Adult_Income_EDA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Homework-1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h2hwTLpzpqAe"
      },
      "outputs": [],
      "source": [
        "#Importing Required Libraries And Modules\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Numpy Stands for numerical python and used for working with arrays.\n",
        "2. Pandas is used for analyze and manipulate the data.\n",
        "3. Matplotlib is also a python library which is used for Data Visualization.\n",
        "4. Seaborn provides a high-level interface for drawing attractive and informative statistical graphics and it is closely integrated with pandas data structures in python. "
      ],
      "metadata": {
        "id": "CtvSHDOAFeaS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading Data\n",
        "df = pd.read_csv(\"/content/adult.csv\")\n",
        "#df.columns = ['Age','workclass','fnlwgt','education','education-num','marital-status','occupation','relationship','race','sex','capital-gain','capital-loss','hrs-per-week','native-country','income']"
      ],
      "metadata": {
        "id": "gqWHXWQgp3jE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "txsOwqZzp917",
        "outputId": "4b4c0797-d461-4b66-b2f7-b7d8bb17f8a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-49fab160-0d53-4fec-8a22-1a2d6eac418c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "      <td>?</td>\n",
              "      <td>77053</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>?</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</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>82</td>\n",
              "      <td>Private</td>\n",
              "      <td>132870</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</td>\n",
              "      <td>18</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>66</td>\n",
              "      <td>?</td>\n",
              "      <td>186061</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>?</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</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>54</td>\n",
              "      <td>Private</td>\n",
              "      <td>140359</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>4</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3900</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>41</td>\n",
              "      <td>Private</td>\n",
              "      <td>264663</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Separated</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3900</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49fab160-0d53-4fec-8a22-1a2d6eac418c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-49fab160-0d53-4fec-8a22-1a2d6eac418c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-49fab160-0d53-4fec-8a22-1a2d6eac418c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age workclass  fnlwgt  ... hours.per.week  native.country income\n",
              "0   90         ?   77053  ...             40   United-States  <=50K\n",
              "1   82   Private  132870  ...             18   United-States  <=50K\n",
              "2   66         ?  186061  ...             40   United-States  <=50K\n",
              "3   54   Private  140359  ...             40   United-States  <=50K\n",
              "4   41   Private  264663  ...             40   United-States  <=50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Head() function defined inside pandas and used to show top 5 rows from dataset \n",
        "by default. \n",
        "we can pass parameter also for required number of rows."
      ],
      "metadata": {
        "id": "KbN49amMIxhm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01Vd7PUnqQtf",
        "outputId": "9a0848ab-512d-4727-9c33-eaa3275f436a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32561, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Shape also defined inside pandas and used to check the dimention of dataset.\n",
        "Here our data set is of 32561x15 which means, it contains 32561 rows and 15 columns."
      ],
      "metadata": {
        "id": "YwA0FvxWI6oF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe() #It shows mean, standard deviation, min value, max value, total count of values of numerical variables"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Aaxh4qOm0rEi",
        "outputId": "62cc1bd5-6bc4-46be-ab7a-6d57cb867cd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c5c3ea50-c14a-40db-b395-9b9ec8e08700\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5c3ea50-c14a-40db-b395-9b9ec8e08700')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c5c3ea50-c14a-40db-b395-9b9ec8e08700 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c5c3ea50-c14a-40db-b395-9b9ec8e08700');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "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": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Describe() is a function inside pandas.\n",
        "It performs mathematical operations on dataset like:\n",
        " (i)   Count total values availabe in perticular column.\n",
        " (ii)  Calculating Mean(Average).\n",
        " (iii) Calculating Standard Deviation etc.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "f69-qAdkJW2Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KygXFZNP1Bkh",
        "outputId": "99cfc5d5-5e67-4e37-f860-dec07b85e939"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32561 entries, 0 to 32560\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   age             32561 non-null  int64 \n",
            " 1   workclass       32561 non-null  object\n",
            " 2   fnlwgt          32561 non-null  int64 \n",
            " 3   education       32561 non-null  object\n",
            " 4   education.num   32561 non-null  int64 \n",
            " 5   marital.status  32561 non-null  object\n",
            " 6   occupation      32561 non-null  object\n",
            " 7   relationship    32561 non-null  object\n",
            " 8   race            32561 non-null  object\n",
            " 9   sex             32561 non-null  object\n",
            " 10  capital.gain    32561 non-null  int64 \n",
            " 11  capital.loss    32561 non-null  int64 \n",
            " 12  hours.per.week  32561 non-null  int64 \n",
            " 13  native.country  32561 non-null  object\n",
            " 14  income          32561 non-null  object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 3.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "info() shows number of columns available, value counts, data type of each column."
      ],
      "metadata": {
        "id": "s-YlcBgUKlTb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum() #Checking if null value available"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KxjwgvrQeMh1",
        "outputId": "3917122a-cf48-44e4-d3fb-9c4262b85b4c"
      },
      "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",
              "income            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "isnull() checks whether any column contains null value or not. \n",
        "isnull().sum() shows total count of null values(if available) in each column."
      ],
      "metadata": {
        "id": "0_ZGbWjwK892"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique() #Number of unique values  in each column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qyoie-CQea5m",
        "outputId": "3689a124-6bd6-4c62-bbc6-312499f3977b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                  73\n",
              "workclass             9\n",
              "fnlwgt            21648\n",
              "education            16\n",
              "education.num        16\n",
              "marital.status        7\n",
              "occupation           15\n",
              "relationship          6\n",
              "race                  5\n",
              "sex                   2\n",
              "capital.gain        119\n",
              "capital.loss         92\n",
              "hours.per.week       94\n",
              "native.country       42\n",
              "income                2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "nunique() shows total no. of unique values in each column."
      ],
      "metadata": {
        "id": "DgP362MiLUfD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T #Transpose of data description table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "qSnZcTz1em-c",
        "outputId": "21d16c03-0972-4f09-b925-f566f2a2f8e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1346b11d-9087-4eac-b93f-2aa720f1de7d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>32561.0</td>\n",
              "      <td>38.581647</td>\n",
              "      <td>13.640433</td>\n",
              "      <td>17.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fnlwgt</th>\n",
              "      <td>32561.0</td>\n",
              "      <td>189778.366512</td>\n",
              "      <td>105549.977697</td>\n",
              "      <td>12285.0</td>\n",
              "      <td>117827.0</td>\n",
              "      <td>178356.0</td>\n",
              "      <td>237051.0</td>\n",
              "      <td>1484705.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education.num</th>\n",
              "      <td>32561.0</td>\n",
              "      <td>10.080679</td>\n",
              "      <td>2.572720</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital.gain</th>\n",
              "      <td>32561.0</td>\n",
              "      <td>1077.648844</td>\n",
              "      <td>7385.292085</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>99999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital.loss</th>\n",
              "      <td>32561.0</td>\n",
              "      <td>87.303830</td>\n",
              "      <td>402.960219</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4356.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hours.per.week</th>\n",
              "      <td>32561.0</td>\n",
              "      <td>40.437456</td>\n",
              "      <td>12.347429</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>99.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1346b11d-9087-4eac-b93f-2aa720f1de7d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1346b11d-9087-4eac-b93f-2aa720f1de7d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1346b11d-9087-4eac-b93f-2aa720f1de7d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  count           mean  ...       75%        max\n",
              "age             32561.0      38.581647  ...      48.0       90.0\n",
              "fnlwgt          32561.0  189778.366512  ...  237051.0  1484705.0\n",
              "education.num   32561.0      10.080679  ...      12.0       16.0\n",
              "capital.gain    32561.0    1077.648844  ...       0.0    99999.0\n",
              "capital.loss    32561.0      87.303830  ...       0.0     4356.0\n",
              "hours.per.week  32561.0      40.437456  ...      45.0       99.0\n",
              "\n",
              "[6 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Value Counts \n"
      ],
      "metadata": {
        "id": "4Eudu_RLev9t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhCCkv3fe7nH",
        "outputId": "ea5dc511-9875-4216-dae6-86e5f801855e"
      },
      "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",
              "       'income'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below operation is being done just to check if any column contains missing values.\n",
        "we will do this operation on each column, one by one."
      ],
      "metadata": {
        "id": "JvxLBB8QLzkJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking value count\n",
        "df['workclass'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yR5z1OAR3pLr",
        "outputId": "3b884d7c-ede4-4ccd-9889-6696fc94300d"
      },
      "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": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['education'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UR9IbrgL1NCg",
        "outputId": "0ef814d7-98ad-4e51-ec25-db5949e95015"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HS-grad         10501\n",
              "Some-college     7291\n",
              "Bachelors        5355\n",
              "Masters          1723\n",
              "Assoc-voc        1382\n",
              "11th             1175\n",
              "Assoc-acdm       1067\n",
              "10th              933\n",
              "7th-8th           646\n",
              "Prof-school       576\n",
              "9th               514\n",
              "12th              433\n",
              "Doctorate         413\n",
              "5th-6th           333\n",
              "1st-4th           168\n",
              "Preschool          51\n",
              "Name: education, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['marital.status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cReYZu0D1SD9",
        "outputId": "202cbbc7-dbe1-4196-f044-5889bc03daee"
      },
      "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": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['occupation'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lqJUGb251kcT",
        "outputId": "a5fbf21e-da99-494d-8f11-8f3236a4f063"
      },
      "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": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['race'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "35rVWx-_2JYj",
        "outputId": "375264a6-1ab4-4ee0-b7cd-d22173f9ec21"
      },
      "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": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1CwPVspX2JTs",
        "outputId": "942c7c90-06da-47e2-b0d4-6fca0726ca80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      21790\n",
              "Female    10771\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['native.country'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gRXqzNSR2JHu",
        "outputId": "b767a8b7-73be-49bd-816b-94fb28a85956"
      },
      "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",
              "Trinadad&Tobago                  19\n",
              "Cambodia                         19\n",
              "Laos                             18\n",
              "Thailand                         18\n",
              "Yugoslavia                       16\n",
              "Outlying-US(Guam-USVI-etc)       14\n",
              "Honduras                         13\n",
              "Hungary                          13\n",
              "Scotland                         12\n",
              "Holand-Netherlands                1\n",
              "Name: native.country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Filling ? Values\n",
        "\n",
        "there are many ways for dealing with '?' values like, forward-filling, filling with mean/meaian of that perticular column(for numeric type) or replacing with top most value.\n",
        "\n",
        "Here we will replace '?', with most occuring value in that perticular column."
      ],
      "metadata": {
        "id": "qJcOvJIXgskb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Replacing '?' with 'NaN Using Numpy\n",
        "#df.replace('?', np.NaN, inplace = True)\n",
        "#df.head()\n",
        "\n",
        "#Forward fill applying to Replace 'NaN'\n",
        "\n",
        "#df.fillna(method = 'ffill', inplace = True)\n",
        "#df.head()"
      ],
      "metadata": {
        "id": "YO48GUuA2I33"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['workclass'] = df['workclass'].replace('?', 'Private')\n",
        "df['native.country'] = df['native.country'].replace('?','United-States')\n",
        "df['occupation'] = df['occupation'].replace('?', 'Prof-specialty')\n"
      ],
      "metadata": {
        "id": "hiu0Cd3Treux"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Engineering\n",
        "In this Step, we will bind most relevant features in same category.\n",
        "this may affect results, but we are doing just for more clarity in visualization"
      ],
      "metadata": {
        "id": "g5bkQPSoii7u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10) #Just checking, '?' values been changed or not"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "1pRswuyExEq3",
        "outputId": "73742ec2-7579-429a-9e1e-18e335e80021"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-24b7adfd-5e8b-44f0-916a-669c9c169640\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "      <td>Private</td>\n",
              "      <td>77053</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</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>82</td>\n",
              "      <td>Private</td>\n",
              "      <td>132870</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</td>\n",
              "      <td>18</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>66</td>\n",
              "      <td>Private</td>\n",
              "      <td>186061</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</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>54</td>\n",
              "      <td>Private</td>\n",
              "      <td>140359</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>4</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3900</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>41</td>\n",
              "      <td>Private</td>\n",
              "      <td>264663</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Separated</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3900</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>34</td>\n",
              "      <td>Private</td>\n",
              "      <td>216864</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Other-service</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3770</td>\n",
              "      <td>45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>150601</td>\n",
              "      <td>10th</td>\n",
              "      <td>6</td>\n",
              "      <td>Separated</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>3770</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>74</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>88638</td>\n",
              "      <td>Doctorate</td>\n",
              "      <td>16</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Other-relative</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3683</td>\n",
              "      <td>20</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>68</td>\n",
              "      <td>Federal-gov</td>\n",
              "      <td>422013</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3683</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>41</td>\n",
              "      <td>Private</td>\n",
              "      <td>70037</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Craft-repair</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>3004</td>\n",
              "      <td>60</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24b7adfd-5e8b-44f0-916a-669c9c169640')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-24b7adfd-5e8b-44f0-916a-669c9c169640 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-24b7adfd-5e8b-44f0-916a-669c9c169640');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age    workclass  fnlwgt  ... hours.per.week  native.country income\n",
              "0   90      Private   77053  ...             40   United-States  <=50K\n",
              "1   82      Private  132870  ...             18   United-States  <=50K\n",
              "2   66      Private  186061  ...             40   United-States  <=50K\n",
              "3   54      Private  140359  ...             40   United-States  <=50K\n",
              "4   41      Private  264663  ...             40   United-States  <=50K\n",
              "5   34      Private  216864  ...             45   United-States  <=50K\n",
              "6   38      Private  150601  ...             40   United-States  <=50K\n",
              "7   74    State-gov   88638  ...             20   United-States   >50K\n",
              "8   68  Federal-gov  422013  ...             40   United-States  <=50K\n",
              "9   41      Private   70037  ...             60   United-States   >50K\n",
              "\n",
              "[10 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "for education column,\n",
        "binding preschoool, 1st-4th, 5th-6th, 7th-8th, 9th, 10th, 11th and 12th into School Category.\n",
        "and so on..... for required columns "
      ],
      "metadata": {
        "id": "7nhc8BViOUB7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.education = df.education.replace(['Preschool', '1st-4th','5th-6th','7th-8th','9th','10th','11th','12th'],'School')\n",
        "df.education = df.education.replace(['Some-college','Assoc-acdm','Prof-school','Assoc-voc'],'Higher')"
      ],
      "metadata": {
        "id": "CsChAggXiieg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In Below Step, we converting income column's values into 0 & 1.\n",
        "Where 0 represents salary less than and Equals to 50k.\n",
        "1 represents salary greater than 50k"
      ],
      "metadata": {
        "id": "C_tC0gshO9Id"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['income'] = df['income'].replace('<=50K', 0)\n",
        "df['income'] = df['income'].replace('>50K', 1)"
      ],
      "metadata": {
        "id": "Yob9f4aakA4D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['marital.status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkUsEVC4tBcx",
        "outputId": "a2a0abd6-ba99-4539-aed7-d6d4e97d7465"
      },
      "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": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['marital.status'] = df['marital.status'].replace(['Married-civ-spouse','Married-spouse-absent','Married-AF-spouse'],'Married')"
      ],
      "metadata": {
        "id": "jUi5Y8yhszNk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['workclass'] = df['workclass'].replace(['Local-gov','State-gov','Federal-gov'],'Govt')\n",
        "df['workclass'] = df['workclass'].replace(['Self-emp-not-inc','Self-emp-inc'],'Self-emp')"
      ],
      "metadata": {
        "id": "0GwyOnNWtkAm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "cov() calculates the covariance among all the columns which have numerical values."
      ],
      "metadata": {
        "id": "4cinTsp8PqW9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.cov() #Covariance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "O_UttO46y8vn",
        "outputId": "a9cb8415-53ce-4dc3-8e87-03feb330299c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e7871f79-54dd-4671-b193-81499caa7a9b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>186.061400</td>\n",
              "      <td>-1.103507e+05</td>\n",
              "      <td>1.281849</td>\n",
              "      <td>7.824819e+03</td>\n",
              "      <td>317.560742</td>\n",
              "      <td>11.580130</td>\n",
              "      <td>1.364997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fnlwgt</th>\n",
              "      <td>-110350.685300</td>\n",
              "      <td>1.114080e+10</td>\n",
              "      <td>-11729.527298</td>\n",
              "      <td>3.366625e+05</td>\n",
              "      <td>-436030.333167</td>\n",
              "      <td>-24460.426185</td>\n",
              "      <td>-427.056721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education.num</th>\n",
              "      <td>1.281849</td>\n",
              "      <td>-1.172953e+04</td>\n",
              "      <td>6.618890</td>\n",
              "      <td>2.330008e+03</td>\n",
              "      <td>82.856445</td>\n",
              "      <td>4.705338</td>\n",
              "      <td>0.368685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital.gain</th>\n",
              "      <td>7824.818537</td>\n",
              "      <td>3.366625e+05</td>\n",
              "      <td>2330.007877</td>\n",
              "      <td>5.454254e+07</td>\n",
              "      <td>-94085.760688</td>\n",
              "      <td>7150.032029</td>\n",
              "      <td>705.230910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital.loss</th>\n",
              "      <td>317.560742</td>\n",
              "      <td>-4.360303e+05</td>\n",
              "      <td>82.856445</td>\n",
              "      <td>-9.408576e+04</td>\n",
              "      <td>162376.937814</td>\n",
              "      <td>269.953755</td>\n",
              "      <td>25.935432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hours.per.week</th>\n",
              "      <td>11.580130</td>\n",
              "      <td>-2.446043e+04</td>\n",
              "      <td>4.705338</td>\n",
              "      <td>7.150032e+03</td>\n",
              "      <td>269.953755</td>\n",
              "      <td>152.458995</td>\n",
              "      <td>1.212651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>income</th>\n",
              "      <td>1.364997</td>\n",
              "      <td>-4.270567e+02</td>\n",
              "      <td>0.368685</td>\n",
              "      <td>7.052309e+02</td>\n",
              "      <td>25.935432</td>\n",
              "      <td>1.212651</td>\n",
              "      <td>0.182826</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7871f79-54dd-4671-b193-81499caa7a9b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e7871f79-54dd-4671-b193-81499caa7a9b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e7871f79-54dd-4671-b193-81499caa7a9b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                          age        fnlwgt  ...  hours.per.week      income\n",
              "age                186.061400 -1.103507e+05  ...       11.580130    1.364997\n",
              "fnlwgt         -110350.685300  1.114080e+10  ...   -24460.426185 -427.056721\n",
              "education.num        1.281849 -1.172953e+04  ...        4.705338    0.368685\n",
              "capital.gain      7824.818537  3.366625e+05  ...     7150.032029  705.230910\n",
              "capital.loss       317.560742 -4.360303e+05  ...      269.953755   25.935432\n",
              "hours.per.week      11.580130 -2.446043e+04  ...      152.458995    1.212651\n",
              "income               1.364997 -4.270567e+02  ...        1.212651    0.182826\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualization"
      ],
      "metadata": {
        "id": "XODlcA6GP6fU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), annot=True) #heatmap for reading about correlation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "6PeZHlMylKo9",
        "outputId": "45396393-0192-4a4f-ac9c-654e242ad1a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a10721dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 176
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observations according above visuals:\n",
        "\n",
        "\n",
        "\n",
        "*   Education-Number(Basically No. of Degrees) and income have cov of 0.34 it means these both attributes are slightly proportional.\n",
        "*   same with the Income and Age.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "LkFRZej4QI5d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Histogram\n",
        "its important for numeric data"
      ],
      "metadata": {
        "id": "zKKkQH3bldTI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(figsize=(12,12), layout=(3,3), sharex=False )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "id": "GXjHMoCS_5_l",
        "outputId": "9db0d997-34b9-42f6-e247-13e3dc6875ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f1a1396c1d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a105f6c90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a105bb110>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1a1056d610>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a10524b10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a1055bfd0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1a1051c5d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a104d3a10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a104d3a50>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 178
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x864 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BoxPlot"
      ],
      "metadata": {
        "id": "plvtWtBHmmej"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(kind ='box', figsize=(12,12), layout=(3,3), sharex = False, subplots = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "AxyaIySqmj-Y",
        "outputId": "24f1b444-ca3d-4ae0-c9ba-8d7d1b24ce0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                  AxesSubplot(0.125,0.657941;0.227941x0.222059)\n",
              "fnlwgt            AxesSubplot(0.398529,0.657941;0.227941x0.222059)\n",
              "education.num     AxesSubplot(0.672059,0.657941;0.227941x0.222059)\n",
              "capital.gain         AxesSubplot(0.125,0.391471;0.227941x0.222059)\n",
              "capital.loss      AxesSubplot(0.398529,0.391471;0.227941x0.222059)\n",
              "hours.per.week    AxesSubplot(0.672059,0.391471;0.227941x0.222059)\n",
              "income                  AxesSubplot(0.125,0.125;0.227941x0.222059)\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 179
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x864 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obvservations:\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "*   Most people ranges in age group of 30 to 50yrs.\n",
        "*   most of people have 9-12 degrees/diplomas\n",
        "\n",
        "\n",
        "*   Almost all of people working 40 hours per week\n",
        "*   few people earning >50K in comparison of who are earning <=50K.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "41XfFa9oSJwu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['income'], palette='Greens_r', hue='education',data=df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "khSfPPOSrJqW",
        "outputId": "01643e9a-dd08-4ae9-c40f-b96a83d6199c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a1027c990>"
            ]
          },
          "metadata": {},
          "execution_count": 180
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observations:\n",
        "\n",
        "\n",
        "\n",
        "*   people who are earning Highest in less than 50k category, have HS-grap Education.\n",
        "*   People who are earning Highest in more than 50k category, have Higher level education.\n",
        "\n"
      ],
      "metadata": {
        "id": "69OSdJBvTFVJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['income'], palette='coolwarm', hue='relationship', data = df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "nR0k89FMrJn2",
        "outputId": "d8cd7106-f480-4af0-be5a-014a26b386c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a10013b10>"
            ]
          },
          "metadata": {},
          "execution_count": 181
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['income'], palette='coolwarm', hue='sex', data = df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "zJlisXGWnREs",
        "outputId": "8b1cbfaa-0e1f-4c17-874f-7a261d21a4b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a0ff52810>"
            ]
          },
          "metadata": {},
          "execution_count": 182
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observations:\n",
        "\n",
        "\n",
        "*   Males are earning more than females in both categories\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "3RY1xlErTqnC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['race'], hue='income', data=df, palette='magma')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "-QhUFk6_n9vz",
        "outputId": "9a35ddd6-ba0a-4ec6-9122-0ba05e12791f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a0fd006d0>"
            ]
          },
          "metadata": {},
          "execution_count": 183
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observations:\n",
        "\n",
        "\n",
        "*   White people are earning more than everone else.\n",
        "*   Americal-Indian-Eskimo people are earning less.\n",
        "\n"
      ],
      "metadata": {
        "id": "XWzgF18gT6fs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(3, 2, figsize=(12,12))\n",
        "index = 0\n",
        "for i in range(3):\n",
        "    for j in range(i+1,4):\n",
        "        ax1 = int(index/2)\n",
        "        ax2 = index % 2\n",
        "        axes[ax1][ax2].scatter(df[df.columns[i]], df[df.columns[j]], color='red')\n",
        "        axes[ax1][ax2].set_xlabel(df.columns[i])\n",
        "        axes[ax1][ax2].set_ylabel(df.columns[j])\n",
        "        index = index + 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "M3UbP4bAK4i9",
        "outputId": "97b59274-2b84-41a6-823f-a5a999964df5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x864 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "SgbN0AMqLs7U"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}