My_WS01.ipynb

{
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sbogde/pandamonium/blob/main/My_WS01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b7b9cdb2-208b-4adf-9d0b-dba757ff7e24",
      "metadata": {
        "id": "b7b9cdb2-208b-4adf-9d0b-dba757ff7e24"
      },
      "source": [
        "# Workshop 01"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "3d329a1b-15a3-45d2-bb3b-436b62dfede7",
      "metadata": {
        "id": "3d329a1b-15a3-45d2-bb3b-436b62dfede7"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "2e90c6f7-3896-4321-86ea-a10f92996097",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2e90c6f7-3896-4321-86ea-a10f92996097",
        "outputId": "81d0f401-f0c2-404c-be48-feeae57c8ace"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Loading the 'adult.csv' dataset into a pandas DataFrame called 'data'\n",
        "# Check if running in Google Colab\n",
        "# Dynamically set the file path based on the execution environment\n",
        "if 'google.colab' in str(get_ipython()):\n",
        "    from google.colab import drive, data_table\n",
        "    drive.mount('/content/drive', force_remount=True)\n",
        "    data_path = '/content/drive/My Drive/Colab Notebooks/data-mining/data/adult.csv'\n",
        "    data_table.enable_dataframe_formatter()\n",
        "else:\n",
        "    data_path = './data/adult.csv'  # Local path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "901cc1ba-c3d5-4517-ba88-fc70584d307e",
      "metadata": {
        "id": "901cc1ba-c3d5-4517-ba88-fc70584d307e"
      },
      "outputs": [],
      "source": [
        "# Loading the 'adult.csv' dataset into a pandas DataFrame called 'data'\n",
        "data = pd.read_csv(data_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "a815c9b3-4e7c-4c37-834c-7c6059c268fd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
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        "id": "a815c9b3-4e7c-4c37-834c-7c6059c268fd",
        "outputId": "1cf335e5-5c79-4c02-d004-61501284d94e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   39          State-gov   77516   Bachelors   13        Never-married  \\\n",
              "0  50   Self-emp-not-inc   83311   Bachelors   13   Married-civ-spouse   \n",
              "1  38            Private  215646     HS-grad    9             Divorced   \n",
              "2  53            Private  234721        11th    7   Married-civ-spouse   \n",
              "3  28            Private  338409   Bachelors   13   Married-civ-spouse   \n",
              "4  37            Private  284582     Masters   14   Married-civ-spouse   \n",
              "\n",
              "         Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
              "0     Exec-managerial         Husband   White     Male      0   0   13   \n",
              "1   Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
              "2   Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
              "3      Prof-specialty            Wife   Black   Female      0   0   40   \n",
              "4     Exec-managerial            Wife   White   Female      0   0   40   \n",
              "\n",
              "    United-States   <=50K  \n",
              "0   United-States   <=50K  \n",
              "1   United-States   <=50K  \n",
              "2   United-States   <=50K  \n",
              "3            Cuba   <=50K  \n",
              "4   United-States   <=50K  "
            ],
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              "      <td>234721</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
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              "      <td>338409</td>\n",
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              "      <td>Cuba</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>37</td>\n",
              "      <td>Private</td>\n",
              "      <td>284582</td>\n",
              "      <td>Masters</td>\n",
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 \"samples\": [\n          \" Married-civ-spouse\",\n          \" Divorced\",\n          \" Married-AF-spouse\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" Adm-clerical\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \" Machine-op-inspct\",\n          \" ?\",\n          \" Exec-managerial\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" Not-in-family\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \" Husband\",\n          \" Not-in-family\",\n          \" Other-relative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" White\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \" Black\",\n          \" Other\",\n          \" Asian-Pac-Islander\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" Male\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \" Female\",\n          \" Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" 2174\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7385,\n        \"min\": 0,\n        \"max\": 99999,\n        \"num_unique_values\": 119,\n        \"samples\": [\n          3781,\n          15831\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 402,\n        \"min\": 0,\n        \"max\": 4356,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          419,\n          2051\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" 40\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          6,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" United-States\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 42,\n        \"samples\": [\n          \" El-Salvador\",\n          \" Philippines\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" <=50K\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \" >50K\",\n          \" <=50K\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            },
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 50,\n            'f': \"50\",\n        },\n\" Self-emp-not-inc\",\n{\n            'v': 83311,\n            'f': \"83311\",\n        },\n\" Bachelors\",\n{\n            'v': 13,\n            'f': \"13\",\n        },\n\" Married-civ-spouse\",\n\" Exec-managerial\",\n\" Husband\",\n\" White\",\n\" Male\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 13,\n            'f': \"13\",\n        },\n\" United-States\",\n\" <=50K\"],\n [{\n            'v': 1,\n            'f': \"1\",\n        },\n{\n            'v': 38,\n            'f': \"38\",\n        },\n\" Private\",\n{\n            'v': 215646,\n            'f': \"215646\",\n        },\n\" HS-grad\",\n{\n            'v': 9,\n            'f': \"9\",\n        },\n\" Divorced\",\n\" Handlers-cleaners\",\n\" Not-in-family\",\n\" White\",\n\" Male\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 40,\n            'f': \"40\",\n        },\n\" United-States\",\n\" <=50K\"],\n [{\n            'v': 2,\n            'f': \"2\",\n        },\n{\n            'v': 53,\n            'f': \"53\",\n        },\n\" Private\",\n{\n            'v': 234721,\n            'f': \"234721\",\n        },\n\" 11th\",\n{\n            'v': 7,\n            'f': \"7\",\n        },\n\" Married-civ-spouse\",\n\" Handlers-cleaners\",\n\" Husband\",\n\" Black\",\n\" Male\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 40,\n            'f': \"40\",\n        },\n\" United-States\",\n\" <=50K\"],\n [{\n            'v': 3,\n            'f': \"3\",\n        },\n{\n            'v': 28,\n            'f': \"28\",\n        },\n\" Private\",\n{\n            'v': 338409,\n            'f': \"338409\",\n        },\n\" Bachelors\",\n{\n            'v': 13,\n            'f': \"13\",\n        },\n\" Married-civ-spouse\",\n\" Prof-specialty\",\n\" Wife\",\n\" Black\",\n\" Female\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 40,\n            'f': \"40\",\n        },\n\" Cuba\",\n\" <=50K\"],\n [{\n            'v': 4,\n            'f': \"4\",\n        },\n{\n            'v': 37,\n            'f': \"37\",\n        },\n\" Private\",\n{\n            'v': 284582,\n            'f': \"284582\",\n        },\n\" Masters\",\n{\n            'v': 14,\n            'f': \"14\",\n        },\n\" Married-civ-spouse\",\n\" Exec-managerial\",\n\" Wife\",\n\" White\",\n\" Female\",\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 40,\n            'f': \"40\",\n        },\n\" United-States\",\n\" <=50K\"]],\n        columns: [[\"number\", \"index\"], [\"number\", \"39\"], [\"string\", \" State-gov\"], [\"number\", \" 77516\"], [\"string\", \" Bachelors\"], [\"number\", \" 13\"], [\"string\", \" Never-married\"], [\"string\", \" Adm-clerical\"], [\"string\", \" Not-in-family\"], [\"string\", \" White\"], [\"string\", \" Male\"], [\"number\", \" 2174\"], [\"number\", \" 0\"], [\"number\", \" 40\"], [\"string\", \" United-States\"], [\"string\", \" <=50K\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-ee3d2d2f-fdc4-4816-a3d2-8bda742e3326\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee3d2d2f-fdc4-4816-a3d2-8bda742e3326')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n    </g>\n</svg>\n  </button>\n  \n<style>\n  .colab-df-quickchart {\n      --bg-color: #E8F0FE;\n      --fill-color: #1967D2;\n      --hover-bg-color: #E2EBFA;\n      --hover-fill-color: #174EA6;\n      --disabled-fill-color: #AAA;\n      --disabled-bg-color: #DDD;\n  }\n\n  [theme=dark] .colab-df-quickchart {\n      --bg-color: #3B4455;\n      --fill-color: #D2E3FC;\n      --hover-bg-color: #434B5C;\n      --hover-fill-color: #FFFFFF;\n      --disabled-bg-color: #3B4455;\n      --disabled-fill-color: #666;\n  }\n\n  .colab-df-quickchart {\n    background-color: var(--bg-color);\n    border: none;\n    border-radius: 50%;\n    cursor: pointer;\n    display: none;\n    fill: var(--fill-color);\n    height: 32px;\n    padding: 0;\n    width: 32px;\n  }\n\n  .colab-df-quickchart:hover {\n    background-color: var(--hover-bg-color);\n    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n    fill: var(--button-hover-fill-color);\n  }\n\n  .colab-df-quickchart-complete:disabled,\n  .colab-df-quickchart-complete:disabled:hover {\n    background-color: var(--disabled-bg-color);\n    fill: var(--disabled-fill-color);\n    box-shadow: none;\n  }\n\n  .colab-df-spinner {\n    border: 2px solid var(--fill-color);\n    border-color: transparent;\n    border-bottom-color: var(--fill-color);\n    animation:\n      spin 1s steps(1) infinite;\n  }\n\n  @keyframes spin {\n    0% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n      border-left-color: var(--fill-color);\n    }\n    20% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    30% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n      border-right-color: var(--fill-color);\n    }\n    40% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    60% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n    }\n    80% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-bottom-color: var(--fill-color);\n    }\n    90% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n    }\n  }\n</style>\n\n  <script>\n    async function quickchart(key) {\n      const quickchartButtonEl =\n        document.querySelector('#' + key + ' button');\n      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n      quickchartButtonEl.classList.add('colab-df-spinner');\n      try {\n        const charts = await google.colab.kernel.invokeFunction(\n            'suggestCharts', [key], {});\n      } catch (error) {\n        console.error('Error during call to suggestCharts:', error);\n      }\n      quickchartButtonEl.classList.remove('colab-df-spinner');\n      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n    }\n    (() => {\n      let quickchartButtonEl =\n        document.querySelector('#df-ee3d2d2f-fdc4-4816-a3d2-8bda742e3326 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "9ef39570-4883-4c90-9356-a85f951b4788",
      "metadata": {
        "id": "9ef39570-4883-4c90-9356-a85f951b4788"
      },
      "outputs": [],
      "source": [
        "# Define column names as they are not present in the dataset\n",
        "column_names = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education_num\", \"marital_status\",\n",
        "                \"occupation\", \"relationship\", \"race\", \"sex\", \"capital_gain\", \"capital_loss\",\n",
        "                \"hours_per_week\", \"native_country\", \"income\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "fd71abf9-2549-466b-8bec-9f5c0073a212",
      "metadata": {
        "id": "fd71abf9-2549-466b-8bec-9f5c0073a212"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(data_path, names=column_names, na_values=\"?\", skipinitialspace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "24ffa261-b4a8-4a1f-a528-ffc43cdfcf0f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "24ffa261-b4a8-4a1f-a528-ffc43cdfcf0f",
        "outputId": "48606d3d-575a-46c7-8d74-6cb43162fb14"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  workclass  fnlwgt  education  education_num marital_status  \\\n",
              "0   39  State-gov   77516  Bachelors             13  Never-married   \n",
              "\n",
              "     occupation   relationship   race   sex  capital_gain  capital_loss  \\\n",
              "0  Adm-clerical  Not-in-family  White  Male          2174             0   \n",
              "\n",
              "   hours_per_week native_country income  \n",
              "0              40  United-States  <=50K  "
            ],
            "text/html": [
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              "    <div>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
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              "      <td>&lt;=50K</td>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-2eb91c0a-4f3b-4041-9c9b-a7244b351252 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-2eb91c0a-4f3b-4041-9c9b-a7244b351252');\n",
              "        const dataTable =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 32561,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 17,\n        \"max\": 90,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          28,\n          73,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workclass\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Self-emp-not-inc\",\n          \"Self-emp-inc\",\n          \"State-gov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fnlwgt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105549,\n        \"min\": 12285,\n        \"max\": 1484705,\n        \"num_unique_values\": 21648,\n        \"samples\": [\n          128485,\n          469907,\n          235951\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Bachelors\",\n          \"HS-grad\",\n          \"Some-college\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 16,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          13,\n          9,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Never-married\",\n          \"Married-civ-spouse\",\n          \"Married-AF-spouse\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"occupation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"Machine-op-inspct\",\n          \"Protective-serv\",\n          \"Adm-clerical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relationship\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Not-in-family\",\n          \"Husband\",\n          \"Other-relative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"race\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Black\",\n          \"Other\",\n          \"Asian-Pac-Islander\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital_gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7385,\n        \"min\": 0,\n        \"max\": 99999,\n        \"num_unique_values\": 119,\n        \"samples\": [\n          3781,\n          15831\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital_loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 402,\n        \"min\": 0,\n        \"max\": 4356,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          419,\n          2051\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours_per_week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          6,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"native_country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"El-Salvador\",\n          \"Italy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \">50K\",\n          \"<=50K\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            },
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 39,\n            'f': \"39\",\n        },\n\"State-gov\",\n{\n            'v': 77516,\n            'f': \"77516\",\n        },\n\"Bachelors\",\n{\n            'v': 13,\n            'f': \"13\",\n        },\n\"Never-married\",\n\"Adm-clerical\",\n\"Not-in-family\",\n\"White\",\n\"Male\",\n{\n            'v': 2174,\n            'f': \"2174\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 40,\n            'f': \"40\",\n        },\n\"United-States\",\n\"<=50K\"]],\n        columns: [[\"number\", \"index\"], [\"number\", \"age\"], [\"string\", \"workclass\"], [\"number\", \"fnlwgt\"], [\"string\", \"education\"], [\"number\", \"education_num\"], [\"string\", \"marital_status\"], [\"string\", \"occupation\"], [\"string\", \"relationship\"], [\"string\", \"race\"], [\"string\", \"sex\"], [\"number\", \"capital_gain\"], [\"number\", \"capital_loss\"], [\"number\", \"hours_per_week\"], [\"string\", \"native_country\"], [\"string\", \"income\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-28261356-1f18-4f2f-aec7-525ae17e288c\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-28261356-1f18-4f2f-aec7-525ae17e288c')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n    </g>\n</svg>\n  </button>\n  \n<style>\n  .colab-df-quickchart {\n      --bg-color: #E8F0FE;\n      --fill-color: #1967D2;\n      --hover-bg-color: #E2EBFA;\n      --hover-fill-color: #174EA6;\n      --disabled-fill-color: #AAA;\n      --disabled-bg-color: #DDD;\n  }\n\n  [theme=dark] .colab-df-quickchart {\n      --bg-color: #3B4455;\n      --fill-color: #D2E3FC;\n      --hover-bg-color: #434B5C;\n      --hover-fill-color: #FFFFFF;\n      --disabled-bg-color: #3B4455;\n      --disabled-fill-color: #666;\n  }\n\n  .colab-df-quickchart {\n    background-color: var(--bg-color);\n    border: none;\n    border-radius: 50%;\n    cursor: pointer;\n    display: none;\n    fill: var(--fill-color);\n    height: 32px;\n    padding: 0;\n    width: 32px;\n  }\n\n  .colab-df-quickchart:hover {\n    background-color: var(--hover-bg-color);\n    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n    fill: var(--button-hover-fill-color);\n  }\n\n  .colab-df-quickchart-complete:disabled,\n  .colab-df-quickchart-complete:disabled:hover {\n    background-color: var(--disabled-bg-color);\n    fill: var(--disabled-fill-color);\n    box-shadow: none;\n  }\n\n  .colab-df-spinner {\n    border: 2px solid var(--fill-color);\n    border-color: transparent;\n    border-bottom-color: var(--fill-color);\n    animation:\n      spin 1s steps(1) infinite;\n  }\n\n  @keyframes spin {\n    0% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n      border-left-color: var(--fill-color);\n    }\n    20% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    30% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n      border-right-color: var(--fill-color);\n    }\n    40% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    60% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n    }\n    80% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-bottom-color: var(--fill-color);\n    }\n    90% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n    }\n  }\n</style>\n\n  <script>\n    async function quickchart(key) {\n      const quickchartButtonEl =\n        document.querySelector('#' + key + ' button');\n      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n      quickchartButtonEl.classList.add('colab-df-spinner');\n      try {\n        const charts = await google.colab.kernel.invokeFunction(\n            'suggestCharts', [key], {});\n      } catch (error) {\n        console.error('Error during call to suggestCharts:', error);\n      }\n      quickchartButtonEl.classList.remove('colab-df-spinner');\n      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n    }\n    (() => {\n      let quickchartButtonEl =\n        document.querySelector('#df-28261356-1f18-4f2f-aec7-525ae17e288c button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "6f76aa57-3f3d-411c-b6b1-1be85eb8fb97",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6f76aa57-3f3d-411c-b6b1-1be85eb8fb97",
        "outputId": "c81f35d7-f0b4-4048-ff40-50dd3d39c907"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data shape: (32561, 15)\n"
          ]
        }
      ],
      "source": [
        "#The DataFrame.shape property tells you the dimensionality of the dataset (Pandas DataFrame) in the form of the number of rows and columns. In this case, how many rows and columns are needed?\n",
        "print(f'data shape: {data.shape}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "43ec6179-e3c1-449e-8f8d-1d2230e3c9a4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "43ec6179-e3c1-449e-8f8d-1d2230e3c9a4",
        "outputId": "a8476a4a-731e-4bb1-b81d-4c527afd58c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  count           mean            std      min       25%  \\\n",
              "age             32561.0      38.581647      13.640433     17.0      28.0   \n",
              "fnlwgt          32561.0  189778.366512  105549.977697  12285.0  117827.0   \n",
              "education_num   32561.0      10.080679       2.572720      1.0       9.0   \n",
              "capital_gain    32561.0    1077.648844    7385.292085      0.0       0.0   \n",
              "capital_loss    32561.0      87.303830     402.960219      0.0       0.0   \n",
              "hours_per_week  32561.0      40.437456      12.347429      1.0      40.0   \n",
              "\n",
              "                     50%       75%        max  \n",
              "age                 37.0      48.0       90.0  \n",
              "fnlwgt          178356.0  237051.0  1484705.0  \n",
              "education_num       10.0      12.0       16.0  \n",
              "capital_gain         0.0       0.0    99999.0  \n",
              "capital_loss         0.0       0.0     4356.0  \n",
              "hours_per_week      40.0      45.0       99.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f4fc6d3a-75f4-4338-9703-d95671270891\" 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",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4fc6d3a-75f4-4338-9703-d95671270891')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f4fc6d3a-75f4-4338-9703-d95671270891 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-f4fc6d3a-75f4-4338-9703-d95671270891');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e82ab7ba-f937-4cdf-9b7b-cdfd3e0ee6f1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e82ab7ba-f937-4cdf-9b7b-cdfd3e0ee6f1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e82ab7ba-f937-4cdf-9b7b-cdfd3e0ee6f1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 32561.0,\n        \"max\": 32561.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          32561.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77375.40916972022,\n        \"min\": 10.0806793403151,\n        \"max\": 189778.36651208502,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          38.58164675532078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42552.31647110165,\n        \"min\": 2.5727203320673406,\n        \"max\": 105549.97769702233,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          13.640432553581146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5013.783268816739,\n        \"min\": 0.0,\n        \"max\": 12285.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12285.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48096.38695647175,\n        \"min\": 0.0,\n        \"max\": 117827.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          117827.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72806.43070979376,\n        \"min\": 0.0,\n        \"max\": 178356.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          178356.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96767.09463242142,\n        \"min\": 0.0,\n        \"max\": 237051.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          237051.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 598900.1903291566,\n        \"min\": 16.0,\n        \"max\": 1484705.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          90.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            },
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[\"age\",\n{\n            'v': 32561.0,\n            'f': \"32561.0\",\n        },\n{\n            'v': 38.58164675532078,\n            'f': \"38.58164675532078\",\n        },\n{\n            'v': 13.640432553581146,\n            'f': \"13.640432553581146\",\n        },\n{\n            'v': 17.0,\n            'f': \"17.0\",\n        },\n{\n            'v': 28.0,\n            'f': \"28.0\",\n        },\n{\n            'v': 37.0,\n            'f': \"37.0\",\n        },\n{\n            'v': 48.0,\n            'f': \"48.0\",\n        },\n{\n            'v': 90.0,\n            'f': \"90.0\",\n        }],\n [\"fnlwgt\",\n{\n            'v': 32561.0,\n            'f': \"32561.0\",\n        },\n{\n            'v': 189778.36651208502,\n            'f': \"189778.36651208502\",\n        },\n{\n            'v': 105549.97769702233,\n            'f': \"105549.97769702233\",\n        },\n{\n            'v': 12285.0,\n            'f': \"12285.0\",\n        },\n{\n            'v': 117827.0,\n            'f': \"117827.0\",\n        },\n{\n            'v': 178356.0,\n            'f': \"178356.0\",\n        },\n{\n            'v': 237051.0,\n            'f': \"237051.0\",\n        },\n{\n            'v': 1484705.0,\n            'f': \"1484705.0\",\n        }],\n [\"education_num\",\n{\n            'v': 32561.0,\n            'f': \"32561.0\",\n        },\n{\n            'v': 10.0806793403151,\n            'f': \"10.0806793403151\",\n        },\n{\n            'v': 2.5727203320673406,\n            'f': \"2.5727203320673406\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 9.0,\n            'f': \"9.0\",\n        },\n{\n            'v': 10.0,\n            'f': \"10.0\",\n        },\n{\n            'v': 12.0,\n            'f': \"12.0\",\n        },\n{\n            'v': 16.0,\n            'f': \"16.0\",\n        }],\n [\"capital_gain\",\n{\n            'v': 32561.0,\n            'f': \"32561.0\",\n        },\n{\n            'v': 1077.6488437087312,\n            'f': \"1077.6488437087312\",\n        },\n{\n            'v': 7385.292084839299,\n            'f': \"7385.292084839299\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 99999.0,\n            'f': \"99999.0\",\n        }],\n [\"capital_loss\",\n{\n            'v': 32561.0,\n            'f': \"32561.0\",\n        },\n{\n            'v': 87.303829734959,\n            'f': \"87.303829734959\",\n        },\n{\n            'v': 402.960218649059,\n            'f': \"402.960218649059\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n{\n            'v': 4356.0,\n            'f': \"4356.0\",\n        }],\n [\"hours_per_week\",\n{\n            'v': 32561.0,\n            'f': \"32561.0\",\n        },\n{\n            'v': 40.437455852092995,\n            'f': \"40.437455852092995\",\n        },\n{\n            'v': 12.34742868173081,\n            'f': \"12.34742868173081\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 40.0,\n            'f': \"40.0\",\n        },\n{\n            'v': 40.0,\n            'f': \"40.0\",\n        },\n{\n            'v': 45.0,\n            'f': \"45.0\",\n        },\n{\n            'v': 99.0,\n            'f': \"99.0\",\n        }]],\n        columns: [[\"string\", \"index\"], [\"number\", \"count\"], [\"number\", \"mean\"], [\"number\", \"std\"], [\"number\", \"min\"], [\"number\", \"25%\"], [\"number\", \"50%\"], [\"number\", \"75%\"], [\"number\", \"max\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-b65451ac-7f55-46f3-9c48-52143e01b69c\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b65451ac-7f55-46f3-9c48-52143e01b69c')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n    </g>\n</svg>\n  </button>\n  \n<style>\n  .colab-df-quickchart {\n      --bg-color: #E8F0FE;\n      --fill-color: #1967D2;\n      --hover-bg-color: #E2EBFA;\n      --hover-fill-color: #174EA6;\n      --disabled-fill-color: #AAA;\n      --disabled-bg-color: #DDD;\n  }\n\n  [theme=dark] .colab-df-quickchart {\n      --bg-color: #3B4455;\n      --fill-color: #D2E3FC;\n      --hover-bg-color: #434B5C;\n      --hover-fill-color: #FFFFFF;\n      --disabled-bg-color: #3B4455;\n      --disabled-fill-color: #666;\n  }\n\n  .colab-df-quickchart {\n    background-color: var(--bg-color);\n    border: none;\n    border-radius: 50%;\n    cursor: pointer;\n    display: none;\n    fill: var(--fill-color);\n    height: 32px;\n    padding: 0;\n    width: 32px;\n  }\n\n  .colab-df-quickchart:hover {\n    background-color: var(--hover-bg-color);\n    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n    fill: var(--button-hover-fill-color);\n  }\n\n  .colab-df-quickchart-complete:disabled,\n  .colab-df-quickchart-complete:disabled:hover {\n    background-color: var(--disabled-bg-color);\n    fill: var(--disabled-fill-color);\n    box-shadow: none;\n  }\n\n  .colab-df-spinner {\n    border: 2px solid var(--fill-color);\n    border-color: transparent;\n    border-bottom-color: var(--fill-color);\n    animation:\n      spin 1s steps(1) infinite;\n  }\n\n  @keyframes spin {\n    0% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n      border-left-color: var(--fill-color);\n    }\n    20% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    30% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: 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   console.error('Error during call to suggestCharts:', error);\n      }\n      quickchartButtonEl.classList.remove('colab-df-spinner');\n      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n    }\n    (() => {\n      let quickchartButtonEl =\n        document.querySelector('#df-b65451ac-7f55-46f3-9c48-52143e01b69c button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "data.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "f2aabcf2-40c9-4c3a-bb09-c2da9e20959f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "f2aabcf2-40c9-4c3a-bb09-c2da9e20959f",
        "outputId": "bbf3b7e3-dfc1-488d-d58e-7959dc61296b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                               40\n",
              "workclass                    Private\n",
              "fnlwgt                        121772\n",
              "education                  Assoc-voc\n",
              "education_num                     11\n",
              "marital_status    Married-civ-spouse\n",
              "occupation              Craft-repair\n",
              "relationship                 Husband\n",
              "race              Asian-Pac-Islander\n",
              "sex                             Male\n",
              "capital_gain                       0\n",
              "capital_loss                       0\n",
              "hours_per_week                    40\n",
              "native_country                   NaN\n",
              "income                          >50K\n",
              "Name: 14, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>workclass</th>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fnlwgt</th>\n",
              "      <td>121772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <td>Assoc-voc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education_num</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital_status</th>\n",
              "      <td>Married-civ-spouse</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>occupation</th>\n",
              "      <td>Craft-repair</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relationship</th>\n",
              "      <td>Husband</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>race</th>\n",
              "      <td>Asian-Pac-Islander</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital_gain</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capital_loss</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hours_per_week</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>native_country</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>income</th>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "data.iloc[14]  # Row indexing starts from 0, so row 15 is index 14"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "ca6a5c42-0941-433d-aeaf-257a914f7618",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ca6a5c42-0941-433d-aeaf-257a914f7618",
        "outputId": "23118fea-cd6a-4f6b-a6bf-d7a8821f4736"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "**Missing Values Before Cleaning:**\n",
            "age                  0\n",
            "workclass         1836\n",
            "fnlwgt               0\n",
            "education            0\n",
            "education_num        0\n",
            "marital_status       0\n",
            "occupation        1843\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     583\n",
            "income               0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# ---- Data Preprocessing ----\n",
        "# Check for missing values\n",
        "print(\"\\n**Missing Values Before Cleaning:**\")\n",
        "print(data.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "8a20021c-ca02-4be2-ab76-4de16217e0c3",
      "metadata": {
        "id": "8a20021c-ca02-4be2-ab76-4de16217e0c3"
      },
      "outputs": [],
      "source": [
        "# Drop rows with missing values\n",
        "data.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "50d3f2b2-79ad-4bd6-a4ff-e3981fae38f6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50d3f2b2-79ad-4bd6-a4ff-e3981fae38f6",
        "outputId": "bfde66ef-3436-48d7-e150-fe635924f1f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30162, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "38fdf926-a7b4-46cb-87c4-634fc673e0cd",
      "metadata": {
        "id": "38fdf926-a7b4-46cb-87c4-634fc673e0cd"
      },
      "outputs": [],
      "source": [
        "#The line below will drop/remove fnlwgt from data.\n",
        "#drop(): To drop a column from the dataframe, pass agruments - column name to be dropped and axis = 1. axis = 0 is to dropping row.\n",
        "data = data.drop('fnlwgt', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "93a67309-bca4-475d-a8ad-4518dd7988a9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93a67309-bca4-475d-a8ad-4518dd7988a9",
        "outputId": "561ebf90-76f8-418d-ec12-5dda0496fc81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30162, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "1f10ce94-94ef-4d92-b0cc-464d89ee0cd1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "1f10ce94-94ef-4d92-b0cc-464d89ee0cd1",
        "outputId": "a3d85a92-d515-4f94-9fea-42e408498a58"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "education\n",
              "HS-grad         9840\n",
              "Some-college    6678\n",
              "Bachelors       5044\n",
              "Masters         1627\n",
              "Assoc-voc       1307\n",
              "11th            1048\n",
              "Assoc-acdm      1008\n",
              "10th             820\n",
              "7th-8th          557\n",
              "Prof-school      542\n",
              "9th              455\n",
              "12th             377\n",
              "Doctorate        375\n",
              "5th-6th          288\n",
              "1st-4th          151\n",
              "Preschool         45\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>HS-grad</th>\n",
              "      <td>9840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Some-college</th>\n",
              "      <td>6678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bachelors</th>\n",
              "      <td>5044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Masters</th>\n",
              "      <td>1627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Assoc-voc</th>\n",
              "      <td>1307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11th</th>\n",
              "      <td>1048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Assoc-acdm</th>\n",
              "      <td>1008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10th</th>\n",
              "      <td>820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7th-8th</th>\n",
              "      <td>557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prof-school</th>\n",
              "      <td>542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9th</th>\n",
              "      <td>455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12th</th>\n",
              "      <td>377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doctorate</th>\n",
              "      <td>375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5th-6th</th>\n",
              "      <td>288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1st-4th</th>\n",
              "      <td>151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Preschool</th>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "#value_counts() produces a frequency table, which shows the occurrence of each unique value per feature or attribute in a dataset\n",
        "data['education'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "9cffb36e-4761-4d90-86cc-c3ef1198217c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cffb36e-4761-4d90-86cc-c3ef1198217c",
        "outputId": "837a1b9e-ab5e-43f0-8b65-97167a3dc685"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "data['education'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "2aaa7a41-545f-4fc8-a94f-aac394b3cd75",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2aaa7a41-545f-4fc8-a94f-aac394b3cd75",
        "outputId": "fe502e43-06e5-45c9-a6f3-016e8da776ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "**Income Distribution:**\n",
            "income\n",
            "<=50K    22654\n",
            ">50K      7508\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Income distribution\n",
        "income_counts = data['income'].value_counts()\n",
        "print(\"\\n**Income Distribution:**\")\n",
        "print(income_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "3b7820c3-a908-446f-b30c-ae3773efe5b5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3b7820c3-a908-446f-b30c-ae3773efe5b5",
        "outputId": "5f16b21c-7139-4d44-d7f5-1b4518be1ad2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "**Income by Gender:**\n",
            "sex     income\n",
            "Female  <=50K      8670\n",
            "        >50K       1112\n",
            "Male    <=50K     13984\n",
            "        >50K       6396\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Gender-based income comparison\n",
        "gender_income = data.groupby([\"sex\", \"income\"]).size()\n",
        "print(\"\\n**Income by Gender:**\")\n",
        "print(gender_income)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "6d429fd2-f0ed-4b99-b3b5-36091191f4a6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6d429fd2-f0ed-4b99-b3b5-36091191f4a6",
        "outputId": "b4f54c5c-626c-4cf5-a9e2-a3e64419cac9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "**Income by Gender:**\n",
            "income  <=50K  >50K\n",
            "sex                \n",
            "Female   8670  1112\n",
            "Male    13984  6396\n"
          ]
        }
      ],
      "source": [
        "# Gender-based income comparison\n",
        "gender_income = data.groupby([\"sex\", \"income\"]).size().unstack()\n",
        "print(\"\\n**Income by Gender:**\")\n",
        "print(gender_income)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "c05bfb14-f21f-4b4b-ac2a-6075c9d59556",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c05bfb14-f21f-4b4b-ac2a-6075c9d59556",
        "outputId": "24b98029-f870-4127-ab1a-9139a30a50ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "📌 **Education vs. Income:**\n",
            "income         <=50K    >50K\n",
            "education                   \n",
            "10th           761.0    59.0\n",
            "11th           989.0    59.0\n",
            "12th           348.0    29.0\n",
            "1st-4th        145.0     6.0\n",
            "5th-6th        276.0    12.0\n",
            "7th-8th        522.0    35.0\n",
            "9th            430.0    25.0\n",
            "Assoc-acdm     752.0   256.0\n",
            "Assoc-voc      963.0   344.0\n",
            "Bachelors     2918.0  2126.0\n",
            "Doctorate       95.0   280.0\n",
            "HS-grad       8223.0  1617.0\n",
            "Masters        709.0   918.0\n",
            "Preschool       45.0     NaN\n",
            "Prof-school    136.0   406.0\n",
            "Some-college  5342.0  1336.0\n"
          ]
        }
      ],
      "source": [
        "# Education level vs. Income\n",
        "education_income = data.groupby([\"education\", \"income\"]).size().unstack()\n",
        "print(\"\\n📌 **Education vs. Income:**\")\n",
        "print(education_income)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "b47c2db2-b366-4246-b2fa-8383df5fdc04",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "b47c2db2-b366-4246-b2fa-8383df5fdc04",
        "outputId": "34569c09-f979-4100-912c-1c87e2aaf8bf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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iXaSkpMDd3V35HP8nTJr0JPOSnJ2dHZMmIiKiD4wuQ2s4EJyIiIhIB0yaiIiIiHTApImIiIhIBxzTREREpKL09HSkpaUZuhmfLFNTUxgbG+vlWEyaiIiIVCAiSEhIQFJSkqGb8slzcHCAq6vre8+jaNCkKSoqCpMnT0ZMTAzu3LmDtWvXonnz5sp+EcGIESMwb948JCUloWrVqpgzZw6KFi2q1Hnw4AF69+6NDRs2wMjICC1btsSMGTNgY2Oj1Dl58iR69uyJI0eOIF++fOjduzcGDRqk1ZbffvsNoaGhuHbtGooWLYqJEyeiUaNGqr8GRET0ccpMmJydnWFlZcWJjw1ARPD06VMkJiYCAPLnz/9exzNo0vTkyRN89tln6NSpE1q0aJFl/6RJkzBz5kyEh4fD09MToaGhCAwMxJkzZ2BhYQEACA4Oxp07d7B9+3akpaWhY8eO6NatG5YvXw7g1fwL9evXR0BAAMLCwnDq1Cl06tQJDg4O6NatGwDgwIEDaNu2LcaPH4/GjRtj+fLlaN68OY4dO4bSpUvn3AtCREQfhfT0dCVhcnJyMnRzPmmWlpYAgMTERDg7O7/fpTrJJQDI2rVrlccZGRni6uoqkydPVsqSkpLE3NxcVqxYISIiZ86cEQBy5MgRpc6WLVtEo9HIrVu3RETkl19+EUdHR0lNTVXqDB48WIoXL648bt26tQQFBWm1p3LlyvLNN9/o3P7k5GQBIMnJyTo/h4iIPk7Pnj2TM2fOyNOnTw3dFBKRp0+fypkzZ+TZs2dZ9r3N53euvXvu6tWrSEhIQEBAgFJmb2+PypUrIzo6GgAQHR0NBwcH+Pn5KXUCAgJgZGSEQ4cOKXVq1KgBMzMzpU5gYCDOnz+Phw8fKnVej5NZJzNOdlJTU5GSkqK1ERERvY6X5HIHff0ecm3SlJCQAABwcXHRKndxcVH2JSQkwNnZWWu/iYkJ8uTJo1Unu2O8HuNNdTL3Z2f8+PGwt7dXNq47R0RE9HHLtUlTbjd06FAkJycrW3x8vKGbRERERCrKtUmTq6srAODu3bta5Xfv3lX2ubq6KiPiM718+RIPHjzQqpPdMV6P8aY6mfuzY25urqwzx/XmiIjIUDp06KB15zmpJ9cmTZ6ennB1dUVkZKRSlpKSgkOHDsHf3x8A4O/vj6SkJMTExCh1du7ciYyMDFSuXFmpExUVpTWx2Pbt21G8eHE4OjoqdV6Pk1knMw4RERGRQZOmx48fIzY2FrGxsQBeDf6OjY3FjRs3oNFo0K9fP4wdOxbr16/HqVOnEBISAjc3NyWjLlmyJBo0aICuXbvi8OHD2L9/P3r16oU2bdrAzc0NAPDVV1/BzMwMnTt3RlxcHFatWoUZM2ZgwIABSjv69u2LiIgITJkyBefOncPIkSNx9OhR9OrVK6dfEiIiondWq1Yt9OnTB4MGDUKePHng6uqKkSNHatVJSkrCN998AxcXF1hYWKB06dLYuHGjsv+PP/5AqVKlYG5uDg8PD0yZMkXr+R4eHhg7dixCQkJgY2ODwoULY/369bh37x6aNWsGGxsblC1bFkePHtV63r59+1C9enVYWlrC3d0dffr0wZMnT1R7LVShxq19utq1a5cAyLK1b99eRF5NOxAaGiouLi5ibm4udevWlfPnz2sd4/79+9K2bVuxsbEROzs76dixozx69EirzokTJ6RatWpibm4uBQoUkAkTJmRpy+rVq6VYsWJiZmYmpUqVkk2bNr3VuXDKASIiypQ55UB2t7jrW/v27aVZs2YiIlKzZk2xs7OTkSNHyoULFyQ8PFw0Go1s27ZNRETS09Pl888/l1KlSsm2bdvk8uXLsmHDBtm8ebOIiBw9elSMjIxk9OjRcv78eVm0aJFYWlrKokWLlHiFCxeWPHnySFhYmFy4cEG+/fZbsbOzkwYNGsjq1avl/Pnz0rx5cylZsqRkZGSIiMilS5fE2tpapk2bJhcuXJD9+/eLr6+vdOjQQfXXR+Sffx9v8/mtERExYM720UhJSYG9vT2Sk5M5vomIKCeMtH+P5ybrrx3ZeP78Oa5evQpPT09lMma1dOjQAUlJSVi3bh1q1aqF9PR07N27V9lfqVIl1KlTBxMmTMC2bdvQsGFDnD17FsWKFctyrODgYNy7dw/btm1TygYNGoRNmzYhLi4OwKuepurVq2Pp0qUAXt2Bnj9/foSGhmL06NEAgIMHD8Lf3x937tyBq6srunTpAmNjY/z666/Kcfft24eaNWviyZMnqr9G//T7eJvP71w7pomIiIjeXtmyZbUe58+fX7lpKjY2FgULFsw2YQKAs2fPomrVqlplVatWxcWLF5Genp5tjMwpe8qUKZOlLDPuiRMnsHjxYtjY2ChbYGAgMjIycPXq1Xc91RzHBXuJiIg+IqamplqPNRoNMjIyAPzfkiL6jJE5cWR2ZZlxHz9+jG+++QZ9+vTJcqxChQrppU05gUkTERHRJ6Js2bK4efMmLly4kG1vU8mSJbF//36tsv3796NYsWLvtWZb+fLlcebMGRQpUuSdj5Eb8PIcERHRJ6JmzZqoUaMGWrZsie3bt+Pq1avYsmULIiIiAADfffcdIiMjMWbMGFy4cAHh4eH4+eef8f33379X3MGDB+PAgQPo1asXYmNjcfHiRfz5558f3F3qTJqIiIg+IX/88QcqVqyItm3bwsfHB4MGDVLGK5UvXx6rV6/GypUrUbp0aQwfPhyjR49Ghw4d3itm2bJlsWfPHly4cAHVq1eHr68vhg8frkwP9KHg3XN6wrvniIhyGO+eIx3x7jkiIiKiHMSkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHJoZuABER0afEY8imHI13bUJQjsZ7k927d6N27dpZyu/cuQNXV1fl8ezZszF58mQkJCTgs88+w6xZs1CpUiVlv4eHB/r164d+/foBAEQEAwcOxNy5c7F+/XrUqlVLtXNgTxMRERHp7OHDh3j8+PE7P//8+fO4c+eOsjk7Oyv7Vq1ahQEDBmDEiBE4duwYPvvsMwQGBiIxMTHbY6Wnp6Nz585YsmQJdu3apWrCBDBpIiIion/x8uVLbNq0Ca1atUL+/Plx+fLldz6Ws7MzXF1dlc3I6P9SkalTp6Jr167o2LEjfHx8EBYWBisrKyxcuDDLcVJTU9GqVSvs2LEDe/fuRYUKFd65Tbpi0kRERETZOnXqFL777jsULFgQISEhyJcvH3bt2oXPPvsMAFCqVCnY2Ni8cWvYsGGWY5YrVw758+dHvXr1sH//fqX8xYsXiImJQUBAgFJmZGSEgIAAREdHax3j8ePHCAoKwpkzZ7B//34UL15cpVdAG8c0ERERkeL+/fv43//+h/DwcMTFxaFRo0b45Zdf0LhxY5iZmWnV3bx5M9LS0t54LEtLS+Xn/PnzIywsDH5+fkhNTcX8+fNRq1YtHDp0COXLl8dff/2F9PR0uLi4aB3DxcUF586d0yobM2YMbG1tcfbsWeTLl08PZ60bJk1ERESkmDVrFkaNGoXq1avj0qVLcHd3f2PdwoUL63zc4sWLa/UIValSBZcvX8a0adOwdOnSt2pj/fr1sWPHDowbNw7Tpk17q+e+D16eIyIiIkW3bt0wZswYJCQkoFSpUujYsSN27tyJjIyMLHXf5fLc6ypVqoRLly4BAPLmzQtjY2PcvXtXq87du3e17q4DgLp16+LPP/9EWFgY+vbt+55nrDv2NBEREZHCzc0Nw4YNw7Bhw3DgwAGEh4ejRYsWsLW1RXBwMNq1a4dSpUoBeLvLc9mJjY1F/vz5AQBmZmaoUKECIiMj0bx5cwBARkYGIiMj0atXryzPrV+/PjZs2ICmTZtCRDBz5sx3PGPdMWkiIiKibFWpUgVVqlTBjBkzsG7dOixevBg//fQTjh8/jjJlyrzV5bnp06fD09MTpUqVwvPnzzF//nzs3LkT27ZtU+oMGDAA7du3h5+fHypVqoTp06fjyZMn6NixY7bHDAgIwMaNG9GkSRNkZGTg559/fu9z/idMmoiIiOgfWVhYoE2bNmjTpg1u374NGxubtz7Gixcv8N133+HWrVuwsrJC2bJlsWPHDq0JL7/88kvcu3cPw4cPR0JCAsqVK4eIiIgsg8NfV6dOHWzatAmNGzeGiODnn3+GRqN5p/P8NxoREVWO/IlJSUmBvb09kpOTYWdnZ+jmEBF9/Ebav8dzk/XXjmw8f/4cV69ehaenJywsLFSNRf/un34fb/P5zYHgRERERDpg0kRERESkAyZNRERERDpg0kRERESkAyZNRERERDpg0kRERESkAyZNRERERDpg0kRERESkAyZNRERERDpg0kRERESkA649R0RElJPeZ/mXd4qn3pIxHh4euH79ulbZ+PHjMWTIEOXxyZMn0bNnTxw5cgT58uVD7969MWjQoP9r3siRWLduHWJjY5WyvXv3okmTJujQoQOmTZum2lpyb4tJExERESlu374NZ2dnmJjoliKMHj0aXbt2VR7b2toqP6ekpKB+/foICAhAWFgYTp06hU6dOsHBwQHdunXL9nibNm1Cq1atMGTIEAwfPvz9TkbPeHmOiIiIFPPmzUPBggXx/fff49SpU/9a39bWFq6urspmbW2t7Fu2bBlevHiBhQsXolSpUmjTpg369OmDqVOnZnus5cuXo0WLFpg0aVKuS5gAJk1ERET0msGDB2PGjBk4e/Ysypcvj/Lly2PmzJm4d+9etvUnTJgAJycn+Pr6YvLkyXj58qWyLzo6GjVq1ICZmZlSFhgYiPPnz+Phw4dax5k9ezY6duyIhQsXolevXuqc3Hti0kREREQKCwsLfPnll9i0aRNu3bqFkJAQLF68GAUKFEDz5s2xdu1aJTHq06cPVq5ciV27duGbb77BuHHjtMYrJSQkwMXFRev4mY8TEhKUsrNnz6JXr16YM2cOgoODc+As3w2TJiIiIsqWs7Mz+vXrh2PHjuHPP/9EdHQ0WrRogdOnTwMABgwYgFq1aqFs2bLo3r07pkyZglmzZiE1NfWt4hQsWBDly5fH5MmTcefOHTVORS+YNBEREVG2Hj16hEWLFqFOnTpo0qQJSpcujfDwcPj4+GRbv3Llynj58iWuXbsGAHB1dcXdu3e16mQ+dnV1VcpsbW2xY8cOWFtbo3bt2rk2cWLSRERERIr09HRs2bIFX331FVxcXDBhwgTUrVsXV65cQWRkJEJCQrTGKL0uNjYWRkZGcHZ2BgD4+/sjKioKaWlpSp3t27ejePHicHR01Hquo6MjduzYATs7O9SqVQu3b99W7yTfEZMmIiIiUowbNw5t27ZVen/Onz+PH374AYUKFdKqFx0djenTp+PEiRO4cuUKli1bhv79++Prr79WEqKvvvoKZmZm6Ny5M+Li4rBq1SrMmDEDAwYMyDa2g4MDtm/fDkdHx1yZOHGeJiIiIlK0a9cOAwcOhIWFxT/WMzc3x8qVKzFy5EikpqbC09MT/fv310qI7O3tsW3bNvTs2RMVKlRA3rx5MXz48DfO0fT6cxo0aICaNWti9+7dKFCggN7O731oREQM3YiPQUpKCuzt7ZGcnAw7OztDN4eI6OP3PjNrqzhLNgA8f/4cV69ehaen578mH6S+f/p9vM3nNy/PEREREemASRMRERGRDpg0EREREemASRMRERGRDpg0ERERqYT3WuUO+vo9MGkiIiLSM1NTUwDA06dPDdwSAv7v95D5e3lXnKeJiIhIz4yNjeHg4IDExEQAgJWVFTQajYFb9ekRETx9+hSJiYlwcHCAsbHxex2PSRMREZEKMtdWy0ycyHAcHBy01rp7V0yaiIiIVKDRaJA/f344Oztrrb1GOcvU1PS9e5gyMWkiIqIPksfz5e/83Gv6a8a/MjY21tuHNhkWB4ITERER6YBJExEREZEOcnXSlJ6ejtDQUHh6esLS0hLe3t4YM2aM1nwLIoLhw4cjf/78sLS0REBAAC5evKh1nAcPHiA4OBh2dnZwcHBA586d8fjxY606J0+eRPXq1WFhYQF3d3dMmjQpR86RiIiIPgy5OmmaOHEi5syZg59//hlnz57FxIkTMWnSJMyaNUupM2nSJMycORNhYWE4dOgQrK2tERgYiOfPnyt1goODERcXh+3bt2Pjxo2IiopCt27dlP0pKSmoX78+ChcujJiYGEyePBkjR47E3Llzc/R8iYiIKPfSSC6errRx48ZwcXHBggULlLKWLVvC0tIS//vf/yAicHNzw3fffYfvv/8eAJCcnAwXFxcsXrwYbdq0wdmzZ+Hj44MjR47Az88PABAREYFGjRrh5s2bcHNzw5w5c/DDDz8gISEBZmZmAIAhQ4Zg3bp1OHfunE5tTUlJgb29PZKTk2FnZ6fnV4KIiP7OY8imd37utQlBemwJfcje5vM7V/c0ValSBZGRkbhw4QIA4MSJE9i3bx8aNmwIALh69SoSEhIQEBCgPMfe3h6VK1dGdHQ0ACA6OhoODg5KwgQAAQEBMDIywqFDh5Q6NWrUUBImAAgMDMT58+fx8OFD1c+TiIiIcr9cPeXAkCFDkJKSghIlSsDY2Bjp6en48ccfERwcDABISEgAALi4uGg9z8XFRdmXkJAAZ2dnrf0mJibIkyePVh1PT88sx8jc5+jomKVtqampSE1NVR6npKS8z6kSERFRLpere5pWr16NZcuWYfny5Th27BjCw8Px008/ITw83NBNw/jx42Fvb69s7u7uhm4SERERqShXJ00DBw7EkCFD0KZNG5QpUwbt2rVD//79MX78eAD/N0X93bt3tZ539+5dZZ+rq2uWKexfvnyJBw8eaNXJ7hivx/i7oUOHIjk5Wdni4+Pf82yJiIgoN8vVSdPTp09hZKTdRGNjY2RkZAAAPD094erqisjISGV/SkoKDh06BH9/fwCAv78/kpKSEBMTo9TZuXMnMjIyULlyZaVOVFSU1jT327dvR/HixbO9NAcA5ubmsLOz09qIiIjo45Wrk6YmTZrgxx9/xKZNm3Dt2jWsXbsWU6dOxRdffAHg1bo+/fr1w9ixY7F+/XqcOnUKISEhcHNzQ/PmzQEAJUuWRIMGDdC1a1ccPnwY+/fvR69evdCmTRu4ubkBAL766iuYmZmhc+fOiIuLw6pVqzBjxgwMGDDAUKdOREREuUyuHgg+a9YshIaGokePHkhMTISbmxu++eYbDB8+XKkzaNAgPHnyBN26dUNSUhKqVauGiIgIWFhYKHWWLVuGXr16oW7dujAyMkLLli0xc+ZMZb+9vT22bduGnj17okKFCsibNy+GDx+uNZcTERERfdpy9TxNHxLO00RElLM4TxPpw0czTxMRERFRbsGkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdMCkiYiIiEgHTJqIiIiIdGBi6AYQERER/auR9u/x3GS9NIE9TUREREQ6YNJEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ6yPVJ061bt/D111/DyckJlpaWKFOmDI4eParsFxEMHz4c+fPnh6WlJQICAnDx4kWtYzx48ADBwcGws7ODg4MDOnfujMePH2vVOXnyJKpXrw4LCwu4u7tj0qRJOXJ+RERE9GHI1UnTw4cPUbVqVZiammLLli04c+YMpkyZAkdHR6XOpEmTMHPmTISFheHQoUOwtrZGYGAgnj9/rtQJDg5GXFwctm/fjo0bNyIqKgrdunVT9qekpKB+/fooXLgwYmJiMHnyZIwcORJz587N0fMlIiKi3MvE0A34JxMnToS7uzsWLVqklHl6eio/iwimT5+OYcOGoVmzZgCAJUuWwMXFBevWrUObNm1w9uxZRERE4MiRI/Dz8wMAzJo1C40aNcJPP/0ENzc3LFu2DC9evMDChQthZmaGUqVKITY2FlOnTtVKroiIiOjTlat7mtavXw8/Pz+0atUKzs7O8PX1xbx585T9V69eRUJCAgICApQye3t7VK5cGdHR0QCA6OhoODg4KAkTAAQEBMDIyAiHDh1S6tSoUQNmZmZKncDAQJw/fx4PHz7Mtm2pqalISUnR2oiIiOjjlauTpitXrmDOnDkoWrQotm7dim+//RZ9+vRBeHg4ACAhIQEA4OLiovU8FxcXZV9CQgKcnZ219puYmCBPnjxadbI7xusx/m78+PGwt7dXNnd39/c8WyIiIsrNcnXSlJGRgfLly2PcuHHw9fVFt27d0LVrV4SFhRm6aRg6dCiSk5OVLT4+3tBNIiIiIhXl6qQpf/788PHx0SorWbIkbty4AQBwdXUFANy9e1erzt27d5V9rq6uSExM1Nr/8uVLPHjwQKtOdsd4PcbfmZubw87OTmsjIiKij1euTpqqVq2K8+fPa5VduHABhQsXBvBqULirqysiIyOV/SkpKTh06BD8/f0BAP7+/khKSkJMTIxSZ+fOncjIyEDlypWVOlFRUUhLS1PqbN++HcWLF9e6U4+IiIg+Xbk6aerfvz8OHjyIcePG4dKlS1i+fDnmzp2Lnj17AgA0Gg369euHsWPHYv369Th16hRCQkLg5uaG5s2bA3jVM9WgQQN07doVhw8fxv79+9GrVy+0adMGbm5uAICvvvoKZmZm6Ny5M+Li4rBq1SrMmDEDAwYMMNSpExERUS6Tq6ccqFixItauXYuhQ4di9OjR8PT0xPTp0xEcHKzUGTRoEJ48eYJu3bohKSkJ1apVQ0REBCwsLJQ6y5YtQ69evVC3bl0YGRmhZcuWmDlzprLf3t4e27ZtQ8+ePVGhQgXkzZsXw4cP53QDREREpNCIiBi6ER+DlJQU2NvbIzk5meObiIhygMeQTe/83GsTgvTYEsoRI+3f47nJb9z1Np/fufryHBEREVFuwaSJiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh08E5Jk5eXF+7fv5+lPCkpCV5eXu/dKCIiIqLc5p2SpmvXriE9PT1LeWpqKm7duvXejSIiIiLKbd5qcsv169crP2/duhX29v83Z0J6ejoiIyPh4eGht8YRERER5RZvlTRlLk2i0WjQvn17rX2mpqbw8PDAlClT9NY4IiIiotzirZKmjIwMAK8Wyj1y5Ajy5s2rSqOIiIiIcpt3Wnvu6tWr+m4HERERUa72zgv2RkZGIjIyEomJiUoPVKaFCxe+d8OIiIiIcpN3SppGjRqF0aNHw8/PD/nz54dGo9F3u4iIiIhylXdKmsLCwrB48WK0a9dO3+0hIiIiypXeaZ6mFy9eoEqVKvpuCxEREVGu9U5JU5cuXbB8+XJ9t4WIiIgo13qny3PPnz/H3LlzsWPHDpQtWxampqZa+6dOnaqXxhERERHlFu+UNJ08eRLlypUDAJw+fVprHweFExERkb55PH/3K1zX9NSGd0qadu3apafwRERERB+GdxrTRERERPSpeaeeptq1a//jZbidO3e+c4OIiIiIcqN3SpoyxzNlSktLQ2xsLE6fPp1lIV8iIiKij8E7JU3Tpk3LtnzkyJF4/PjxezWIiIiIKDfS65imr7/+muvOERER0UdJr0lTdHQ0LCws9HlIIiIiolzhnS7PtWjRQuuxiODOnTs4evQoQkND9dIwIiIiotzknZIme3t7rcdGRkYoXrw4Ro8ejfr16+ulYURERES5yTslTYsWLdJ3O4iI6F2NtP/3Om98brL+2kH0kXunpClTTEwMzp49CwAoVaoUfH199dIoIiIiotzmnZKmxMREtGnTBrt374aDgwMAICkpCbVr18bKlSuRL18+fbaRiIiIyODe6e653r1749GjR4iLi8ODBw/w4MEDnD59GikpKejTp4++20hERERkcO/U0xQREYEdO3agZMmSSpmPjw9mz57NgeBERET0UXqnnqaMjAyYmppmKTc1NUVGRsZ7N4qIiIgot3mnpKlOnTro27cvbt++rZTdunUL/fv3R926dfXWOCIiIqLc4p2Spp9//hkpKSnw8PCAt7c3vL294enpiZSUFMyaNUvfbSQiIiIyuHca0+Tu7o5jx45hx44dOHfuHACgZMmSCAgI0GvjiIiIiHKLt+pp2rlzJ3x8fJCSkgKNRoN69eqhd+/e6N27NypWrIhSpUph7969arWViIiIyGDeKmmaPn06unbtCjs7uyz77O3t8c0332Dq1Kl6axwRERFRbvFWSdOJEyfQoEGDN+6vX78+YmJi3rtRRERERLnNWyVNd+/ezXaqgUwmJia4d+/eezeKiIiIKLd5q6SpQIECOH369Bv3nzx5Evnz53/vRhERERHlNm+VNDVq1AihoaF4/vx5ln3Pnj3DiBEj0LhxY701joiIiCi3eKspB4YNG4Y1a9agWLFi6NWrF4oXLw4AOHfuHGbPno309HT88MMPqjSUiIiIyJDeKmlycXHBgQMH8O2332Lo0KEQEQCARqNBYGAgZs+eDRcXF1UaSkRERGRIbz25ZeHChbF582Y8fPgQly5dgoigaNGicHR0VKN9RERERLnCO80IDgCOjo6oWLGiPttCRERElGu909pzRERERJ8aJk1EREREOmDSRERERKQDJk1EREREOmDSRERERKQDJk1EREREOmDSRERERKQDJk1EREREOmDSRERERKQDJk1EREREOmDSRERERKQDJk1EREREOmDSRERERKQDJk1EREREOvigkqYJEyZAo9GgX79+Stnz58/Rs2dPODk5wcbGBi1btsTdu3e1nnfjxg0EBQXBysoKzs7OGDhwIF6+fKlVZ/fu3ShfvjzMzc1RpEgRLF68OAfOiIiIiD4UH0zSdOTIEfz6668oW7asVnn//v2xYcMG/Pbbb9izZw9u376NFi1aKPvT09MRFBSEFy9e4MCBAwgPD8fixYsxfPhwpc7Vq1cRFBSE2rVrIzY2Fv369UOXLl2wdevWHDs/IiIiyt0+iKTp8ePHCA4Oxrx58+Do6KiUJycnY8GCBZg6dSrq1KmDChUqYNGiRThw4AAOHjwIANi2bRvOnDmD//3vfyhXrhwaNmyIMWPGYPbs2Xjx4gUAICwsDJ6enpgyZQpKliyJXr164T//+Q+mTZtmkPMlIiKi3OeDSJp69uyJoKAgBAQEaJXHxMQgLS1Nq7xEiRIoVKgQoqOjAQDR0dEoU6YMXFxclDqBgYFISUlBXFycUufvxw4MDFSOQURERGRi6Ab8m5UrV+LYsWM4cuRIln0JCQkwMzODg4ODVrmLiwsSEhKUOq8nTJn7M/f9U52UlBQ8e/YMlpaWWWKnpqYiNTVVeZySkvL2J0dEREQfjFzd0xQfH4++ffti2bJlsLCwMHRztIwfPx729vbK5u7ubugmERERkYpyddIUExODxMRElC9fHiYmJjAxMcGePXswc+ZMmJiYwMXFBS9evEBSUpLW8+7evQtXV1cAgKura5a76TIf/1sdOzu7bHuZAGDo0KFITk5Wtvj4eH2cMhEREeVSuTppqlu3Lk6dOoXY2Fhl8/PzQ3BwsPKzqakpIiMjleecP38eN27cgL+/PwDA398fp06dQmJiolJn+/btsLOzg4+Pj1Ln9WNk1sk8RnbMzc1hZ2entREREdHHK1ePabK1tUXp0qW1yqytreHk5KSUd+7cGQMGDECePHlgZ2eH3r17w9/fH59//jkAoH79+vDx8UG7du0wadIkJCQkYNiwYejZsyfMzc0BAN27d8fPP/+MQYMGoVOnTti5cydWr16NTZs25ewJExERUa6Vq5MmXUybNg1GRkZo2bIlUlNTERgYiF9++UXZb2xsjI0bN+Lbb7+Fv78/rK2t0b59e4wePVqp4+npiU2bNqF///6YMWMGChYsiPnz5yMwMNAQp0RERES50AeXNO3evVvrsYWFBWbPno3Zs2e/8TmFCxfG5s2b//G4tWrVwvHjx/XRRCIiIvoI5eoxTURERES5BZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh2YGLoBRET0fjyeL3/n517TXzOIPnrsaSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh0waSIiIiLSAZMmIiIiIh3k6qRp/PjxqFixImxtbeHs7IzmzZvj/PnzWnWeP3+Onj17wsnJCTY2NmjZsiXu3r2rVefGjRsICgqClZUVnJ2dMXDgQLx8+VKrzu7du1G+fHmYm5ujSJEiWLx4sdqnR0RERB+QXJ007dmzBz179sTBgwexfft2pKWloX79+njy5IlSp3///tiwYQN+++037NmzB7dv30aLFi2U/enp6QgKCsKLFy9w4MABhIeHY/HixRg+fLhS5+rVqwgKCkLt2rURGxuLfv36oUuXLti6dWuOni8RERHlXiaGbsA/iYiI0Hq8ePFiODs7IyYmBjVq1EBycjIWLFiA5cuXo06dOgCARYsWoWTJkjh48CA+//xzbNu2DWfOnMGOHTvg4uKCcuXKYcyYMRg8eDBGjhwJMzMzhIWFwdPTE1OmTAEAlCxZEvv27cO0adMQGBiY4+dNREREuU+u7mn6u+TkZABAnjx5AAAxMTFIS0tDQECAUqdEiRIoVKgQoqOjAQDR0dEoU6YMXFxclDqBgYFISUlBXFycUuf1Y2TWyTxGdlJTU5GSkqK1ERER0cfrg0maMjIy0K9fP1StWhWlS5cGACQkJMDMzAwODg5adV1cXJCQkKDUeT1hytyfue+f6qSkpODZs2fZtmf8+PGwt7dXNnd39/c+RyIiIsq9PpikqWfPnjh9+jRWrlxp6KYAAIYOHYrk5GRli4+PN3STiIiISEW5ekxTpl69emHjxo2IiopCwYIFlXJXV1e8ePECSUlJWr1Nd+/ehaurq1Ln8OHDWsfLvLvu9Tp/v+Pu7t27sLOzg6WlZbZtMjc3h7m5+XufGxEREX0YcnVPk4igV69eWLt2LXbu3AlPT0+t/RUqVICpqSkiIyOVsvPnz+PGjRvw9/cHAPj7++PUqVNITExU6mzfvh12dnbw8fFR6rx+jMw6mccgIiIiytU9TT179sTy5cvx559/wtbWVhmDZG9vD0tLS9jb26Nz584YMGAA8uTJAzs7O/Tu3Rv+/v74/PPPAQD169eHj48P2rVrh0mTJiEhIQHDhg1Dz549lZ6i7t274+eff8agQYPQqVMn7Ny5E6tXr8amTZsMdu5ERESUu+TqnqY5c+YgOTkZtWrVQv78+ZVt1apVSp1p06ahcePGaNmyJWrUqAFXV1esWbNG2W9sbIyNGzfC2NgY/v7++PrrrxESEoLRo0crdTw9PbFp0yZs374dn332GaZMmYL58+dzugEiIiJS5OqeJhH51zoWFhaYPXs2Zs+e/cY6hQsXxubNm//xOLVq1cLx48ffuo1ERET0acjVPU1EREREuQWTJiIiIiIdMGkiIiIi0gGTJiIiIiIdMGkiIiIi0gGTJiIiIiIdMGkiIiIi0kGunqeJiIgoVxpp/x7PTdZfOyhHsaeJiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh0wGVUiIj0gctqEH302NNEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ6YNJEREREpAMmTUREREQ64OSWnwpOvEdERPRe2NNEREREpAP2NH0iPJ4vf+fnXtNfM4jUxR5VIlIRkyYiIj3gFxOijx8vzxERERHpgEkTERERkQ6YNBERERHpgEkTERERkQ6YNBERERHpgHfP5STeDk1ERPTBYk8TERERkQ7Y05SDOI8Lkbr4N0ZEamLSRERE9JaYoH+aeHmOiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh0wKSJiIiISAe8e47oY8SJVIlIDZ/4/xYmTUQfId4OTUSkf7w8R0RERKQDJk1EREREOuDlOSLSK48hm975udcmBOmxJURE+sWeJiIiIiIdsKeJ1PeJ321B9FHj3zd9Qpg0kep4JxcREX0MeHmOiIiISAdMmoiIiIh0wMtzRCrhXWRERB8X9jQRERER6YBJExEREZEOmDQRERER6YBjmujjxjlkiIhIT5g0/c3s2bMxefJkJCQk4LPPPsOsWbNQqVIlQzeL3hHniCIi0p9P/X8qk6bXrFq1CgMGDEBYWBgqV66M6dOnIzAwEOfPn4ezs7Ohm0dElOt86h+i9GnhmKbXTJ06FV27dkXHjh3h4+ODsLAwWFlZYeHChYZuGhERERkYe5r+vxcvXiAmJgZDhw5VyoyMjBAQEIDo6GgDtoyIiOj/cA44w2HS9P/99ddfSE9Ph4uLi1a5i4sLzp07l6V+amoqUlNTlcfJya8GDaekpLwxRkbq03du3z8dVxeMzdiMzdiMzdiM/eZ9IvLvBxISEZFbt24JADlw4IBW+cCBA6VSpUpZ6o8YMUIAcOPGjRs3btw+gi0+Pv5fcwX2NP1/efPmhbGxMe7evatVfvfuXbi6umapP3ToUAwYMEB5nJGRgQcPHsDJyQkajeat46ekpMDd3R3x8fGws7N7+xN4D4zN2IzN2IzN2J9qbBHBo0eP4Obm9q91mTT9f2ZmZqhQoQIiIyPRvHlzAK8SocjISPTq1StLfXNzc5ibm2uVOTg4vHc77OzscvzNxtiMzdiMzdiM/SnHtre316kek6bXDBgwAO3bt4efnx8qVaqE6dOn48mTJ+jYsaOhm0ZEREQGxqTpNV9++SXu3buH4cOHIyEhAeXKlUNERESWweFERET06WHS9De9evXK9nKc2szNzTFixIgsl/wYm7EZm7EZm7EZO3fE1ojoco8dERER0aeNM4ITERER6YBJExEREZEOmDQRERER6YBJExEREZEOmDQRERER6YBTDhDRRy0xMRGJiYnIyMjQKi9btqxqMfv06YMiRYqgT58+WuU///wzLl26hOnTp6sWm+hT8+LFC1y9ehXe3t4wMVE3reGUA/TJycjIwKVLl7L9IK1Ro4aBWvXxunv3Lr7//ntERkYiMTExy0ri6enpqsSNiYlB+/btcfbsWSWmRqOBiECj0agWFwAKFCiA9evXo0KFClrlx44dQ9OmTXHz5k3VYmfGMTU1RZkyZQAAf/75JxYtWgQfHx+MHDkSZmZmqsZPSkrC77//jsuXL2PgwIHIkycPjh07BhcXFxQoUEDV2IYSHh6OvHnzIigoCAAwaNAgzJ07Fz4+PlixYgUKFy6s13gnT57Uua6+vyCsX79e57pNmzbVa+zXPX36FL1790Z4eDgA4MKFC/Dy8kLv3r1RoEABDBkyRO8xmTQZUHp6OhYvXqx8mPz9A3znzp2qxX7y5AkmTJjwxthXrlxRLbYhz/vgwYP46quvcP369Swf3mp/kBryNTdk7IYNG+LGjRvo1asX8ufPn2VB62bNmqkS97PPPoO3tzcGDx4MFxeXLHH1/SH2OgsLC5w+fRpFihTRKr906RJKly6N58+fqxYbACpWrIghQ4agZcuWuHLlCkqVKoUvvvgCR44cQVBQkKo9XSdPnkRAQADs7e1x7do1nD9/Hl5eXhg2bBhu3LiBJUuW6DWer6+vzoukHzt2TK+xX1e8eHHMmTMHderUQXR0NAICAjBt2jRs3LgRJiYmWLNmjV7jGRkZKV8CsqPmFwQjI91G9qj9P7Vv377Yv38/pk+fjgYNGuDkyZPw8vLCn3/+iZEjR+L48eN6j8nLcwbUt29fLF68GEFBQShdurTOf/j60KVLF+zZswft2rXL9oNMTYY87+7du8PPzw+bNm3K8fM25GtuyNj79u3D3r17Ua5cuRyLCbxKBP/4448siUtOKFKkCCIiIrKsLrBlyxZ4eXmpHv/ChQvK6/3bb7+hRo0aWL58Ofbv3482bdqomjQNGDAAHTp0wKRJk2Bra6uUN2rUCF999ZXe42UusA4Az58/xy+//AIfHx/4+/sDePVFKS4uDj169NB77NfFx8cr77V169ahZcuW6NatG6pWrYpatWrpPd7Vq1f1fkxd/f1Ll6GsW7cOq1atwueff671P61UqVK4fPmyOkGFDMbJyUk2bdpkkNj29vayb98+g8Q25HlbWVnJxYsXDRLbkK+5IWOXLFlSjh07luNxmzVrJr///nuOxxURWbBggVhaWsrw4cNl9+7dsnv3bgkNDRUrKyuZO3eu6vFtbW3lwoULIiISEBAg06dPFxGR69evi4WFhaqx7ezs5NKlSyIiYmNjI5cvXxYRkWvXrom5ubmqsTt37izDhg3LUj58+HDp2LGjqrHz5cunvM/LlSsnS5YsERGRS5cuibW1taqxP1WWlpbK++v191psbKzY2dmpEpM9TQZkZmZmkG/BAODo6Ig8efIYJLYhz7ty5cq4dOmSQeIb8jU3ZOzp06djyJAh+PXXX+Hh4ZFjcefPn4/27dvj9OnTKF26NExNTbX2qznWolOnTkhNTcWPP/6IMWPGAAA8PDwwZ84chISEqBY3k5+fH8aOHYuAgADs2bMHc+bMAfCqd0LtBcjNzc2RkpKSpfzChQvIly+fqrF/++03HD16NEv5119/DT8/PyxcuFC12PXq1UOXLl3g6+uLCxcuoFGjRgCAuLi4HHnfX758GdOnT8fZs2cBAD4+Pujbty+8vb1Vj71nzx789NNPWrEHDhyI6tWrqxo386pB7969AUDpbZo/f77S06h3qqRipJOffvpJevToIRkZGTkee+nSpfKf//xHnjx5kuOxc/q8T5w4oWxr1qwRHx8fWbRokRw9elRr34kTJ1RthyFf85yO7eDgII6OjspmZmYmRkZGYmNjo1Xu6OioWhvWr18v9vb2otFosmxGRkaqxf27xMREefToUY7FE3n1ni9durTY2dnJyJEjlfJevXpJ27ZtVY3duXNnad68ubx48UJsbGzkypUrcv36dfH19ZW+ffuqGtvFxUUWLVqUpXzRokXi7OysauyHDx9Kr169pGnTprJlyxalfPjw4TJ27FhVY0dERIiZmZlUqlRJ+vfvL/3795dKlSqJubm5bNu2TdXYS5cuFRMTE2ndurXMmDFDZsyYIa1btxZTU1NZtmyZqrH37t0rNjY20r17d7GwsJC+fftKvXr1xNraWo4ePapKTA4Ez2EtWrTQerxz507kyZMHpUqVyvJNWN8DB/8+YPLSpUsQEXh4eGSJre8Bk4Y8b0MOmDTka27I2Jl3s+iiffv2eo2dycPDA40bN0ZoaKjqvStvcu/ePZw/fx4AUKJECeTNm1f1mOnp6di/fz/KlCkDR0dHrX3Pnz+HsbFxlt+/PiUnJ+M///kPjh49ikePHsHNzQ0JCQnw9/fH5s2bYW1trVrsCRMmYNSoUejatSsqVaoEADh06BAWLlyI0NBQVe6mAoCXL19i3Lhx6NSpEwoWLKhKjH/i6+uLwMBATJgwQat8yJAh2LZtm6oD4EuWLIlu3bqhf//+WuVTp07FvHnzlN4ntVy+fBkTJkzAiRMn8PjxY5QvXx6DBw9W7hzVNyZNOaxjx4461120aJFeY48aNUrnuiNGjNBrbEOe9/Xr13Wuq+87qgz5mhsydm5ga2uL2NjYHLk88XdPnjxB7969sWTJEmXQrLGxMUJCQjBr1ixYWVmpGt/CwgJnz56Fp6enqnH+yf79+7U+yAICAnIk7urVqzFjxgzlw7pkyZLo27cvWrdurWpcGxsbnD59OkcvQWeysLDAqVOnULRoUa3yCxcuoGzZsqrerWlubo64uDiD3Sma41TpvyLKpfbs2SNpaWlZytPS0mTPnj0GaNHHz8jISO7evZul/K+//lL1MllISIjMmzdPteP/k27duomXl5ds3rxZkpOTJTk5WTZt2iTe3t7SvXt31eNXqFBBduzYoXqc7ISHh8vz58+zlKempkp4eLgBWpQzmjZtKosXLzZI7IIFC8rq1auzlK9atUrc3d1Vje3t7S1hYWFZyufMmSNFihRRNXbm39bft5SUFElNTVUlJpMmA6pdu7Y8fPgwS3lycrLUrl1b1dienp7y119/ZSl/+PCheHp6qhrbkOdtqA9wEcO+5oaMrdFosn3Nb926peqdXGPHjpW8efNK+/bt5aefflLGW2RuanJycpJdu3ZlKd+5c6fkzZtX1dgiIlu2bJFy5crJhg0b5Pbt21k+VNRkyL8xQ5ozZ464urrKd999J8uXL5c///xTa1PTqFGjxMHBQSZMmCBRUVESFRUl48ePFwcHBxk9erSqsX/55RcxMzOT7t27y5IlS2TJkiXyzTffiLm5ebbJlD5ljk9801aoUCEZPny4pKen6y+mCC/PGYqRkRESEhLg7OysVZ6YmIgCBQogLS0tx2PfvXsX7u7uePHiRY7Hzqnzvnv3bpa7eC5cuAA/P79s7/rRZ+zc9pqrGXvmzJkAgP79+2PMmDGwsbFR9qWnpyMqKgrXrl1TZQI6AP94aUqj0ag6oaeVlRViYmJQsmRJrfK4uDhUqlQJT548US02oD354Ovj2iQHZkN/09/YiRMnULt2bTx48EC12Onp6Zg2bRpWr16NGzduZHlfqxn7nyZ8VPs1FxFMnz4dU6ZMwe3btwEAbm5uGDhwIPr06aP6vGxr167FlClTtC6JDhw4ULWJazMtWbIEP/zwAzp06KCMYTt8+DDCw8MxbNgw3Lt3Dz/99BMGDhyI//73v3qJySkHDOD16e/PnDmDhIQE5XF6ejoiIiJUW2rg9envt27dCnt7e63YkZGRqo2DMOR5Zw5E12g06NChA8zNzbVinzx5ElWqVFEltiFfc0PGnjZtGoBX/9DDwsJgbGys7DMzM4OHhwfCwsJUiQ0YdvI/f39/jBgxAkuWLIGFhQUA4NmzZxg1apR6t0K/ZteuXarH+LvMGw80Gg3q1q2rtQZYeno6rl69igYNGqjahlGjRmH+/Pn47rvvMGzYMPzwww+4du0a1q1bh+HDh6sa25ATPmo0GvTv3x/9+/fHo0ePAEBrYlG1ffHFF/jiiy9yLF6m8PBwTJkyRWu8WpMmTVCmTBn8+uuviIyMRKFChfDjjz/qLWliT5MBZN7NBSDbO7osLS0xa9YsdOrUSZXYALK9m8zU1BQeHh6YMmUKGjdurEpsQ5135kD08PBwtG7dGpaWlsq+zA/wrl27qnJ3k6Ffc0PFzlS7dm2sXbsWDg4OqsXIbU6fPo3AwECkpqbis88+A/Cqp8XCwgJbt25FqVKlDNxC/cu88WDUqFH47rvvtHoWM//GWrZsqeq6d97e3pg5cyaCgoK0bgSYOXMmDh48iOXLl6sW+3XPnz9XkuWcZIi7NTPFxMQoPU2lSpWCr6+v6jEtLS1x8uTJLAPgL168iM8++wxPnz7F1atXUapUKTx9+lQvMdnTZABXr16FiMDLywuHDx/W6sY2MzODs7Oz1rdyfcr8NuTp6YkjR47k6B+VIc878448Dw8PfP/996re9vx3hnzNDRkbAO7cuYNq1aqhRYsWuHPnDoyMjODl5YXmzZujQ4cOev99DxgwQOe6U6dO1Wvs15UuXRoXL17EsmXLcO7cOQBA27ZtERwcrJWwqykpKQkLFizQ+iDr1KmTVm+jPmXegenh4YEvv/zSIElDQkKCcqu5jY0NkpOTAUCZekJN6enpGDduHMLCwnD37l1l8djQ0FB4eHigc+fOqsU25N2aiYmJaNOmDXbv3q18MUpKSkLt2rWxcuVKVSc0dXd3x4IFC7JMtbBgwQK4u7sDAO7fv59l6o33orfRUUQfoEWLFklSUpKhm/FROnLkiNjb20uFChWkWrVqYmxsLO3atZMvv/xSHBwcpEqVKpKSkqLXmLVq1dLa7OzsxMrKSnx9fcXX11esra3Fzs5O9RsOnj17purx/82RI0ckT548UqBAAfniiy/kiy++kIIFC4qTk5PExMQYtG1qKlasmBw8eFBERKpWrSrjx48XEZGVK1dKvnz5VI09atQo8fLykv/9739ay3usXLlSPv/8c1VjG/JuzdatW4ufn5+cOXNGKYuLixM/Pz9p06aNqrH//PNPMTMzk7Jly0rnzp2lc+fO8tlnn4m5ubls2LBBRF4NVO/fv7/eYjJpMpDU1FRZtWqV9OvXT9q0aSNt2rSRfv36yerVq1W7VVIXCQkJMmrUKFVjxMfHZztD8osXL3L8tn9TU1OtP3Y1xMfHy71795THUVFR8tVXX0m1atUkODhYDhw4oGr8DRs2SGhoqLL2XGRkpDRs2FACAwPl119/VS1u1apVtWajXrp0qVSuXFlERB48eCDlypWTPn36qBZ/ypQp0qRJE3nw4IFS9uDBA2nWrJn89NNPqsUVebX2W0hIiGzbtk2vd+7oqlq1atKhQwet6TXS0tKkffv2Ur16dVVjv3z5UiZPniwVK1YUFxeXHJsBXkRk8ODB8uOPP4rIq2TFxMREihQpImZmZjJ48GBVY3t7eyvTPLy+DtrZs2fFwcFB1diGvFvTzs5ODh8+nKX80KFDYm9vr2psEZErV67I4MGDlS8HQ4YMkatXr6oWj0mTAVy8eFG8vLzEwsJCatasKa1bt5bWrVtLzZo1xcLCQooUKWKwRWVjY2NVuy349u3bUrFiRdFoNEqvw+vJU0JCgmqx//6PO3PTaDRib2+v6j/0SpUqKd961q1bJ0ZGRtK0aVPlD93U1FTZr29hYWFiYmIiFSpUEDs7O1m6dKnY2tpKly5d5JtvvhFLS0tlMVd9e/3btohIenq6mJqaSkJCgoiIbNu2Tdzc3FSJLSLi5uYmp0+fzlJ+6tQpyZ8/v2pxRUTWrFkj//nPf8TS0lJcXV2lb9++cuTIEVVjvs7CwkLOnj2bpTwuLk4sLS1VjR0aGir58+eXn376SSwsLGTMmDHSuXNncXJyUn2qh7+Ljo6WKVOmyPr161WPZWFhIdeuXRMR7aQpLi5O9QV7LS0ts/3yd/r0abGyslI1to2NjRw/fjxL+bFjx8TW1lbV2IbApMkAAgICpFmzZtnOl5KcnCzNmjWT+vXrqxL772ut/X1btWqVaolLSEiIVK5cWY4cOSLbt2+XChUqiJ+fn9ITkJCQIBqNRpXYNjY2EhQUJIsXL1a2RYsWibGxsfz4449KmRqsra3lypUrIiJSuXJlmTBhgtb+WbNmia+vryqxfXx8ZO7cuSLy6lunhYWFzJ49W9m/aNEiKVmypCqxCxcurPRuibxKmjUajTx9+lRERK5evarqPE02NjZv/PZtY2OjWtzXpaSkyMKFC6VevXpibGwsRYsWVb0nV0TE2dlZtm7dmqU8IiJC9TXYvLy8ZOPGjSLy6ndw6dIlERGZMWOGquvevXjxQjp27Kj8reW08uXLy9KlS0VEO2kaNWqUVKtWTdXYderUkVatWmldFn769Km0atVK6tatq2rspk2bSo0aNeTWrVtK2c2bN6VmzZrSvHlzVWOLvOq5Dw4OFn9/f7l586aIiCxZskT27t2rSjwmTQZgaWkpp06deuP+kydPqvZtMHMysDctYqrmYqZubm5y6NAh5fHz58+lSZMmUq5cObl//76qPU0XL16UihUrSkhIiFbvlomJicTFxakSM5O9vb2yGLCzs3OWhYEvXbqk2rdBS0tLuX79uvLY1NRU67139epV1WL37dtXSpcuLVu2bJGdO3dK7dq1pVatWsr+iIgI8fb2ViW2iEi7du3Ew8ND/vjjD4mPj5f4+Hj5/fffxdPTU0JCQlSL+yZxcXFSrly5HJngsXfv3lKwYEFZuXKl3LhxQ27cuCErVqyQggULqr5orpWVlfKec3V1VcZQXb58Wezs7FSNbWdnZ7Ckad26dWJvby8TJkwQKysrmTx5snTp0kXMzMxUXzT31KlT4ubmJk5OTlKnTh2pU6eOODk5SYECBbLtbdWnGzduSLly5cTU1FS8vLzEy8tLTE1NxdfXV+Lj41WN/fvvv4ulpaV06dJFzM3NlUR11qxZ0rBhQ1ViMmkygPz58//j5Zj169erdvnAyclJFixYINeuXct227Rpk2r/1K2treXChQtaZWlpadK8eXMpW7asnDx5UtUPlLS0NBk0aJB4e3srPSA5kTQ1bdpUhgwZIiIigYGBWS5RzJs3T4oWLapK7IIFC0pUVJSIvJqBW6PRyKZNm5T9u3fvloIFC6oS+9GjR9K6dWsxMTERjUYjVapU0fpA27p1a7ZLP+jLkydP5NtvvxVzc3NlhmAzMzP59ttv5fHjx6rFfd2zZ89k1apV0qxZMzE3N5dChQqpPrZG5NWYyT59+oiZmZly7ubm5tKvX79slzjRJ0MOxg4JCZGpU6eqGuOfREVFSUBAgOTLl08sLS2latWq2fb4qeHJkycyd+5cGTBggAwYMEDmzZun9OqqLSMjQ7Zt2yYzZ86UmTNnyvbt23Mkbrly5ZSleV7v3Tt27Ji4uLioEpNJkwGEhoaKo6OjTJ06VU6cOCEJCQmSkJAgJ06ckKlTp0qePHlkxIgRqsSuX7++jBkz5o37Y2NjVbtEVqZMGfn999+zlGcmToUKFcqRb+GRkZFSqFAhGTp0qJiamqqeNJ05c0acnJwkJCRExowZIzY2NvL111/Ljz/+KCEhIWJubi6LFi1SJXbPnj2laNGiMnbsWKlUqZK0b99eSpQoIVu2bJGIiAgpU6aMdOrUSZXYmZ49e5btwP+c8vjxY+Xyc04lSxERERISEiJ2dnaSJ08e6datm0HWNnzy5ImcPHlSTp48KU+ePMmRmIYcjD1mzBhxcHCQli1byrhx43J06RzKeZaWlsqg79eTpsuXL4u5ubkqMTm5pYFMnDgRM2bMQEJCgtaEj66urujXrx8GDRqkSty1a9fiyZMn+Prrr7Pd//DhQ6xfvx7t27fXe+zBgwcjNjYWW7duzbLv5cuXaNmyJTZs2JAjM+vev38fXbt2xa5du3Dw4EEUL15c1XiXL1/GsGHDsGnTJjx+/BgAYGJigooVK2LgwIFo3ry5KnGfPHmC/v37Izo6GlWqVMGsWbMwc+ZM/PDDD0hLS0PNmjWxatWqLMurfAySk5ORnp6OPHnyaJU/ePAAJiYmsLOzUy22lZUVGjdujODgYDRq1AimpqaqxcrtDh48iAMHDqBo0aJo0qSJqrEMuXSOl5cXjhw5AicnJ63ypKQklC9fXtXYwKsJHXft2oXExMQs/0PVng09MjISkZGR2cZeuHChanG9vLwwd+5cBAQEwNbWFidOnICXlxeWLFmCCRMm4MyZM3qPyaTJwK5evaosJ+Lq6qrakha5wcuXL/H06dM3fli9fPkSt27dQuHChXO4ZTlHRJR/LHnz5jXYh+nz58+RlpaWo0st5LSGDRuiSZMm6NGjh1Z5WFgY1q9fj82bN6sW+9GjR8pre/PmTbi5uf3j2mT6kLlUkC7WrFmjShvS0tLwzTffIDQ09KP+X5adf1rfsVChQkhNTVUt9rx58/Dtt98ib968cHV11VprTqPR4NixY6rFHjVqFEaPHg0/Pz/kz58/yzp3a9euVS32+PHj8b///Q8LFy5EvXr1sHnzZly/fh39+/dHaGgoevfurfeYTJpyofj4eIwYMULVDJ2xgaCgIMyfPx/58+fPkXiv279/P/z8/LTWwPsUYuekPHnyYP/+/VkWzT137hyqVq2K+/fv50g77OzsEBsbCy8vL1XjZC4VpIvMGfLVYG9vj9jY2E8macpc37F58+YIDw/Pdn3H7du3K8ubqKFw4cLo0aMHBg8erFqMN8mfPz8mTZqEdu3a5XhsEcG4ceMwfvx4ZZkUc3NzfP/99xgzZowqMZk05UInTpxA+fLlVV0Vm7Gh1Z2b03LqgzS3xc5J1tbWOHjwoLKsRqZTp06hcuXKeluL6t8Y8n1mCO3bt0e5cuXQv3//HI/9pmV0NBoNLCwsUKRIETRr1izLJdv3kRvWdzTk37STkxMOHz4Mb2/vHI+d6cWLF7h06RIeP34MHx8frXUP9Y1rzxnA6yvPZ0fNa9+fauzcxpDfVT6V70mVKlXC3LlzMWvWLK3ysLAwVKhQwUCtyjkvX77E7t27cfnyZXz11VewtbXF7du3YWdnp+qHStGiRTF69Gjs378fFSpUyLLOY58+fVSLffz4cRw7dgzp6enKOMULFy7A2NgYJUqUwC+//ILvvvsO+/btg4+Pj15iGnp9RwBo1aoVtm3bhu7du+d47C5dumD58uWqr+33T8zMzPT2+/w37GkyACMjo2y/lbxOo9Go0uPyqcbOTunSpbFlyxZlYcecZMjeh0+l52P//v0ICAhAxYoVUbduXQCvBqweOXIE27ZtQ/Xq1XOkHePHj8e3336rLGaaE65fv44GDRrgxo0bSE1NVRaP7du3L1JTUxEWFqZabEMOxp4+fTr27t2LRYsWKWMnk5OT0aVLF1SrVg1du3bFV199hWfPnmV7Q4q+JSUlqfZ7nzlzpvLzkydPMHXqVAQFBaFMmTJZxkrqO1F9vUcvIyMD4eHhKFu2LMqWLZsltr4Xxjb02D0mTQZQoEAB/PLLL2jWrFm2+2NjY1GhQgVVkodPNXamGzduwN3dPctgRRFBfHw8ChUqpFrs1y1fvhzNmjXL8i38Y4+d02JjYzF58mTExsbC0tISZcuWxdChQ1G0aFFDN01VzZs3h62tLRYsWAAnJyclSd69eze6du2KixcvGrqJqihQoAC2b9+epdchLi4O9evXx61bt3Ds2DHUr18ff/31l15jT5w4ER4eHvjyyy8BvOr9+eOPP5A/f35s3rwZn332mV7j6TpmTI1EtXbt2jrH3rlzp15jG3rsHi/PGUCFChUQExPzxuTh33pjGPvdeXp64s6dO1nucHnw4AE8PT1zrJfrq6++ypE4uS12TitXrhyWLVuW43FbtmyJSpUqZRmYO2nSJBw5cgS//fabqvH37t2LAwcOwMzMTKvcw8MDt27dUjX26zL/nv/+JUUtycnJSExMzJI03bt3DykpKQAABwcHvHjxQu+xw8LClPfa9u3bsWPHDkRERGD16tUYOHAgtm3bptd4V69e1evx3sauXbsMFlvNmxh0waTJAAYOHIgnT568cX+RIkVUe1N+qrEziUi2/8AfP34MCwsLVWM/efIEEyZMeON8JmpetjBk7Nzg+fPnWT4o1ZynKSoqCiNHjsxS3rBhQ0yZMkW1uJkyMjKy/QJw8+bNHJlmYsmSJZg8ebLSo1WsWDEMHDhQ9TusmjVrhk6dOmHKlCmoWLEiAODIkSP4/vvvlbnQDh8+jGLFiuk9dkJCgnKpf+PGjWjdujXq168PDw8PVK5cWe/xcgtDzodmCEyaDODfxlJYW1ujZs2ajK1HmdfgNRoNQkNDYWVlpexLT0/HoUOHUK5cOVViZ+rSpQv27NmDdu3aZTufycca21CePn2KQYMGYfXq1dlOL6Bmr+Ljx4+z9PIAr+6myuzxUFP9+vUxffp0zJ07F8Cr9/3jx48xYsQINGrUSNXYU6dORWhoKHr16oWqVasCAPbt24fu3bvjr7/+UvWuul9//RX9+/dHmzZt8PLlSwCvJpFt3749pk2bBgAoUaIE5s+fr/fYjo6OiI+Ph7u7OyIiIjB27FgAr76oqd2DbciezTZt2mQ7H9rq1atVmQ/N19dX5/9fasxPxTFN9EnIvAa/Z88e+Pv7a32gmZmZwcPDA99//72qY10cHBywadMm5YMkJxkytqH07NkTu3btwpgxY9CuXTvMnj0bt27dwq+//ooJEyYgODhYtdiVKlVC48aNs8zEPHLkSGzYsAExMTGqxQZe9SgFBgZCRHDx4kX4+fnh4sWLyJs3L6KiolSdAd7T0xOjRo1CSEiIVnl4eDhGjhyZI5eVHj9+rPSeenl5qXq3YKZevXph48aNKFq0KI4fP45r167BxsYGK1euxKRJk1SdYDJfvnzYuXNnttNrBAQE4O7du6rFzun50EaNGqVz3REjRug1NsCeJvpEZF7269ixI2bMmGGQLmNHR0e9zg/zocQ2lA0bNmDJkiWoVasWOnbsiOrVq6NIkSIoXLgwli1bpmrSFBoaihYtWuDy5cuoU6cOgFd37q1YsUL18UwAULBgQZw4cQIrV67EyZMn8fjxY3Tu3BnBwcGwtLRUNfadO3dQpUqVLOVVqlTBnTt3VI2dycbGBnFxcWjatGmO3fAwbdo0eHh4ID4+HpMmTVIStTt37mTphdE3Q/ZspqamKr16r0tLS8OzZ8/0Hk+NROitqLKiHdEHIjk5WdauXStnz55VPdbSpUvlP//5T44tnJpbYhuKtbW1XL9+XUREChQoIIcOHRIRkStXroi1tbXq8Tdu3ChVqlQRKysrcXJyktq1a8vu3btVj2topUqVUhbsfd2YMWOkdOnSOdYOW1tbZQHXj13FihVl1KhRWcpHjBgh5cuXVzV2rVq1pFevXlnKe/ToIdWqVVM1dqajR4/K0qVLZenSpXLs2DFVY7GniT4prVu3Ro0aNdCrVy88e/YMfn5+uHbtGkQEK1euRMuWLfUa7+/X3y9dugQXFxd4eHhkmc9E3933hoydG3h5eeHq1asoVKgQSpQogdWrV6NSpUrYsGFDjsyZFBQUhKCgINXjvImhFnAdNWoUvvzyS0RFRSmXg/fv34/IyEisXr1atbh/JwYaeXLmzBncuHEjy40HTZs2VS2mIXs2x44di4CAAJw4cSLb+dDUlJiYiDZt2mD37t3K33RSUhJq166NlStXIl++fHqPyaSJPilRUVH44YcfALxaSFJEkJSUhPDwcIwdO1bvSVPmHTuGYMjYuUHHjh1x4sQJ1KxZE0OGDEGTJk3w888/Iy0tTe8T7r1JTEwMzp49CwAoVaoUfH19cyTuvy3gqmbS1LJlSxw6dAjTpk3DunXrAAAlS5bE4cOHc+z8DeHKlSv44osvcOrUKa3pUzJfezUHgzdp0gTr1q3DuHHj8Pvvvytzku3YsUO1m2syVa1aFdHR0Zg8eTJWr16txF6wYIHq86H17t0bjx49QlxcnDKm6syZM2jfvj369OmDFStW6D0mB4LTJ8XS0hIXLlyAu7s7QkJC4ObmhgkTJuDGjRvw8fHB48ePDd1EUsn169cRExODIkWKoGzZsqrGMsQ34NcZcgHX3GLfvn2oWLFiji1M3aRJExgbG2P+/Pnw9PTE4cOHcf/+fXz33Xf46aefcmwG+k+Jvb09duzYoUwvkenw4cOoX78+kpKS9B7TSO9HJMrF3N3dER0djSdPniAiIgL169cHADx8+FD1eZq8vLyyvZMkKSlJ9SVNDBk7N7h58ybc3d3RokUL1RMmQPsb8IMHD/DgwQOcPn0aKSkpqq69lunhw4do1aqV6nGyY2xsjMTExCzl9+/fh7GxcY61o1q1ajh48CA2b96Mhw8fqh4vOjoao0ePRt68eWFkZAQjIyNUq1YN48ePV/13Hh8fj5s3byqPDx8+jH79+ilTTqjp2LFjOHXqlPL4zz//RPPmzfHf//5XlUlEX5eRkZFlqAHwagD83y9J642qI6aIcpnZs2eLiYmJODg4yGeffSbp6ekiIjJz5kypVauWqrE1Go3cvXs3S3lCQoKYmpp+tLFzg5weFGxnZyeHDx/OUn7o0CGxt7dXPX6nTp1kzpw5qsfJzpvea7du3RILCwtVYk6YMEGGDRumPM7IyJDAwEDRaDSi0WjExcVFTp8+rUrsTA4ODnLlyhUREfHy8pKdO3eKiMilS5fE0tJS1djVqlWTJUuWiIjInTt3xNbWVvz9/SVv3rzZDhDXJz8/P/n9999FROTy5ctibm4ubdu2lSJFikjfvn1Vjd20aVOpUaOG3Lp1Sym7efOm1KxZU5o3b65KTI5pok9Kjx49ULlyZdy4cQP16tWDkdGrzlYvLy/8+OOPqsRcv3698vPWrVthb2+vPE5PT0dkZKTO60h9SLFzE8nhUQiG+Ab8+gKuRYoUQWhoKA4ePJgjC7i+Hl+j0WD+/PlacyOlp6cjKioKJUqU0HtcAFi1apXWpcjff/8dUVFR2Lt3L0qWLImQkBCMGjVK1YHopUuXxokTJ+Dp6YnKlStj0qRJMDMzw9y5c1XvzT19+jQqVaoE4NWkkmXKlMH+/fuxbds2dO/eXdUxbBcuXFAmBv7tt99Qs2ZNLF++HPv370ebNm0wffp01WL//PPPaNq0KTw8PJTZ2OPj41G6dGn873//UyUmxzQR4dUf2ogRI7Bw4UK9HzszMctubT1TU1N4eHhgypQpaNy48UcVOzextbVVFq3NCc2aNUNSUhJWrFgBNzc3AMCtW7cQHBwMR0dHrF27Vu8xDbmA6+vxr1+/joIFC2pdisucQHb06NGqLCni6OiIAwcOKIOBO3bsiPT0dCxZsgQAcPDgQbRq1Qrx8fF6j51p69atePLkCVq0aIGLFy+iSZMmuHDhApycnLBy5UrlzjI12NjY4PTp0/Dw8EDTpk1RtWpVDB48GDdu3EDx4sVVmS8pk52dHWJiYlC0aFHUq1cPjRs3Rt++fXMkNvDqC9GOHTtw7tw5AK9uOggICFA1INEnLzY2VoyMjFSN4eHhIffu3VM1Rm6MnRuMGzdOHj58mGPxbty4IeXKlRNTU1Px8vISLy8vMTU1FV9fX4mPj8+xdhhCrVq15MGDBzka08bGRuvya/HixbUuT16/fl21S4P/5P79+5KRkaF6nEqVKsngwYMlKipKLCwsJDY2VkREoqOjpUCBAqrGrl27toSEhMiSJUvE1NRULl68KCIiu3fvlsKFC6sa2xB4eY4+Ca9fpspOTixYm7l8xJMnT7B69WpcunQJbm5uaNOmDZycnFSJ2bt3b7Ru3dqgK6LnBkOHDs3ReO7u7jh27FjOfgPOJdRedDs73t7eiIqKgpeXF27cuIELFy6gRo0ayv6bN2+q9jfWqVMnneqp0YudaeLEifjiiy8wefJktG/fHp999hmAV//3Mi/bqWX69OkIDg7GunXr8MMPP6BIkSIAXl0izW5meH3q06cPihQpkuVy888//4xLly6pcmmQl+fok2BkZJTtJarXaTQaVeZS8fHxwb59+5AnTx7Ex8ejevXqSEpKQrFixXD58mWYmJjg4MGDqowtyjxvb29vdO7cGe3bt4erq6ve4+RGhlzENDtJSUk5MqkmYNhzN0TsefPmoX///vjyyy9x8OBB2Nvb48CBA8r+sWPH4tChQ9iwYYPeYxsZGaFw4cLw9fX9x/8valySfV16ejpSUlLg6OiolF27dg1WVlaqrjX4Js+fP4exsXG2Y/v0pUCBAli/fj0qVKigVX7s2DE0bdpU645CvTFsRxdRznBzc5N169a9cf/x48dVuzz3+t1EwcHBUqVKFUlKShIRkUePHklAQIC0bdtWtdg7duyQvn37St68ecXU1FSaNm0qGzZsUO4c/FjlzZtXTp48maX85MmT4uzsrGrsCRMmyMqVK5XHrVq1EiMjI3Fzc1MunajJkOduqNgLFiyQ5s2bS/fu3eXOnTta+7799lv5448/VInbo0cPcXR0lHLlysmMGTPk/v37qsT5N2lpabJ9+3YJCwuTlJQUEXl1x+KjR49yrA3ffvttjg4DMDc3Vy4Hvu7ixYtibm6uSkwmTfRJaNKkiYSGhr5xf2xsrGg0GlViv540eXl5ybZt27T279+/X9zd3VWP/eLFC1m1apUEBgaKsbGxuLm5yX//+99s/+l8DCwsLOTcuXNZys+ePav6+BYPDw/Zv3+/iIhs27ZNHBwcZOvWrdK5c2epV6+eqrFFDHvuhoidnJys06aW58+fy/LlyyUgIECsrKykVatWEhERkSPjmURErl27JiVKlBArKysxNjZWxnf16dNHvvnmmxxpg0jOT+1RqlQpmTVrVpbymTNnSsmSJVWJyckt6ZMwcODAf7y+XqRIEVXHYmQupfD8+XPkz59fa1+BAgVw79491WJnMjU1RevWrREREYErV66ga9euWLZsGYoXL656bEMoU6YMVq1alaV85cqV8PHxUTV2QkKCcgv0xo0b0bp1a9SvXx+DBg3CkSNHVI0NGPbcDRHbwcEBjo6O/7qpxdzcHG3btsX27dtx5swZlCpVCj169ICHh0eOrDLQt29f+Pn54eHDh7C0tFTKv/jiC0RGRqoeP5Pk8GifAQMGYNCgQRgxYgT27NmDPXv2YPjw4RgyZAj69++vSkwOBKdPwr8tYWBtba3qGk1169aFiYkJUlJScP78eZQuXVrZd/36ddUGqb5JoUKFMHLkSIwYMQI7duzI0dg5xZCLmDo6OiI+Ph7u7u6IiIjA2LFjAbz6UFFzDbJMhjx3Q8R+/QuPiKBRo0aYP38+ChQooEq8f/L6+Mmc+F0DwN69e3HgwAGYmZlplXt4eODWrVt6j7d+/Xo0bNhQ1fFKuujUqRNSU1Px448/YsyYMQBenfOcOXMQEhKiTlBV+q+ISDFy5EitLSIiQmv/999/L23atFEltoeHh/z111+qHPtDsHHjRqlSpYpYWVmJk5OT1K5dW3bv3q163J49e0rhwoUlICBAnJyclHElK1asEF9fX9Xjixju3A0dWyTrFARqe/3ynIWFhfznP/+RTZs25di4QQcHB4mLixMR7XPfu3evKuPIjIyMJDExUfk5uxngc1piYmKOjN/i3XNERHqWlpaGGTNmID4+Hh06dICvry8AYNq0abC1tUWXLl0M3MKPW05OZtqjRw+sXLkS7u7u6NSpE4KDg5E3b17V477uyy+/hL29PebOnQtbW1ucPHkS+fLlQ7NmzVCoUCEsWrRIr/FcXV0xb948NGnSBEZGRrh7967qi1D/mwkTJqB79+6q36HKpImIPmoxMTE4e/YsAKBUqVJKAkPqMuTrnpNJk5GREQoVKgRfX19l7GJ21qxZo1ob4uPj0aBBA4gILl68CD8/P1y8eBF58+ZFVFSU3qccGDlyJEaPHv2P55sppy5R2tnZITY2VvXfOcc0EdFHKTExEW3atMHu3buVb59JSUmoXbs2Vq5cqeo34/DwcOTNmxdBQUEAgEGDBmHu3Lnw8fHBihUrULhwYdViA68+qKZNm4bVq1fjxo0bWVabf/DggWqxDfm6v06XD3R9CAkJybFYb+Lu7o4TJ05g1apVOHHiBB4/fozOnTsjODhYa2C4vowcORJt2rTBpUuX0LRpUyxatCjH5iB7k5zq/2FPExF9lL788ktcuXIFS5YsUdYkO3PmDNq3b48iRYpgxYoVqsUuXrw45syZgzp16iA6OhoBAQGYNm0aNm7cCBMTE1V7HQBg+PDhmD9/Pr777jsMGzYMP/zwA65du4Z169Zh+PDhqizYm8kQr3uLFi20Hm/YsAF16tSBtbW1Vrnar7shpKWloUSJEti4caPyeuekUaNGYeDAgbCyssrx2K/Lqd5FJk1E9FGyt7fHjh07ULFiRa3yw4cPo379+khKSlIttpWVFc6dO4dChQph8ODBuHPnDpYsWYK4uDjUqlVL9SkmvL29MXPmTAQFBcHW1haxsbFK2cGDB7F8+XLVYhvide/YsaNO9fQ9tie3KFCgAHbs2GGQpCnTvXv3cP78eQCvvjTk9Bin+Ph4FChQQFmkXC28PEdEH6WMjIxsb4k2NTVFRkaGqrFtbGxw//59FCpUCNu2bcOAAQMAABYWFqqv+g68mieqTJkySluSk5MBAI0bN0ZoaKiqsQ3xun+syZCuevbsiYkTJ2L+/PkwMcnZj/WnT5+iV69eWLp0qTJ+ydjYGCEhIZg1a5bqPVBJSUn4/fffcfnyZQwcOBB58uTBsWPH4OLiosqUE5zckog+SnXq1EHfvn1x+/ZtpezWrVvo378/6tatq2rsevXqoUuXLujSpQsuXLiARo0aAQDi4uJUH88EAAULFsSdO3cAvOp12rZtGwDgyJEjMDc3VzW2IV/3T9WRI0ewZs0aFCpUCIGBgWjRooXWpqb+/ftjz549WL9+PZKSkpCUlIQ///wTe/bswXfffadq7JMnT6JYsWKYOHEifvrpJ6UXc82aNaot0s2kiYg+Sj///DNSUlLg4eEBb29veHt7w9PTEykpKZg1a5aqsWfPng1/f3/cu3cPf/zxhzJ5aUxMDNq2batqbEB7JujevXsjNDQURYsWRUhICDp16qRqbEO+7p8qBwcHtGzZEoGBgXBzc4O9vb3WpqY//vgDCxYsQMOGDWFnZwc7Ozs0atQI8+bNw++//65q7AEDBqBDhw64ePEiLCwslPJGjRohKipKlZgc00REHy0RwY4dO3Du3DkAQMmSJREQEJDj7Xj06BFWrFiB+fPnIyYmJsduw8508OBBHDhwAEWLFkWTJk1UjyciiIyMVKYcMNTr/rHLyMjA5MmTsX79erx48QJ16tTByJEjVblj7k2srKwQExOTZTxVXFwcKlWqhCdPnqgW297eHseOHYO3t7fWQPDr16+jePHieP78ud5jckwTEX20NBoN6tWrh3r16gGAqoO/sxMVFYUFCxbgjz/+gJubG1q0aIHZs2erHvf+/ftK71Z8fDw2b96MZ8+ewc/PT9W4GRkZWLx4MdasWYNr165Bo9HA09MT9vb2EBGD35r/sfnxxx8xcuRIBAQEwNLSEjNnzsS9e/ewcOHCHGuDv78/RowYgSVLlii9Pc+ePcOoUaPg7++vamxzc3OkpKRkKb9w4YJ6A9FVn3OciMgAJkyYICtXrlQet2rVSoyMjMTNzU1iY2NVi3vnzh0ZP368FClSRJydnaVXr15iYmKiLHOhppMnT0rhwoXFyMhIihcvLsePHxcXFxexsbEROzs7MTY2lrVr16oSOyMjQ4KCgkSj0Ui5cuWkTZs28uWXX0rZsmVFo9FIs2bNVIn7KStSpIiEhYUpj7dv3y5mZmY5tnyLyKv3nJubmzg5OUmdOnWkTp064uTkJAUKFJDTp0+rGrtz587SvHlzefHihdjY2MiVK1fk+vXr4uvrK3379lUlJpMmIvooeXh4yP79+0VEZNu2beLg4CBbt26Vzp07S7169VSJ2bhxY7Gzs5O2bdvKxo0b5eXLlyIiOZY0NWjQQBo3biz79u2Tb775RgoUKCCdOnWS9PR0SU9Plx49ekjlypVVib1w4UKxtbWVnTt3ZtkXGRkptra2Eh4erkrsT5WZmZncuHFDq8zc3Fzi4+NztB1PnjyRuXPnyoABA2TAgAEyb948efr0qepxk5KSJCAgQBwcHMTY2Fjc3d3F1NRUatSoIY8fP1YlJsc0EdFHydLSEhcuXIC7uzv69u2L58+f49dff8WFCxdQuXJlPHz4UO8xTUxM0KdPH3z77bcoWrSoUm5qaooTJ07Ax8dH7zFflzdvXuzcuRNly5bF48ePYWdnhyNHjqBChQoAgHPnzuHzzz9X5TJl/fr1UadOHQwZMiTb/ePGjcOePXuwdetWvcf+VBkbGyMhIUHrUlTm2nOenp6qxzf0xJqZ9u3bh5MnT+Lx48coX768quPnOKaJiD5Kjo6OiI+Ph7u7OyIiIjB27FgArwYpqzUQe9++fViwYAEqVKiAkiVLol27dmjTpo0qsbLz4MEDuLq6Ang1P5O1tTUcHR2V/Y6Ojnj06JEqsU+ePIlJkya9cX/Dhg0xc+ZMVWJ/qkQEHTp00JpG4vnz5+jevbvWbOhqzYRuamqqymDrt1WtWjVUq1YtR2IxaSKij1KLFi3w1VdfoWjRorh//z4aNmwIADh+/DiKFCmiSszPP/8cn3/+OaZPn45Vq1Zh4cKFGDBgADIyMrB9+3a4u7vD1tZWldiZ/j7YOqcGXz948AAuLi5v3O/i4qJK796nrH379lnKvv766xxtgyEn1gRezVG1a9cuJCYmZpk8derUqXqPx8tzRPRRSktLw4wZMxAfH48OHTrA19cXADBt2jTY2tqiS5cuOdKO8+fPY8GCBVi6dCmSkpJQr149rF+/XpVYRkZGaNiwodLz8Pc12FJTUxEREaFKT1t2l4ped/fuXbi5ueX4dAukrsw5wWxsbFCmTJkcXe9v3LhxGDZsGIoXLw4XFxetLwgajQY7d+7Ue0wmTUREOSA9PR0bNmzAwoULVUuaDLkG298Ttr9TM2Ejw/m395yaS9y4uLhg4sSJ6NChg2ox/o5JExF9lMLDw5E3b14EBQUBAAYNGoS5c+fCx8cHK1asyJHlTD4ln/qiuZ+a3DCxZv78+REVFaV104XamDQR0UepePHimDNnDurUqYPo6GgEBARg2rRp2LhxI0xMTFS9bED0sRszZozWxJpbt25F27Ztc3RizUmTJuH27duYPn16jsVk0kREHyUrKyucO3cOhQoVwuDBg3Hnzh0sWbIEcXFxqFWrFu7du2foJhJ9sIoWLYrvv/8e33zzDQBgx44dCAoKwrNnz2BklDPL2mZkZCAoKAgXLlyAj48PTE1Ntfar8cWIC/YS0UfJxsYG9+/fBwBs27ZNWUrFwsICz549M2TTiD54N27cQKNGjZTHAQEB0Gg0uH37do61oU+fPti1axeKFSsGJyenHFmomFMOENFHqV69eujSpQt8fX1x4cIF5R98XFwcxzMRvaeXL18qa81lMjU1RVpaWo61ITw8HH/88YcybjEnMGkioo/S7NmzMWzYMMTHx+OPP/5QFrCNiYlB27ZtDdw6og+boSfWBIA8efLA29tbteNnh2OaiOiT8OjRI6xYsQLz589HTEwMb30neg+54W7JRYsWISIiAosWLYKVlZVqcV7HpImIPmpRUVFYsGAB/vjjD7i5uaFFixZo2bIlKlasaOimEdF78PX1xeXLlyEi8PDwyDIQ/NixY3qPyctzRPTRSUhIwOLFi7FgwQKkpKSgdevWSE1Nxbp161RfNJeIckbz5s1zPCZ7mojoo9KkSRNERUUhKCgIwcHBaNCgAYyNjWFqaooTJ04waSKid8aeJiL6qGzZsgV9+vTBt99+m6MzBRORYcTExODs2bMAgFKlSinrTKqB8zQR0Udl3759ePToESpUqIDKlSvj559/xl9//WXoZhGRniUmJqJOnTqoWLEi+vTpgz59+qBChQqoW7euapPXMmkioo/K559/jnnz5uHOnTv45ptvsHLlSri5uSEjIwPbt2/Ho0ePDN1EItKD3r1749GjR4iLi8ODBw/w4MEDnD59GikpKejTp48qMTmmiYg+eufPn8eCBQuwdOlSJCUloV69eli/fr2hm0VE78He3h47duzIcifs4cOHUb9+fSQlJek9JnuaiOijV7x4cUyaNAk3b97EihUrDN0cItKDjIyMLNMMAK9mJs/IyFAlJnuaiIiI6IPTrFkzJCUlYcWKFXBzcwMA3Lp1C8HBwXB0dMTatWv1HpNJExEREX1w4uPj0bRpU8TFxcHd3R3Aq4WEy5Qpg/Xr16NgwYJ6j8mkiYiIiD5IIoIdO3bg3LlzAAAfHx/UrVtXtXgc00REREQfjOjoaGzcuBEAoNFoUK9ePdjZ2WHKlClo27YtunXrhtTUVFViM2kiIiKiD8bo0aMRFxenPD516hS6du2KevXqYciQIdiwYQPGjx+vSmxeniMiIqIPRv78+bFhwwb4+fkBAH744Qfs2bMH+/btAwD89ttvGDFiBM6cOaP32OxpIiIiog/Gw4cP4eLiojzes2cPGjZsqDyuWLEi4uPjVYnNpImIiIg+GC4uLrh69SoA4MWLFzh27Bg+//xzZf+jR4+ynb9JH5g0ERER0QejUaNGGDJkCPbu3YuhQ4fCysoK1atXV/afPHkS3t7eqsQ2UeWoRERERCoYM2YMWrRogZo1a8LGxgbh4eEwMzNT9i9cuBD169dXJTYHghMREdEHJzk5GTY2NjA2NtYqf/DgAWxsbLQSKX1h0kRERESkA45pIiIiItIBkyYiIiIiHTBpIiIiItIBkyYiyhU0Gg3WrVtn6GagQ4cOaN68uaGbobrFixfDwcHB0M0g+qAwaSIiverQoQM0Gk2WrUGDBoZumpZr165Bo9EgNjZWq3zGjBlYvHix6vFzS5JIRLrjPE1EpHcNGjTAokWLtMrMzc0N1Jq3Y29vb+gmEFEuxZ4mItI7c3NzuLq6am2Ojo7K/osXL6JGjRqwsLCAj48Ptm/frvX83bt3Q6PRICkpSSmLjY2FRqPBtWvXlLL9+/ejVq1asLKygqOjIwIDA/Hw4UMAQEREBKpVqwYHBwc4OTmhcePGuHz5svJcT09PAICvry80Gg1q1aoFIOvludTUVPTp0wfOzs6wsLBAtWrVcOTIkSxtjYyMhJ+fH6ysrFClShWcP3/+vV7D+fPno2TJkrCwsECJEiXwyy+/KPuqVKmCwYMHa9W/d+8eTE1NERUVpbT7+++/R4ECBWBtbY3KlStj9+7d79Umok8dkyYiylEZGRlo0aIFzMzMcOjQIYSFhWVJAHQRGxuLunXrwsfHB9HR0di3bx+aNGmC9PR0AMCTJ08wYMAAHD16FJGRkTAyMsIXX3yBjIwMAMDhw4cBADt27MCdO3ewZs2abOMMGjQIf/zxB8LDw3Hs2DEUKVIEgYGBePDggVa9H374AVOmTMHRo0dhYmKCTp06vfU5ZVq2bBmGDx+OH3/8EWfPnsW4ceMQGhqK8PBwAEBwcDBWrlyJ16fZW7VqFdzc3JTlJHr16oXo6GisXLkSJ0+eRKtWrdCgQQNcvHjxndtF9MkTIiI9at++vRgbG4u1tbXW9uOPP4qIyNatW8XExERu3bqlPGfLli0CQNauXSsiIrt27RIA8vDhQ6XO8ePHBYBcvXpVRETatm0rVatW1bld9+7dEwBy6tQpERG5evWqAJDjx49naX+zZs1EROTx48diamoqy5YtU/a/ePFC3NzcZNKkSVpt3bFjh1Jn06ZNAkCePXv2xva8fr5/5+3tLcuXL9cqGzNmjPj7+4uISGJiopiYmEhUVJSy39/fXwYPHiwiItevXxdjY2Ot11hEpG7dujJ06FAREVm0aJHY29u/sX1ElBXHNBGR3tWuXRtz5szRKsuTJw8A4OzZs3B3d4ebm5uyz9/f/61jxMbGolWrVm/cf/HiRQwfPhyHDh3CX3/9pfQw3bhxA6VLl9YpxuXLl5GWloaqVasqZaampqhUqRLOnj2rVbds2bLKz/nz5wcAJCYmolChQjqfE/Cqh+zy5cvo3LkzunbtqpS/fPlSGW+VL18+1K9fH8uWLUP16tVx9epVREdH49dffwUAnDp1Cunp6ShWrJjWsVNTU+Hk5PRW7SGi/8OkiYj0ztraGkWKFHnn5xsZvRo5IK9dfkpLS9OqY2lp+Y/HaNKkCQoXLox58+bBzc0NGRkZKF26NF68ePHO7fonpqamys8ajQYAlETtbTx+/BgAMG/ePFSuXFlr3+trbAUHB6NPnz6YNWsWli9fjjJlyqBMmTLKMYyNjRETE5NlXS4bG5u3bhMRvcIxTUSUo0qWLIn4+HjcuXNHKTt48KBWnXz58gGAVp2/Tw1QtmxZREZGZhvj/v37OH/+PIYNG4a6deuiZMmSygDxTJmLeWaOgcqOt7c3zMzMsH//fqUsLS0NR44cgY+Pzz+c5btzcXGBm5sbrly5giJFimhtmYPXAaBZs2Z4/vw5IiIisHz5cgQHByv7fH19kZ6ejsTExCzHcHV1VaXdRJ8C9jQRkd6lpqYiISFBq8zExAR58+ZFQEAAihUrhvbt22Py5MlISUnBDz/8oFW3SJEicHd3x8iRI/Hjjz/iwoULmDJliladoUOHokyZMujRowe6d+8OMzMz7Nq1C61atUKePHng5OSEuXPnIn/+/Lhx4waGDBmi9XxnZ2dYWloiIiICBQsWhIWFRZbpBqytrfHtt99i4MCByJMnDwoVKoRJkybh6dOn6Ny583u/TlevXs2SDBYtWhSjRo1Cnz59YG9vjwYNGiA1NRVHjx7Fw4cPMWDAAKVtzZs3R2hoKM6ePYu2bdsqxyhWrBiCg4MREhKCKVOmwNfXF/fu3UNkZCTKli2LoKCg92470SfJ0IOqiOjj0r59ewGQZStevLhS5/z581KtWjUxMzOTYsWKSURERJaB0fv27ZMyZcqIhYWFVK9eXX777TetgeAiIrt375YqVaqIubm5ODg4SGBgoDJ4fPv27VKyZEkxNzeXsmXLyu7du7PEmDdvnri7u4uRkZHUrFlTaX/mQHARkWfPnknv3r0lb968Ym5uLlWrVpXDhw8r+3UZtJ6d7F4jALJ3714REVm2bJmUK1dOzMzMxNHRUWrUqCFr1qzROsbmzZsFgNSoUSPL8V+8eCHDhw8XDw8PMTU1lfz588sXX3whJ0+eFBEOBCd6FxqR1wYNEBEREVG2OKaJiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh0wKSJiIiISAdMmoiIiIh08P8AApXEpmGBewkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Data Visualisations\n",
        "education_income.plot(kind=\"bar\", stacked=True)\n",
        "plt.xlabel(\"Education Level\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Education Level vs. Income\")\n",
        "plt.legend(title=\"Income\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "257e5ad2-1c99-4075-b225-93ad1b932c9c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "257e5ad2-1c99-4075-b225-93ad1b932c9c",
        "outputId": "f1310900-350d-4ed9-fe64-e1df03679b81"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Age distribution\n",
        "plt.figure(figsize=(8, 4))\n",
        "sns.histplot(data['age'], bins=30, kde=True, color=\"blue\")\n",
        "plt.title(\"Age Distribution\")\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "d59af6e0-04c3-4b9e-8475-51b85ee99669",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "d59af6e0-04c3-4b9e-8475-51b85ee99669",
        "outputId": "8fbc888f-f630-4481-91a0-3e973700bcfb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "sns.countplot(y=data[\"workclass\"], order=data[\"workclass\"].value_counts().index, color=\"blue\")\n",
        "plt.title(\"Workclass Distribution\")\n",
        "plt.xlabel(\"Count\")\n",
        "plt.ylabel(\"Workclass\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "4795a976-93b0-40a2-9372-1b86e53d7b18",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "4795a976-93b0-40a2-9372-1b86e53d7b18",
        "outputId": "2153599f-f2fe-4b21-a96a-c6868da247d5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Income distribution by gender visualization. Create a horizontal bar chart\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.countplot(y=data[\"workclass\"], hue=data[\"workclass\"], order=data[\"workclass\"].value_counts().index, palette=\"magma\", legend=False)\n",
        "plt.title(\"Workclass Distribution\")\n",
        "plt.xlabel(\"Count\")\n",
        "plt.ylabel(\"Workclass\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "c7a6c4a0-7ff1-4b54-a605-7d15beee510c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "c7a6c4a0-7ff1-4b54-a605-7d15beee510c",
        "outputId": "0f3dd45f-49d9-4923-c471-d218169848e8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Income distribution by gender visualization Create a vertical bar chart\n",
        "plt.figure(figsize=(6, 4))\n",
        "sns.countplot(x='income', hue='sex', data=data, palette=\"coolwarm\")\n",
        "plt.title(\"Income Distribution by Gender\")\n",
        "plt.xlabel(\"Income\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "726bf3dc-95ee-4b16-b99b-9bc4c8c6c5fc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "726bf3dc-95ee-4b16-b99b-9bc4c8c6c5fc",
        "outputId": "eee376a1-8f58-4910-cd4e-69e1e8b69b6c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Hours per week vs Income\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.boxplot(x='income', y='hours_per_week',hue='income', data=data, palette=\"Set1\", dodge=False)\n",
        "plt.title(\"Hours Worked Per Week by Income Class\")\n",
        "plt.xlabel(\"Income\")\n",
        "plt.ylabel(\"Hours per Week\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "ccf463c8-982b-4d76-beb9-583f37a6180a",
      "metadata": {
        "id": "ccf463c8-982b-4d76-beb9-583f37a6180a"
      },
      "outputs": [],
      "source": [
        "# Convert categorical columns into numerical representations\n",
        "categorical_cols = data.select_dtypes(include=['object']).columns #Identifies categorical columns\n",
        "df_encoded = pd.get_dummies(data, columns=categorical_cols, drop_first=True) #Converts categorical variables into numeric form using one-hot encoding #Prevents redundant categories to avoid multicollinearity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "32d0c13e-fbbe-4c51-9d8c-0aaa9e28637c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32d0c13e-fbbe-4c51-9d8c-0aaa9e28637c",
        "outputId": "86098a3a-ba29-4520-d0e5-02b16002719a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30162, 96)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "df_encoded.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "e42ee50d-7889-455c-ae29-0c36f82fad86",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        },
        "id": "e42ee50d-7889-455c-ae29-0c36f82fad86",
        "outputId": "2943743f-afdd-4687-fa60-7f2d0e4bc2ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Correlation matrix\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(df_encoded.corr(), cmap='coolwarm', annot=False)\n",
        "plt.title(\"Correlation Matrix\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "30982e44-f37d-4603-833e-12a3b06cc937",
      "metadata": {
        "id": "30982e44-f37d-4603-833e-12a3b06cc937"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "data[\"income\"] = encoder.fit_transform(data[\"income\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "8f4acb89-1d53-450a-9142-c542df12da7b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "8f4acb89-1d53-450a-9142-c542df12da7b",
        "outputId": "56ce9506-ff09-48f4-bfc8-b550187b9d44"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Income distribution by gender visualization Create a vertical bar chart\n",
        "plt.figure(figsize=(6, 4))\n",
        "sns.countplot(x='income', hue='sex', data=data, palette=\"coolwarm\")\n",
        "plt.title(\"Income Distribution by Gender\")\n",
        "plt.xlabel(\"Income\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "cd491382-011e-41e6-a4fe-87e1067dfd49",
      "metadata": {
        "id": "cd491382-011e-41e6-a4fe-87e1067dfd49"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python [conda env:base] *",
      "language": "python",
      "name": "conda-base-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.7"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}