-0.117950</td>\n",
              "      <td>0.110641</td>\n",
              "      <td>0.030642</td>\n",
              "      <td>0.024817</td>\n",
              "      <td>0.018894</td>\n",
              "      <td>0.010903</td>\n",
              "      <td>-0.012725</td>\n",
              "      <td>-0.040490</td>\n",
              "      <td>0.001099</td>\n",
              "      <td>0.044499</td>\n",
              "      <td>0.011730</td>\n",
              "      <td>0.011276</td>\n",
              "      <td>0.054049</td>\n",
              "      <td>-0.035988</td>\n",
              "      <td>-0.003398</td>\n",
              "      <td>-0.005354</td>\n",
              "      <td>-0.015070</td>\n",
              "      <td>-0.145465</td>\n",
              "      <td>-0.103131</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.075672</td>\n",
              "      <td>-0.060636</td>\n",
              "      <td>-0.039643</td>\n",
              "      <td>0.051823</td>\n",
              "      <td>0.042629</td>\n",
              "      <td>0.031819</td>\n",
              "      <td>-0.073347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relationship_ Own-child</th>\n",
              "      <td>-0.432918</td>\n",
              "      <td>0.013818</td>\n",
              "      <td>-0.102369</td>\n",
              "      <td>-0.053601</td>\n",
              "      <td>-0.050922</td>\n",
              "      <td>-0.249254</td>\n",
              "      <td>0.041824</td>\n",
              "      <td>-0.228532</td>\n",
              "      <td>0.092638</td>\n",
              "      <td>-0.042780</td>\n",
              "      <td>-0.038263</td>\n",
              "      <td>0.061656</td>\n",
              "      <td>-0.051211</td>\n",
              "      <td>-0.075958</td>\n",
              "      <td>-0.016901</td>\n",
              "      <td>0.040513</td>\n",
              "      <td>0.110432</td>\n",
              "      <td>0.064051</td>\n",
              "      <td>-0.020278</td>\n",
              "      <td>-0.031012</td>\n",
              "      <td>-0.031316</td>\n",
              "      <td>-0.000002</td>\n",
              "      <td>-0.016690</td>\n",
              "      <td>-0.029879</td>\n",
              "      <td>-0.070958</td>\n",
              "      <td>-0.043364</td>\n",
              "      <td>-0.023641</td>\n",
              "      <td>-0.080294</td>\n",
              "      <td>-0.004152</td>\n",
              "      <td>-0.048619</td>\n",
              "      <td>0.119939</td>\n",
              "      <td>-0.089719</td>\n",
              "      <td>-0.380070</td>\n",
              "      <td>0.509226</td>\n",
              "      <td>-0.016594</td>\n",
              "      <td>-0.029372</td>\n",
              "      <td>-0.068758</td>\n",
              "      <td>0.049039</td>\n",
              "      <td>-0.065888</td>\n",
              "      <td>-0.101447</td>\n",
              "      <td>-0.003306</td>\n",
              "      <td>0.086407</td>\n",
              "      <td>-0.017141</td>\n",
              "      <td>0.072358</td>\n",
              "      <td>0.106223</td>\n",
              "      <td>-0.083506</td>\n",
              "      <td>0.038102</td>\n",
              "      <td>-0.012443</td>\n",
              "      <td>-0.031997</td>\n",
              "      <td>-0.354353</td>\n",
              "      <td>-0.251228</td>\n",
              "      <td>-0.075672</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.147709</td>\n",
              "      <td>-0.096571</td>\n",
              "      <td>0.005439</td>\n",
              "      <td>0.019779</td>\n",
              "      <td>-0.003572</td>\n",
              "      <td>-0.017878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relationship_ Unmarried</th>\n",
              "      <td>0.043188</td>\n",
              "      <td>0.004400</td>\n",
              "      <td>-0.321273</td>\n",
              "      <td>-0.029004</td>\n",
              "      <td>-0.039136</td>\n",
              "      <td>-0.037182</td>\n",
              "      <td>-0.017690</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>-0.008357</td>\n",
              "      <td>0.014401</td>\n",
              "      <td>0.031953</td>\n",
              "      <td>0.016518</td>\n",
              "      <td>-0.036617</td>\n",
              "      <td>-0.042401</td>\n",
              "      <td>0.011038</td>\n",
              "      <td>0.008533</td>\n",
              "      <td>0.007305</td>\n",
              "      <td>-0.006820</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.009683</td>\n",
              "      <td>0.003316</td>\n",
              "      <td>0.012497</td>\n",
              "      <td>0.011819</td>\n",
              "      <td>0.018690</td>\n",
              "      <td>-0.060525</td>\n",
              "      <td>-0.019367</td>\n",
              "      <td>0.042857</td>\n",
              "      <td>-0.018440</td>\n",
              "      <td>-0.008577</td>\n",
              "      <td>-0.027991</td>\n",
              "      <td>0.004162</td>\n",
              "      <td>0.328876</td>\n",
              "      <td>-0.317487</td>\n",
              "      <td>-0.053074</td>\n",
              "      <td>0.071972</td>\n",
              "      <td>0.195842</td>\n",
              "      <td>0.161356</td>\n",
              "      <td>0.106715</td>\n",
              "      <td>-0.056221</td>\n",
              "      <td>-0.020329</td>\n",
              "      <td>-0.024488</td>\n",
              "      <td>-0.022870</td>\n",
              "      <td>0.007117</td>\n",
              "      <td>-0.012215</td>\n",
              "      <td>0.067294</td>\n",
              "      <td>-0.016226</td>\n",
              "      <td>-0.021293</td>\n",
              "      <td>0.002273</td>\n",
              "      <td>-0.031903</td>\n",
              "      <td>-0.283941</td>\n",
              "      <td>-0.201307</td>\n",
              "      <td>-0.060636</td>\n",
              "      <td>-0.147709</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.077382</td>\n",
              "      <td>-0.010770</td>\n",
              "      <td>0.148604</td>\n",
              "      <td>0.025171</td>\n",
              "      <td>-0.128129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relationship_ Wife</th>\n",
              "      <td>0.020854</td>\n",
              "      <td>-0.016897</td>\n",
              "      <td>-0.319311</td>\n",
              "      <td>0.017730</td>\n",
              "      <td>0.017138</td>\n",
              "      <td>-0.065140</td>\n",
              "      <td>-0.020525</td>\n",
              "      <td>0.123264</td>\n",
              "      <td>0.027106</td>\n",
              "      <td>-0.004435</td>\n",
              "      <td>0.031123</td>\n",
              "      <td>-0.029325</td>\n",
              "      <td>-0.002933</td>\n",
              "      <td>0.000340</td>\n",
              "      <td>0.004028</td>\n",
              "      <td>-0.013697</td>\n",
              "      <td>-0.020446</td>\n",
              "      <td>-0.003571</td>\n",
              "      <td>-0.008189</td>\n",
              "      <td>-0.002903</td>\n",
              "      <td>-0.011427</td>\n",
              "      <td>-0.004318</td>\n",
              "      <td>0.019836</td>\n",
              "      <td>0.011704</td>\n",
              "      <td>0.018236</td>\n",
              "      <td>0.000143</td>\n",
              "      <td>-0.002358</td>\n",
              "      <td>0.018600</td>\n",
              "      <td>0.001974</td>\n",
              "      <td>-0.001891</td>\n",
              "      <td>-0.014488</td>\n",
              "      <td>-0.089410</td>\n",
              "      <td>0.240279</td>\n",
              "      <td>-0.157175</td>\n",
              "      <td>-0.011463</td>\n",
              "      <td>-0.040551</td>\n",
              "      <td>-0.039893</td>\n",
              "      <td>0.076867</td>\n",
              "      <td>-0.074548</td>\n",
              "      <td>0.019179</td>\n",
              "      <td>-0.027406</td>\n",
              "      <td>-0.035707</td>\n",
              "      <td>-0.007409</td>\n",
              "      <td>0.011220</td>\n",
              "      <td>0.011571</td>\n",
              "      <td>0.046348</td>\n",
              "      <td>-0.020810</td>\n",
              "      <td>0.003717</td>\n",
              "      <td>-0.039790</td>\n",
              "      <td>-0.185640</td>\n",
              "      <td>-0.131614</td>\n",
              "      <td>-0.039643</td>\n",
              "      <td>-0.096571</td>\n",
              "      <td>-0.077382</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015480</td>\n",
              "      <td>0.001248</td>\n",
              "      <td>0.007550</td>\n",
              "      <td>-0.011587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>race_ Asian-Pac-Islander</th>\n",
              "      <td>-0.011111</td>\n",
              "      <td>-0.051323</td>\n",
              "      <td>-0.000856</td>\n",
              "      <td>0.009851</td>\n",
              "      <td>0.004469</td>\n",
              "      <td>-0.004564</td>\n",
              "      <td>-0.365437</td>\n",
              "      <td>0.010543</td>\n",
              "      <td>0.004859</td>\n",
              "      <td>0.013808</td>\n",
              "      <td>-0.019797</td>\n",
              "      <td>-0.004264</td>\n",
              "      <td>0.010203</td>\n",
              "      <td>-0.005265</td>\n",
              "      <td>0.014810</td>\n",
              "      <td>-0.017566</td>\n",
              "      <td>-0.015453</td>\n",
              "      <td>-0.007348</td>\n",
              "      <td>-0.000880</td>\n",
              "      <td>0.012807</td>\n",
              "      <td>-0.012046</td>\n",
              "      <td>-0.010376</td>\n",
              "      <td>-0.004954</td>\n",
              "      <td>-0.005286</td>\n",
              "      <td>0.055682</td>\n",
              "      <td>0.023143</td>\n",
              "      <td>-0.040777</td>\n",
              "      <td>0.025774</td>\n",
              "      <td>0.019321</td>\n",
              "      <td>0.029984</td>\n",
              "      <td>-0.010333</td>\n",
              "      <td>-0.033990</td>\n",
              "      <td>0.010562</td>\n",
              "      <td>0.011579</td>\n",
              "      <td>0.040708</td>\n",
              "      <td>-0.013717</td>\n",
              "      <td>-0.007811</td>\n",
              "      <td>0.010213</td>\n",
              "      <td>-0.022017</td>\n",
              "      <td>0.002779</td>\n",
              "      <td>-0.015965</td>\n",
              "      <td>-0.018031</td>\n",
              "      <td>-0.003552</td>\n",
              "      <td>-0.000358</td>\n",
              "      <td>0.013244</td>\n",
              "      <td>0.028269</td>\n",
              "      <td>-0.004691</td>\n",
              "      <td>0.015109</td>\n",
              "      <td>-0.018576</td>\n",
              "      <td>-0.003908</td>\n",
              "      <td>-0.020447</td>\n",
              "      <td>0.051823</td>\n",
              "      <td>0.005439</td>\n",
              "      <td>-0.010770</td>\n",
              "      <td>0.015480</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.059144</td>\n",
              "      <td>-0.024492</td>\n",
              "      <td>-0.439572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>race_ Black</th>\n",
              "      <td>-0.019434</td>\n",
              "      <td>0.118009</td>\n",
              "      <td>-0.115604</td>\n",
              "      <td>-0.020631</td>\n",
              "      <td>-0.021762</td>\n",
              "      <td>-0.053153</td>\n",
              "      <td>0.011383</td>\n",
              "      <td>-0.089089</td>\n",
              "      <td>0.016658</td>\n",
              "      <td>0.047403</td>\n",
              "      <td>0.037073</td>\n",
              "      <td>-0.000345</td>\n",
              "      <td>-0.047197</td>\n",
              "      <td>-0.058622</td>\n",
              "      <td>0.018371</td>\n",
              "      <td>0.027181</td>\n",
              "      <td>0.022514</td>\n",
              "      <td>0.025906</td>\n",
              "      <td>-0.000172</td>\n",
              "      <td>-0.011348</td>\n",
              "      <td>-0.004471</td>\n",
              "      <td>0.033201</td>\n",
              "      <td>0.002711</td>\n",
              "      <td>-0.010652</td>\n",
              "      <td>-0.051697</td>\n",
              "      <td>-0.026674</td>\n",
              "      <td>0.037145</td>\n",
              "      <td>-0.036943</td>\n",
              "      <td>0.000282</td>\n",
              "      <td>-0.031851</td>\n",
              "      <td>0.011627</td>\n",
              "      <td>0.017840</td>\n",
              "      <td>-0.125506</td>\n",
              "      <td>0.071303</td>\n",
              "      <td>0.018665</td>\n",
              "      <td>0.099531</td>\n",
              "      <td>0.019848</td>\n",
              "      <td>0.041812</td>\n",
              "      <td>-0.046924</td>\n",
              "      <td>-0.046088</td>\n",
              "      <td>-0.032349</td>\n",
              "      <td>0.024703</td>\n",
              "      <td>0.035563</td>\n",
              "      <td>0.034992</td>\n",
              "      <td>0.088125</td>\n",
              "      <td>-0.049521</td>\n",
              "      <td>-0.031795</td>\n",
              "      <td>-0.011302</td>\n",
              "      <td>0.008102</td>\n",
              "      <td>-0.126338</td>\n",
              "      <td>0.003635</td>\n",
              "      <td>0.042629</td>\n",
              "      <td>0.019779</td>\n",
              "      <td>0.148604</td>\n",
              "      <td>0.001248</td>\n",
              "      <td>-0.059144</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.043948</td>\n",
              "      <td>-0.788747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>race_ Other</th>\n",
              "      <td>-0.031039</td>\n",
              "      <td>-0.042718</td>\n",
              "      <td>-0.017478</td>\n",
              "      <td>-0.005632</td>\n",
              "      <td>-0.013594</td>\n",
              "      <td>-0.007201</td>\n",
              "      <td>-0.073147</td>\n",
              "      <td>-0.042909</td>\n",
              "      <td>0.015257</td>\n",
              "      <td>0.012115</td>\n",
              "      <td>0.008118</td>\n",
              "      <td>-0.001348</td>\n",
              "      <td>-0.016823</td>\n",
              "      <td>-0.010731</td>\n",
              "      <td>-0.004977</td>\n",
              "      <td>0.011565</td>\n",
              "      <td>0.003726</td>\n",
              "      <td>0.022786</td>\n",
              "      <td>0.032344</td>\n",
              "      <td>0.020846</td>\n",
              "      <td>0.024025</td>\n",
              "      <td>0.007090</td>\n",
              "      <td>-0.003999</td>\n",
              "      <td>0.000343</td>\n",
              "      <td>-0.026085</td>\n",
              "      <td>-0.004934</td>\n",
              "      <td>0.004614</td>\n",
              "      <td>-0.019463</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>-0.007553</td>\n",
              "      <td>-0.000178</td>\n",
              "      <td>0.004447</td>\n",
              "      <td>-0.021712</td>\n",
              "      <td>0.008418</td>\n",
              "      <td>0.032322</td>\n",
              "      <td>0.007539</td>\n",
              "      <td>0.001686</td>\n",
              "      <td>-0.007524</td>\n",
              "      <td>-0.000885</td>\n",
              "      <td>-0.022211</td>\n",
              "      <td>0.004356</td>\n",
              "      <td>0.010983</td>\n",
              "      <td>0.021437</td>\n",
              "      <td>0.014949</td>\n",
              "      <td>0.010841</td>\n",
              "      <td>-0.006957</td>\n",
              "      <td>-0.010463</td>\n",
              "      <td>-0.013355</td>\n",
              "      <td>0.011221</td>\n",
              "      <td>-0.030130</td>\n",
              "      <td>0.002954</td>\n",
              "      <td>0.031819</td>\n",
              "      <td>-0.003572</td>\n",
              "      <td>0.025171</td>\n",
              "      <td>0.007550</td>\n",
              "      <td>-0.024492</td>\n",
              "      <td>-0.043948</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.326632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>race_ White</th>\n",
              "      <td>0.033412</td>\n",
              "      <td>-0.056896</td>\n",
              "      <td>0.103486</td>\n",
              "      <td>0.014429</td>\n",
              "      <td>0.021044</td>\n",
              "      <td>0.049345</td>\n",
              "      <td>0.200004</td>\n",
              "      <td>0.085224</td>\n",
              "      <td>-0.022055</td>\n",
              "      <td>-0.050995</td>\n",
              "      <td>-0.024132</td>\n",
              "      <td>0.002919</td>\n",
              "      <td>0.040631</td>\n",
              "      <td>0.055585</td>\n",
              "      <td>-0.020843</td>\n",
              "      <td>-0.018280</td>\n",
              "      <td>-0.012494</td>\n",
              "      <td>-0.026520</td>\n",
              "      <td>-0.011564</td>\n",
              "      <td>-0.004735</td>\n",
              "      <td>0.000711</td>\n",
              "      <td>-0.025207</td>\n",
              "      <td>0.001706</td>\n",
              "      <td>0.011396</td>\n",
              "      <td>0.025211</td>\n",
              "      <td>0.012589</td>\n",
              "      <td>-0.012425</td>\n",
              "      <td>0.025307</td>\n",
              "      <td>-0.012256</td>\n",
              "      <td>0.014486</td>\n",
              "      <td>-0.004491</td>\n",
              "      <td>0.000371</td>\n",
              "      <td>0.107653</td>\n",
              "      <td>-0.068446</td>\n",
              "      <td>-0.047996</td>\n",
              "      <td>-0.079078</td>\n",
              "      <td>-0.013310</td>\n",
              "      <td>-0.037164</td>\n",
              "      <td>0.050468</td>\n",
              "      <td>0.045427</td>\n",
              "      <td>0.033319</td>\n",
              "      <td>-0.015762</td>\n",
              "      <td>-0.035965</td>\n",
              "      <td>-0.034644</td>\n",
              "      <td>-0.084228</td>\n",
              "      <td>0.029867</td>\n",
              "      <td>0.032805</td>\n",
              "      <td>0.006923</td>\n",
              "      <td>-0.001723</td>\n",
              "      <td>0.118717</td>\n",
              "      <td>0.006042</td>\n",
              "      <td>-0.073347</td>\n",
              "      <td>-0.017878</td>\n",
              "      <td>-0.128129</td>\n",
              "      <td>-0.011587</td>\n",
              "      <td>-0.439572</td>\n",
              "      <td>-0.788747</td>\n",
              "      <td>-0.326632</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         age  ...  race_ White\n",
              "age                                 1.000000  ...     0.033412\n",
              "fnlwgt                             -0.076646  ...    -0.056896\n",
              "sex                                 0.088832  ...     0.103486\n",
              "capital_gain                        0.077674  ...     0.014429\n",
              "capital_loss                        0.057775  ...     0.021044\n",
              "hours_per_week                      0.068756  ...     0.049345\n",
              "native_country                      0.015917  ...     0.200004\n",
              "classification                      0.234037  ...     0.085224\n",
              "workclass_ ?                        0.042627  ...    -0.022055\n",
              "workclass_ Federal-gov              0.051227  ...    -0.050995\n",
              "workclass_ Local-gov                0.060901  ...    -0.024132\n",
              "workclass_ Private                 -0.198388  ...     0.002919\n",
              "workclass_ Self-emp-inc             0.101808  ...     0.040631\n",
              "workclass_ Self-emp-not-inc         0.136252  ...     0.055585\n",
              "workclass_ State-gov                0.012763  ...    -0.020843\n",
              "education_ 10th                    -0.014504  ...    -0.018280\n",
              "education_ 11th                    -0.088314  ...    -0.012494\n",
              "education_ 12th                    -0.056016  ...    -0.026520\n",
              "education_ 1st-4th                  0.039921  ...    -0.011564\n",
              "education_ 5th-6th                  0.032076  ...    -0.004735\n",
              "education_ 7th-8th                  0.102886  ...     0.000711\n",
              "education_ 9th                      0.023014  ...    -0.025207\n",
              "education_ Assoc-acdm              -0.016196  ...     0.001706\n",
              "education_ Assoc-voc               -0.000434  ...     0.011396\n",
              "education_ Bachelors                0.010516  ...     0.025211\n",
              "education_ Doctorate                0.075787  ...     0.012589\n",
              "education_ HS-grad                  0.019870  ...    -0.012425\n",
              "education_ Masters                  0.094761  ...     0.025307\n",
              "education_ Preschool                0.012146  ...    -0.012256\n",
              "education_ Prof-school              0.060651  ...     0.014486\n",
              "education_ Some-college            -0.111262  ...    -0.004491\n",
              "marital_status_ Divorced            0.129975  ...     0.000371\n",
              "marital_status_ Married-civ-spouse  0.315679  ...     0.107653\n",
              "marital_status_ Never-married      -0.534359  ...    -0.068446\n",
              "marital_status_ Other               0.013528  ...    -0.047996\n",
              "marital_status_ Separated           0.010210  ...    -0.079078\n",
              "marital_status_ Widowed             0.265177  ...    -0.013310\n",
              "occupation_ Adm-clerical           -0.042902  ...    -0.037164\n",
              "occupation_ Craft-repair            0.012515  ...     0.050468\n",
              "occupation_ Exec-managerial         0.099352  ...     0.045427\n",
              "occupation_ Farming-fishing         0.034210  ...     0.033319\n",
              "occupation_ Handlers-cleaners      -0.098579  ...    -0.015762\n",
              "occupation_ Machine-op-inspct      -0.016257  ...    -0.035965\n",
              "occupation_ Other                   0.040149  ...    -0.034644\n",
              "occupation_ Other-service          -0.089346  ...    -0.084228\n",
              "occupation_ Prof-specialty          0.054170  ...     0.029867\n",
              "occupation_ Sales                  -0.031980  ...     0.032805\n",
              "occupation_ Tech-support           -0.019576  ...     0.006923\n",
              "occupation_ Transport-moving        0.026909  ...    -0.001723\n",
              "relationship_ Husband               0.316875  ...     0.118717\n",
              "relationship_ Not-in-family        -0.010106  ...     0.006042\n",
              "relationship_ Other-relative       -0.070002  ...    -0.073347\n",
              "relationship_ Own-child            -0.432918  ...    -0.017878\n",
              "relationship_ Unmarried             0.043188  ...    -0.128129\n",
              "relationship_ Wife                  0.020854  ...    -0.011587\n",
              "race_ Asian-Pac-Islander           -0.011111  ...    -0.439572\n",
              "race_ Black                        -0.019434  ...    -0.788747\n",
              "race_ Other                        -0.031039  ...    -0.326632\n",
              "race_ White                         0.033412  ...     1.000000\n",
              "\n",
              "[59 rows x 59 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 520
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 994
        },
        "id": "8_zgzjRl8Ulc",
        "outputId": "05237baa-64cb-4be0-b4c9-a160fb7b5421"
      },
      "source": [
        "# Matrice de corrélation \n",
        "f, ax = plt.subplots(figsize=(24, 14))\n",
        "corr = dataset_with_dummies.corr()\n",
        "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),square=True, ax=ax)\n",
        "#plt.savefig('matrice_correlation2')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe937e75128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 521
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1728x1008 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYi0cLs-7myX"
      },
      "source": [
        "# Create correlation matrix\n",
        "corr_matrix = dataset_with_dummies.corr().abs()\n",
        "\n",
        "# Select upper triangle of correlation matrix\n",
        "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
        "\n",
        "# Find index of feature columns with correlation greater than 0.95\n",
        "to_drop = [column for column in upper.columns if any(upper[column] > 0.5)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1qWjDKtN70ny",
        "outputId": "a1455992-86db-40f2-edcc-600ebd21975a"
      },
      "source": [
        "to_drop"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['marital_status_ Never-married',\n",
              " 'occupation_ Other',\n",
              " 'relationship_ Husband',\n",
              " 'relationship_ Not-in-family',\n",
              " 'relationship_ Own-child',\n",
              " 'race_ White']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 523
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6AFcm6v-rkQ"
      },
      "source": [
        "my_y=dataset_with_dummies['classification']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Kz_DDyb8-kx6",
        "outputId": "170c8666-29b5-4bbc-96c5-c8ed47de1bb8"
      },
      "source": [
        "dataset_after_dummies = dataset_with_dummies.drop(dataset_with_dummies[to_drop], axis=1 )\n",
        "dataset_after_dummies"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>native_country</th>\n",
              "      <th>classification</th>\n",
              "      <th>workclass_ ?</th>\n",
              "      <th>workclass_ Federal-gov</th>\n",
              "      <th>workclass_ Local-gov</th>\n",
              "      <th>workclass_ Private</th>\n",
              "      <th>workclass_ Self-emp-inc</th>\n",
              "      <th>workclass_ Self-emp-not-inc</th>\n",
              "      <th>workclass_ State-gov</th>\n",
              "      <th>education_ 10th</th>\n",
              "      <th>education_ 11th</th>\n",
              "      <th>education_ 12th</th>\n",
              "      <th>education_ 1st-4th</th>\n",
              "      <th>education_ 5th-6th</th>\n",
              "      <th>education_ 7th-8th</th>\n",
              "      <th>education_ 9th</th>\n",
              "      <th>education_ Assoc-acdm</th>\n",
              "      <th>education_ Assoc-voc</th>\n",
              "      <th>education_ Bachelors</th>\n",
              "      <th>education_ Doctorate</th>\n",
              "      <th>education_ HS-grad</th>\n",
              "      <th>education_ Masters</th>\n",
              "      <th>education_ Preschool</th>\n",
              "      <th>education_ Prof-school</th>\n",
              "      <th>education_ Some-college</th>\n",
              "      <th>marital_status_ Divorced</th>\n",
              "      <th>marital_status_ Married-civ-spouse</th>\n",
              "      <th>marital_status_ Other</th>\n",
              "      <th>marital_status_ Separated</th>\n",
              "      <th>marital_status_ Widowed</th>\n",
              "      <th>occupation_ Adm-clerical</th>\n",
              "      <th>occupation_ Craft-repair</th>\n",
              "      <th>occupation_ Exec-managerial</th>\n",
              "      <th>occupation_ Farming-fishing</th>\n",
              "      <th>occupation_ Handlers-cleaners</th>\n",
              "      <th>occupation_ Machine-op-inspct</th>\n",
              "      <th>occupation_ Other-service</th>\n",
              "      <th>occupation_ Prof-specialty</th>\n",
              "      <th>occupation_ Sales</th>\n",
              "      <th>occupation_ Tech-support</th>\n",
              "      <th>occupation_ Transport-moving</th>\n",
              "      <th>relationship_ Other-relative</th>\n",
              "      <th>relationship_ Unmarried</th>\n",
              "      <th>relationship_ Wife</th>\n",
              "      <th>race_ Asian-Pac-Islander</th>\n",
              "      <th>race_ Black</th>\n",
              "      <th>race_ Other</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32558</th>\n",
              "      <td>58</td>\n",
              "      <td>151910</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32559</th>\n",
              "      <td>22</td>\n",
              "      <td>201490</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</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>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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32560</th>\n",
              "      <td>52</td>\n",
              "      <td>287927</td>\n",
              "      <td>0</td>\n",
              "      <td>15024</td>\n",
              "      <td>0</td>\n",
              "      <td>40</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>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>1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32561 rows × 53 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  fnlwgt  sex  ...  race_ Asian-Pac-Islander  race_ Black  race_ Other\n",
              "0       39   77516    1  ...                         0            0            0\n",
              "1       50   83311    1  ...                         0            0            0\n",
              "2       38  215646    1  ...                         0            0            0\n",
              "3       53  234721    1  ...                         0            1            0\n",
              "4       28  338409    0  ...                         0            1            0\n",
              "...    ...     ...  ...  ...                       ...          ...          ...\n",
              "32556   27  257302    0  ...                         0            0            0\n",
              "32557   40  154374    1  ...                         0            0            0\n",
              "32558   58  151910    0  ...                         0            0            0\n",
              "32559   22  201490    1  ...                         0            0            0\n",
              "32560   52  287927    0  ...                         0            0            0\n",
              "\n",
              "[32561 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 525
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1aXDIjQhp06"
      },
      "source": [
        "##**Récupération des variables et de la variable à  prédire**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GX3rsn9sCKqk"
      },
      "source": [
        "y=my_y\n",
        "t = dataset_after_dummies.drop(['classification'],axis=1)\n",
        "x = dataset_after_dummies.drop(['classification'],axis=1)\n",
        "#Normalisation des données\n",
        "norm = StandardScaler()\n",
        "norm.fit(x)\n",
        "x = norm.transform(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "0fycFJ8zZZNQ",
        "outputId": "44906d31-13e3-44ff-ce05-399f75f36ae3"
      },
      "source": [
        "t"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>native_country</th>\n",
              "      <th>workclass_ ?</th>\n",
              "      <th>workclass_ Federal-gov</th>\n",
              "      <th>workclass_ Local-gov</th>\n",
              "      <th>workclass_ Private</th>\n",
              "      <th>workclass_ Self-emp-inc</th>\n",
              "      <th>workclass_ Self-emp-not-inc</th>\n",
              "      <th>workclass_ State-gov</th>\n",
              "      <th>education_ 10th</th>\n",
              "      <th>education_ 11th</th>\n",
              "      <th>education_ 12th</th>\n",
              "      <th>education_ 1st-4th</th>\n",
              "      <th>education_ 5th-6th</th>\n",
              "      <th>education_ 7th-8th</th>\n",
              "      <th>education_ 9th</th>\n",
              "      <th>education_ Assoc-acdm</th>\n",
              "      <th>education_ Assoc-voc</th>\n",
              "      <th>education_ Bachelors</th>\n",
              "      <th>education_ Doctorate</th>\n",
              "      <th>education_ HS-grad</th>\n",
              "      <th>education_ Masters</th>\n",
              "      <th>education_ Preschool</th>\n",
              "      <th>education_ Prof-school</th>\n",
              "      <th>education_ Some-college</th>\n",
              "      <th>marital_status_ Divorced</th>\n",
              "      <th>marital_status_ Married-civ-spouse</th>\n",
              "      <th>marital_status_ Other</th>\n",
              "      <th>marital_status_ Separated</th>\n",
              "      <th>marital_status_ Widowed</th>\n",
              "      <th>occupation_ Adm-clerical</th>\n",
              "      <th>occupation_ Craft-repair</th>\n",
              "      <th>occupation_ Exec-managerial</th>\n",
              "      <th>occupation_ Farming-fishing</th>\n",
              "      <th>occupation_ Handlers-cleaners</th>\n",
              "      <th>occupation_ Machine-op-inspct</th>\n",
              "      <th>occupation_ Other-service</th>\n",
              "      <th>occupation_ Prof-specialty</th>\n",
              "      <th>occupation_ Sales</th>\n",
              "      <th>occupation_ Tech-support</th>\n",
              "      <th>occupation_ Transport-moving</th>\n",
              "      <th>relationship_ Other-relative</th>\n",
              "      <th>relationship_ Unmarried</th>\n",
              "      <th>relationship_ Wife</th>\n",
              "      <th>race_ Asian-Pac-Islander</th>\n",
              "      <th>race_ Black</th>\n",
              "      <th>race_ Other</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>338409</td>\n",
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              "      <td>1</td>\n",
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              "      <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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32556</th>\n",
              "      <td>27</td>\n",
              "      <td>257302</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</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>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>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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32557</th>\n",
              "      <td>40</td>\n",
              "      <td>154374</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</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>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>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>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>32558</th>\n",
              "      <td>58</td>\n",
              "      <td>151910</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32559</th>\n",
              "      <td>22</td>\n",
              "      <td>201490</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</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>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",
              "      <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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32560</th>\n",
              "      <td>52</td>\n",
              "      <td>287927</td>\n",
              "      <td>0</td>\n",
              "      <td>15024</td>\n",
              "      <td>0</td>\n",
              "      <td>40</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>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>1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32561 rows × 52 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  fnlwgt  sex  ...  race_ Asian-Pac-Islander  race_ Black  race_ Other\n",
              "0       39   77516    1  ...                         0            0            0\n",
              "1       50   83311    1  ...                         0            0            0\n",
              "2       38  215646    1  ...                         0            0            0\n",
              "3       53  234721    1  ...                         0            1            0\n",
              "4       28  338409    0  ...                         0            1            0\n",
              "...    ...     ...  ...  ...                       ...          ...          ...\n",
              "32556   27  257302    0  ...                         0            0            0\n",
              "32557   40  154374    1  ...                         0            0            0\n",
              "32558   58  151910    0  ...                         0            0            0\n",
              "32559   22  201490    1  ...                         0            0            0\n",
              "32560   52  287927    0  ...                         0            0            0\n",
              "\n",
              "[32561 rows x 52 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 607
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K7g68_NIhXo2"
      },
      "source": [
        "##**Creation de la base d'entrainement et de Test**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lI1OiulLEclG"
      },
      "source": [
        "split_size=0.2\n",
        "#Creation of Train and Test dataset\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=split_size,random_state=22)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPaFPqDGZJbF",
        "outputId": "d1f77718-7d27-41c0-9545-3a1b6b1df587"
      },
      "source": [
        "x_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.14233079,  0.20605374,  0.70307135, ..., -0.18155194,\n",
              "        -0.32576824, -0.13490531],\n",
              "       [-1.21564337,  0.24270045,  0.70307135, ..., -0.18155194,\n",
              "        -0.32576824, -0.13490531],\n",
              "       [-1.43558112,  2.04929418, -1.42233076, ..., -0.18155194,\n",
              "        -0.32576824, -0.13490531],\n",
              "       ...,\n",
              "       [-0.77576787, -1.05565231,  0.70307135, ..., -0.18155194,\n",
              "         3.06966697, -0.13490531],\n",
              "       [-1.43558112,  0.29408922, -1.42233076, ..., -0.18155194,\n",
              "        -0.32576824, -0.13490531],\n",
              "       [ 0.25060831, -0.06227825, -1.42233076, ..., -0.18155194,\n",
              "        -0.32576824, -0.13490531]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 600
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9eN4sG0oEyrA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93032c76-c688-476e-a5c8-996973b6fba2"
      },
      "source": [
        "print (\"Train dataset: {0}{1}\".format(x_train.shape, y_train.shape))\n",
        "print (\"Test dataset: {0}{1}\".format(x_test.shape, y_test.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train dataset: (26048, 52)(26048,)\n",
            "Test dataset: (6513, 52)(6513,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hYFcZ6eIiBgf"
      },
      "source": [
        "##**Création des models**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wm1kVRrmFVa4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6231ed33-e362-4c56-8be9-1709e46c17ce"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "names = ['DecisionTree classifier','Naive bayes','Logistic regression']\n",
        "models = []\n",
        "models.append((DecisionTreeClassifier()))\n",
        "models.append((GaussianNB()))\n",
        "models.append((LogisticRegression(random_state=0)))\n",
        "print (models)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
            "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                       random_state=None, splitter='best'), GaussianNB(priors=None, var_smoothing=1e-09), LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
            "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
            "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
            "                   warm_start=False)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzmawMkQGk71",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfa2cde7-c37c-4c9d-9a18-3cf04a547c61"
      },
      "source": [
        "from sklearn import model_selection\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "kfold = model_selection.KFold(n_splits=5,random_state=100)\n",
        "for i in range(0,len(models)):\n",
        "    score=models[i].fit(x_train,y_train)\n",
        "    prediction = models[i].predict(x_test)\n",
        "    acc_score = accuracy_score(y_test,prediction)     \n",
        "    print ('-'*40)\n",
        "    print ('{0}: {1}'.format(names[i],acc_score))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "DecisionTree classifier: 0.8132964839551666\n",
            "----------------------------------------\n",
            "Naive bayes: 0.592814371257485\n",
            "----------------------------------------\n",
            "Logistic regression: 0.8466144633809305\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5RmMt-P-MWjS"
      },
      "source": [
        "##**Validation croisée**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scipRK7krNJE"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QmuHXqV_rNMk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22769279-108a-4b04-a7e7-75d4c30d703c"
      },
      "source": [
        "# Decision Tree\n",
        "tree_clf = DecisionTreeClassifier()\n",
        "tree_scores = cross_val_score(tree_clf, x_train, y_train, cv=5)\n",
        "print(\"Cross-validation scores:\\n{}\".format(tree_scores))\n",
        "print(\"Cross-validation scores mean:\\n{}\".format(tree_scores.mean()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross-validation scores:\n",
            "[0.81746641 0.81228407 0.81765835 0.81301593 0.81973507]\n",
            "Cross-validation scores mean:\n",
            "0.8160319674091314\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RyxFm4RVrNPh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a250031-3c91-438d-9c94-cbaa9adac495"
      },
      "source": [
        "#Naive_bayes\n",
        "bayes_clf = GaussianNB()\n",
        "bayes_scores = cross_val_score(bayes_clf, x_train, y_train, cv=5)\n",
        "print(\"Cross-validation scores:\\n{}\".format(bayes_scores))\n",
        "print(\"Cross-validation scores mean:\\n{}\".format(bayes_scores.mean()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross-validation scores:\n",
            "[0.58771593 0.58598848 0.58771593 0.60568247 0.58533308]\n",
            "Cross-validation scores mean:\n",
            "0.5904871790998083\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "433kXn_wrNTs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24ba5258-5c02-4cdb-f671-dfa52608f79b"
      },
      "source": [
        "#Logistic regression\n",
        "rgl_clf = LogisticRegression(random_state=0)\n",
        "rgl_scores = cross_val_score(rgl_clf, x_train, y_train, cv=5)\n",
        "print(\"Cross-validation scores:\\n{}\".format(rgl_scores))\n",
        "print(\"Cross-validation scores mean:\\n{}\".format(rgl_scores.mean()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cross-validation scores:\n",
            "[0.85412668 0.85489443 0.84952015 0.84718756 0.85448263]\n",
            "Cross-validation scores mean:\n",
            "0.852042290602158\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7a5Hhh9Mfi1"
      },
      "source": [
        "##**Matrice de confusion**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6E2KkGjPXdw"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-qmaYEL7hQG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6b4b3d1-a00c-4b5a-bbdc-4ec556399c03"
      },
      "source": [
        "#DecisionTreeClassifier\n",
        "clf_dtc = DecisionTreeClassifier(random_state = 100)\n",
        "clf_dtc.fit(x_train, y_train)\n",
        "y_pred = clf_dtc.predict(x_test)\n",
        "print ('-'*40)\n",
        "print ('Accuracy score:')\n",
        "print (accuracy_score(y_test,y_pred))\n",
        "print ('-'*40)\n",
        "print ('Confusion Matrix:')\n",
        "conf1 = confusion_matrix(y_test,y_pred)\n",
        "print (conf1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Accuracy score:\n",
            "0.8142177184093352\n",
            "----------------------------------------\n",
            "Confusion Matrix:\n",
            "[[4297  578]\n",
            " [ 632 1006]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "DA_KR2czOfJg",
        "outputId": "e4975727-a26f-430e-f6fb-8508175f57a2"
      },
      "source": [
        "sns.heatmap(conf1, linewidths=1, annot=True, fmt='g')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe937cf7160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 593
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVSsOhQsBol3",
        "outputId": "bd2c1336-0f3c-4a94-e38c-b3d492d98368"
      },
      "source": [
        "#Naive_bayes\n",
        "clf_nb = GaussianNB()\n",
        "clf_nb.fit(x_train, y_train)\n",
        "y_pred = clf_nb.predict(x_test)\n",
        "print ('-'*40)\n",
        "print ('Accuracy score:')\n",
        "print (accuracy_score(y_test,y_pred))\n",
        "print ('-'*40)\n",
        "print ('Confusion Matrix:')\n",
        "conf2 = confusion_matrix(y_test,y_pred)\n",
        "print (conf2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Accuracy score:\n",
            "0.592814371257485\n",
            "----------------------------------------\n",
            "Confusion Matrix:\n",
            "[[2364 2511]\n",
            " [ 141 1497]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "KUzNWe-COdji",
        "outputId": "501659dd-5112-46d1-fcce-891f882d3875"
      },
      "source": [
        "sns.heatmap(conf2, linewidths=1, annot=True, fmt='g')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe93a37e6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 595
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cvGYRU2PAte_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33781ac2-df70-48c1-bacd-4e5b3d1d6b7d"
      },
      "source": [
        "#Logistic regression\n",
        "clf_rgl = LogisticRegression(random_state=100)\n",
        "clf_rgl.fit(x_train, y_train)\n",
        "y_pred = clf_rgl.predict(x_test)\n",
        "print ('-'*40)\n",
        "print ('Accuracy score:')\n",
        "print (accuracy_score(y_test,y_pred))\n",
        "print ('-'*40)\n",
        "print ('Confusion Matrix:')\n",
        "conf3 = confusion_matrix(y_test,y_pred)\n",
        "print (conf3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------\n",
            "Accuracy score:\n",
            "0.8466144633809305\n",
            "----------------------------------------\n",
            "Confusion Matrix:\n",
            "[[4535  340]\n",
            " [ 659  979]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "vqJQ7bTIMo6u",
        "outputId": "4b8ff7b6-7fea-465c-fbee-0ed8fb09ac51"
      },
      "source": [
        "sns.heatmap(conf3, linewidths=1, annot=True, fmt='g')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe93885b438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 597
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZPvG1c2WKw7",
        "outputId": "cb28e06c-fb43-4925-af7b-a7dec7144827"
      },
      "source": [
        "import pandas\n",
        "fi = pd.DataFrame({'feature': t.columns, 'importance': clf_dtc.feature_importances_}).sort_values(by='importance', ascending=False)\n",
        "fi = fi.reset_index()\n",
        "pandas.set_option('display.max_rows',fi.shape[0]+1)\n",
        "print(fi)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    index                             feature  importance\n",
            "0      31  marital_status_ Married-civ-spouse    0.195921\n",
            "1       1                              fnlwgt    0.183535\n",
            "2       0                                 age    0.123971\n",
            "3       3                        capital_gain    0.120256\n",
            "4       5                      hours_per_week    0.067798\n",
            "5       4                        capital_loss    0.051835\n",
            "6      23                education_ Bachelors    0.023142\n",
            "7      42          occupation_ Prof-specialty    0.022167\n",
            "8      37         occupation_ Exec-managerial    0.020701\n",
            "9      12         workclass_ Self-emp-not-inc    0.011370\n",
            "10     26                  education_ Masters    0.010889\n",
            "11     10                  workclass_ Private    0.009501\n",
            "12     43                   occupation_ Sales    0.008984\n",
            "13      6                      native_country    0.008536\n",
            "14      2                                 sex    0.008378\n",
            "15     36            occupation_ Craft-repair    0.007779\n",
            "16      9                workclass_ Local-gov    0.007435\n",
            "17     35            occupation_ Adm-clerical    0.006924\n",
            "18     29             education_ Some-college    0.006842\n",
            "19     11             workclass_ Self-emp-inc    0.006350\n",
            "20     50                         race_ Black    0.006284\n",
            "21     48                  relationship_ Wife    0.006247\n",
            "22     40       occupation_ Machine-op-inspct    0.005440\n",
            "23      8              workclass_ Federal-gov    0.005318\n",
            "24     45        occupation_ Transport-moving    0.005296\n",
            "25     25                  education_ HS-grad    0.005130\n",
            "26     44            occupation_ Tech-support    0.005033\n",
            "27     13                workclass_ State-gov    0.004961\n",
            "28     24                education_ Doctorate    0.004558\n",
            "29     39       occupation_ Handlers-cleaners    0.004381\n",
            "30     41           occupation_ Other-service    0.004060\n",
            "31     49            race_ Asian-Pac-Islander    0.004024\n",
            "32     30            marital_status_ Divorced    0.003956\n",
            "33     21               education_ Assoc-acdm    0.003927\n",
            "34     22                education_ Assoc-voc    0.003875\n",
            "35     28              education_ Prof-school    0.003612\n",
            "36     47             relationship_ Unmarried    0.003555\n",
            "37     38         occupation_ Farming-fishing    0.002680\n",
            "38      7                        workclass_ ?    0.002515\n",
            "39     51                         race_ Other    0.002046\n",
            "40     15                     education_ 11th    0.001467\n",
            "41     33           marital_status_ Separated    0.001449\n",
            "42     46        relationship_ Other-relative    0.001432\n",
            "43     14                     education_ 10th    0.001209\n",
            "44     19                  education_ 7th-8th    0.001114\n",
            "45     32               marital_status_ Other    0.001017\n",
            "46     34             marital_status_ Widowed    0.000984\n",
            "47     20                      education_ 9th    0.000971\n",
            "48     16                     education_ 12th    0.000589\n",
            "49     18                  education_ 5th-6th    0.000327\n",
            "50     27                education_ Preschool    0.000207\n",
            "51     17                  education_ 1st-4th    0.000022\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTYmLiXJWXvs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}