Arcadix_data_analytics_internship_assesment.ipynb

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        "<a href=\"https://colab.research.google.com/github/QadriTalib/Arcadix_data_analytics_internship_assesment/blob/main/Arcadix_data_analytics_internship_assesment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('WA_Fn-UseC_-HR-Employee-Attrition.csv') #Importing the dataset"
      ],
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        "id": "tt55RId0zDEV"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "df #Print the hole dataset"
      ],
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              "      Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
              "0      41       Yes      Travel_Rarely       1102                   Sales   \n",
              "1      49        No  Travel_Frequently        279  Research & Development   \n",
              "2      37       Yes      Travel_Rarely       1373  Research & Development   \n",
              "3      33        No  Travel_Frequently       1392  Research & Development   \n",
              "4      27        No      Travel_Rarely        591  Research & Development   \n",
              "...   ...       ...                ...        ...                     ...   \n",
              "1465   36        No  Travel_Frequently        884  Research & Development   \n",
              "1466   39        No      Travel_Rarely        613  Research & Development   \n",
              "1467   27        No      Travel_Rarely        155  Research & Development   \n",
              "1468   49        No  Travel_Frequently       1023                   Sales   \n",
              "1469   34        No      Travel_Rarely        628  Research & Development   \n",
              "\n",
              "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
              "0                    1          2  Life Sciences              1   \n",
              "1                    8          1  Life Sciences              1   \n",
              "2                    2          2          Other              1   \n",
              "3                    3          4  Life Sciences              1   \n",
              "4                    2          1        Medical              1   \n",
              "...                ...        ...            ...            ...   \n",
              "1465                23          2        Medical              1   \n",
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              "1467                 4          3  Life Sciences              1   \n",
              "1468                 2          3        Medical              1   \n",
              "1469                 8          3        Medical              1   \n",
              "\n",
              "      EmployeeNumber  ...  RelationshipSatisfaction StandardHours  \\\n",
              "0                  1  ...                         1            80   \n",
              "1                  2  ...                         4            80   \n",
              "2                  4  ...                         2            80   \n",
              "3                  5  ...                         3            80   \n",
              "4                  7  ...                         4            80   \n",
              "...              ...  ...                       ...           ...   \n",
              "1465            2061  ...                         3            80   \n",
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              "1467            2064  ...                         2            80   \n",
              "1468            2065  ...                         4            80   \n",
              "1469            2068  ...                         1            80   \n",
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              "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
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              "1467                 1                  6                      0   \n",
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              "1469                 0                  6                      3   \n",
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              "     WorkLifeBalance  YearsAtCompany YearsInCurrentRole  \\\n",
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              "4                           2                     2  \n",
              "...                       ...                   ...  \n",
              "1465                        0                     3  \n",
              "1466                        1                     7  \n",
              "1467                        0                     3  \n",
              "1468                        0                     8  \n",
              "1469                        1                     2  \n",
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              "<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-cb64e013-1111-4de3-b968-d484ca9954d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape #Total Row and Column count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0wk0whP6EZJ",
        "outputId": "6c3844b1-7bf9-4289-b605-4de6685127ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1470, 35)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns #To know what are coloumn in your dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jHcWAH2zQz_",
        "outputId": "a7aa0744-ba82-487a-9954-c47fef6a9cc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
              "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
              "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
              "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
              "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
              "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
              "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
              "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
              "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
              "       'YearsWithCurrManager'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum()) #How many null value."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "deXBGxec0BTk",
        "outputId": "b098ac2e-6702-40d3-99c4-30da4655f21b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Age                         0\n",
            "Attrition                   0\n",
            "BusinessTravel              0\n",
            "DailyRate                   0\n",
            "Department                  0\n",
            "DistanceFromHome            0\n",
            "Education                   0\n",
            "EducationField              0\n",
            "EmployeeCount               0\n",
            "EmployeeNumber              0\n",
            "EnvironmentSatisfaction     0\n",
            "Gender                      0\n",
            "HourlyRate                  0\n",
            "JobInvolvement              0\n",
            "JobLevel                    0\n",
            "JobRole                     0\n",
            "JobSatisfaction             0\n",
            "MaritalStatus               0\n",
            "MonthlyIncome               0\n",
            "MonthlyRate                 0\n",
            "NumCompaniesWorked          0\n",
            "Over18                      0\n",
            "OverTime                    0\n",
            "PercentSalaryHike           0\n",
            "PerformanceRating           0\n",
            "RelationshipSatisfaction    0\n",
            "StandardHours               0\n",
            "StockOptionLevel            0\n",
            "TotalWorkingYears           0\n",
            "TrainingTimesLastYear       0\n",
            "WorkLifeBalance             0\n",
            "YearsAtCompany              0\n",
            "YearsInCurrentRole          0\n",
            "YearsSinceLastPromotion     0\n",
            "YearsWithCurrManager        0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.dtypes) # Each column data type"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyMk3Jz-0cLF",
        "outputId": "7baaadee-bb75-4f40-9d73-bf122e522d0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Age                          int64\n",
            "Attrition                   object\n",
            "BusinessTravel              object\n",
            "DailyRate                    int64\n",
            "Department                  object\n",
            "DistanceFromHome             int64\n",
            "Education                    int64\n",
            "EducationField              object\n",
            "EmployeeCount                int64\n",
            "EmployeeNumber               int64\n",
            "EnvironmentSatisfaction      int64\n",
            "Gender                      object\n",
            "HourlyRate                   int64\n",
            "JobInvolvement               int64\n",
            "JobLevel                     int64\n",
            "JobRole                     object\n",
            "JobSatisfaction              int64\n",
            "MaritalStatus               object\n",
            "MonthlyIncome                int64\n",
            "MonthlyRate                  int64\n",
            "NumCompaniesWorked           int64\n",
            "Over18                      object\n",
            "OverTime                    object\n",
            "PercentSalaryHike            int64\n",
            "PerformanceRating            int64\n",
            "RelationshipSatisfaction     int64\n",
            "StandardHours                int64\n",
            "StockOptionLevel             int64\n",
            "TotalWorkingYears            int64\n",
            "TrainingTimesLastYear        int64\n",
            "WorkLifeBalance              int64\n",
            "YearsAtCompany               int64\n",
            "YearsInCurrentRole           int64\n",
            "YearsSinceLastPromotion      int64\n",
            "YearsWithCurrManager         int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info() # info given count and datatype of it"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "epJbRN7J7Rud",
        "outputId": "6d786fa7-8f9c-4387-928e-f1dad161b4a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1470 entries, 0 to 1469\n",
            "Data columns (total 35 columns):\n",
            " #   Column                    Non-Null Count  Dtype \n",
            "---  ------                    --------------  ----- \n",
            " 0   Age                       1470 non-null   int64 \n",
            " 1   Attrition                 1470 non-null   object\n",
            " 2   BusinessTravel            1470 non-null   object\n",
            " 3   DailyRate                 1470 non-null   int64 \n",
            " 4   Department                1470 non-null   object\n",
            " 5   DistanceFromHome          1470 non-null   int64 \n",
            " 6   Education                 1470 non-null   int64 \n",
            " 7   EducationField            1470 non-null   object\n",
            " 8   EmployeeCount             1470 non-null   int64 \n",
            " 9   EmployeeNumber            1470 non-null   int64 \n",
            " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
            " 11  Gender                    1470 non-null   object\n",
            " 12  HourlyRate                1470 non-null   int64 \n",
            " 13  JobInvolvement            1470 non-null   int64 \n",
            " 14  JobLevel                  1470 non-null   int64 \n",
            " 15  JobRole                   1470 non-null   object\n",
            " 16  JobSatisfaction           1470 non-null   int64 \n",
            " 17  MaritalStatus             1470 non-null   object\n",
            " 18  MonthlyIncome             1470 non-null   int64 \n",
            " 19  MonthlyRate               1470 non-null   int64 \n",
            " 20  NumCompaniesWorked        1470 non-null   int64 \n",
            " 21  Over18                    1470 non-null   object\n",
            " 22  OverTime                  1470 non-null   object\n",
            " 23  PercentSalaryHike         1470 non-null   int64 \n",
            " 24  PerformanceRating         1470 non-null   int64 \n",
            " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
            " 26  StandardHours             1470 non-null   int64 \n",
            " 27  StockOptionLevel          1470 non-null   int64 \n",
            " 28  TotalWorkingYears         1470 non-null   int64 \n",
            " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
            " 30  WorkLifeBalance           1470 non-null   int64 \n",
            " 31  YearsAtCompany            1470 non-null   int64 \n",
            " 32  YearsInCurrentRole        1470 non-null   int64 \n",
            " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
            " 34  YearsWithCurrManager      1470 non-null   int64 \n",
            "dtypes: int64(26), object(9)\n",
            "memory usage: 402.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe() #This function used to find the total count, mean, standard deviation, min value, max value, 25-50-75 percentage of the dataset value."
      ],
      "metadata": {
        "id": "oLw2nAgE7nvm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "6bc6f4cc-3f0a-4a38-cfba-165ea370e287"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\n",
              "count  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \n",
              "mean     36.923810   802.485714          9.192517     2.912925            1.0   \n",
              "std       9.135373   403.509100          8.106864     1.024165            0.0   \n",
              "min      18.000000   102.000000          1.000000     1.000000            1.0   \n",
              "25%      30.000000   465.000000          2.000000     2.000000            1.0   \n",
              "50%      36.000000   802.000000          7.000000     3.000000            1.0   \n",
              "75%      43.000000  1157.000000         14.000000     4.000000            1.0   \n",
              "max      60.000000  1499.000000         29.000000     5.000000            1.0   \n",
              "\n",
              "       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
              "count     1470.000000              1470.000000  1470.000000     1470.000000   \n",
              "mean      1024.865306                 2.721769    65.891156        2.729932   \n",
              "std        602.024335                 1.093082    20.329428        0.711561   \n",
              "min          1.000000                 1.000000    30.000000        1.000000   \n",
              "25%        491.250000                 2.000000    48.000000        2.000000   \n",
              "50%       1020.500000                 3.000000    66.000000        3.000000   \n",
              "75%       1555.750000                 4.000000    83.750000        3.000000   \n",
              "max       2068.000000                 4.000000   100.000000        4.000000   \n",
              "\n",
              "          JobLevel  ...  RelationshipSatisfaction  StandardHours  \\\n",
              "count  1470.000000  ...               1470.000000         1470.0   \n",
              "mean      2.063946  ...                  2.712245           80.0   \n",
              "std       1.106940  ...                  1.081209            0.0   \n",
              "min       1.000000  ...                  1.000000           80.0   \n",
              "25%       1.000000  ...                  2.000000           80.0   \n",
              "50%       2.000000  ...                  3.000000           80.0   \n",
              "75%       3.000000  ...                  4.000000           80.0   \n",
              "max       5.000000  ...                  4.000000           80.0   \n",
              "\n",
              "       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "count       1470.000000        1470.000000            1470.000000   \n",
              "mean           0.793878          11.279592               2.799320   \n",
              "std            0.852077           7.780782               1.289271   \n",
              "min            0.000000           0.000000               0.000000   \n",
              "25%            0.000000           6.000000               2.000000   \n",
              "50%            1.000000          10.000000               3.000000   \n",
              "75%            1.000000          15.000000               3.000000   \n",
              "max            3.000000          40.000000               6.000000   \n",
              "\n",
              "       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "count      1470.000000     1470.000000         1470.000000   \n",
              "mean          2.761224        7.008163            4.229252   \n",
              "std           0.706476        6.126525            3.623137   \n",
              "min           1.000000        0.000000            0.000000   \n",
              "25%           2.000000        3.000000            2.000000   \n",
              "50%           3.000000        5.000000            3.000000   \n",
              "75%           3.000000        9.000000            7.000000   \n",
              "max           4.000000       40.000000           18.000000   \n",
              "\n",
              "       YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "count              1470.000000           1470.000000  \n",
              "mean                  2.187755              4.123129  \n",
              "std                   3.222430              3.568136  \n",
              "min                   0.000000              0.000000  \n",
              "25%                   0.000000              2.000000  \n",
              "50%                   1.000000              3.000000  \n",
              "75%                   3.000000              7.000000  \n",
              "max                  15.000000             17.000000  \n",
              "\n",
              "[8 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ba44f5e-4827-4b16-bb09-4d0098e2adcd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeNumber</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>...</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>StandardHours</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>WorkLifeBalance</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.0</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.0</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "      <td>1470.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36.923810</td>\n",
              "      <td>802.485714</td>\n",
              "      <td>9.192517</td>\n",
              "      <td>2.912925</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1024.865306</td>\n",
              "      <td>2.721769</td>\n",
              "      <td>65.891156</td>\n",
              "      <td>2.729932</td>\n",
              "      <td>2.063946</td>\n",
              "      <td>...</td>\n",
              "      <td>2.712245</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.793878</td>\n",
              "      <td>11.279592</td>\n",
              "      <td>2.799320</td>\n",
              "      <td>2.761224</td>\n",
              "      <td>7.008163</td>\n",
              "      <td>4.229252</td>\n",
              "      <td>2.187755</td>\n",
              "      <td>4.123129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.135373</td>\n",
              "      <td>403.509100</td>\n",
              "      <td>8.106864</td>\n",
              "      <td>1.024165</td>\n",
              "      <td>0.0</td>\n",
              "      <td>602.024335</td>\n",
              "      <td>1.093082</td>\n",
              "      <td>20.329428</td>\n",
              "      <td>0.711561</td>\n",
              "      <td>1.106940</td>\n",
              "      <td>...</td>\n",
              "      <td>1.081209</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.852077</td>\n",
              "      <td>7.780782</td>\n",
              "      <td>1.289271</td>\n",
              "      <td>0.706476</td>\n",
              "      <td>6.126525</td>\n",
              "      <td>3.623137</td>\n",
              "      <td>3.222430</td>\n",
              "      <td>3.568136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>465.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>491.250000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>802.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1020.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>43.000000</td>\n",
              "      <td>1157.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1555.750000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>83.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>1499.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2068.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>17.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ba44f5e-4827-4b16-bb09-4d0098e2adcd')\"\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-4ba44f5e-4827-4b16-bb09-4d0098e2adcd 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-4ba44f5e-4827-4b16-bb09-4d0098e2adcd');\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-7d99261e-dfe7-4cd9-9fbe-27aa58060da4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7d99261e-dfe7-4cd9-9fbe-27aa58060da4')\"\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-7d99261e-dfe7-4cd9-9fbe-27aa58060da4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# percentage of the missing values\n",
        "NAN = [(c, df[c].isna().mean()*100) for c in df]\n",
        "NAN = pd.DataFrame(NAN, columns = [\"column_name\", \"percentage\"])\n",
        "print(NAN)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ym7dRFrNdSJe",
        "outputId": "200ae5d3-65c5-4ae5-c032-63efb65b8df4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 column_name  percentage\n",
            "0                        Age         0.0\n",
            "1                  Attrition         0.0\n",
            "2             BusinessTravel         0.0\n",
            "3                  DailyRate         0.0\n",
            "4                 Department         0.0\n",
            "5           DistanceFromHome         0.0\n",
            "6                  Education         0.0\n",
            "7             EducationField         0.0\n",
            "8              EmployeeCount         0.0\n",
            "9             EmployeeNumber         0.0\n",
            "10   EnvironmentSatisfaction         0.0\n",
            "11                    Gender         0.0\n",
            "12                HourlyRate         0.0\n",
            "13            JobInvolvement         0.0\n",
            "14                  JobLevel         0.0\n",
            "15                   JobRole         0.0\n",
            "16           JobSatisfaction         0.0\n",
            "17             MaritalStatus         0.0\n",
            "18             MonthlyIncome         0.0\n",
            "19               MonthlyRate         0.0\n",
            "20        NumCompaniesWorked         0.0\n",
            "21                    Over18         0.0\n",
            "22                  OverTime         0.0\n",
            "23         PercentSalaryHike         0.0\n",
            "24         PerformanceRating         0.0\n",
            "25  RelationshipSatisfaction         0.0\n",
            "26             StandardHours         0.0\n",
            "27          StockOptionLevel         0.0\n",
            "28         TotalWorkingYears         0.0\n",
            "29     TrainingTimesLastYear         0.0\n",
            "30           WorkLifeBalance         0.0\n",
            "31            YearsAtCompany         0.0\n",
            "32        YearsInCurrentRole         0.0\n",
            "33   YearsSinceLastPromotion         0.0\n",
            "34      YearsWithCurrManager         0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Box plot for Monthly Income\n",
        "ax = sns.boxplot(x=df[\"MonthlyIncome\"])\n",
        "plt.show(ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "4_Uhp8tAejX4",
        "outputId": "c4aa171b-ca90-4ab4-af81-6bee919de04e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAggAAAGwCAYAAADMjZ3mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAheklEQVR4nO3deXBV9f3/8dfJdpNgQoCwRRJkcWNVtKS4UqGGyChVWzACouKO1aogxaW4TAuDrdYqtc6ooHWh2gJ2irghcYGIYo3IIiMIQsumQEgoBAJ5//7wl/vl8g5buORyw/Mxw0xytvv55CT3PLlLEpiZCQAAYA8JsR4AAAA4+hAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADhJdd2xurpaa9asUUZGhoIgiOaYAADAEWJmqqioUE5OjhIS9v04QZ0DYc2aNcrNza3r7gAAIIZWr16tNm3a7HN9nQMhIyMjfAOZmZl1PQwAAKhH5eXlys3NDV/H96XOgVDztEJmZiaBAABAnDnQywN4kSIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADhJsR5AQ2BmqqysjPUw6sTMtGPHDklSKBRSEAQxHlH8S01N5esIIO4RCFFQWVmpwsLCWA8DR4mZM2cqLS0t1sMAgMPCUwwAAMDhEYQo23pakSwhjr6su6uU8cUUSVJF9yukxOQYDyg+BdW7dFzpK7EeBgBETRxdyeKDJSTF70U2MTl+xx5jFusBAECU8RQDAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAJynWA9iTmamyslKSlJqaqiAIYjwiAMCh4H684TiqHkGorKxUYWGhCgsLw99gAID4wf14w3FUBQIAADg6EAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAATlKsBwAAaJgKCwuVlJSkxMRE7dixI2JdXl6eysvLtX379vC6hIQEVVdXq0uXLnryySf17LPP6qWXXlKnTp20aNEimZmGDh2q1atXq7i4WAkJCTIzDRkyRKeeeqomTJigrVu3ateuXQqFQho7dqyWLFmil156SYMHD9app56q3/72t9q2bZsSExO1e/fu8L6PP/64br/9dp111lmaO3euHn/8cV144YV69dVXtWPHDuXl5Wn16tVKSUlRUlKSkpOTddppp6m4uLjWudeMrXPnzlq4cKEkKSMjQxUVFbVud/7556ukpCTitmrmO3z48OifnIMQmJnVZcfy8nI1btxYW7ZsUWZmZlQGs337dhUWFkqSZs6cqbS0tKgc90jbc9wVPYZKickxHtEh2F2ljH//VVIcjv1ossfXMZ6+d4Fo27x5sy699NLDPs6TTz6p2267TdXV1Qe1fdOmTbVp06aIZVlZWdqyZYvMTEEQqHHjxiorK9vnvtnZ2XrmmWd03XXX6fvvvz/sOUTL9OnTlZWVFbXjHez1m6cYAABR8+qrr0blOL/85S8POg4kuTiQpLKyMtX8H9jMao2DPffduHGj7r//fm3cuPHQB3wE/eY3v4nJ7R5VTzHs+WBGZWVlDEdyaCLGWrcHZBDv4vR7F4imNWvW6JVXXonKser44PZh3+aXX35Z77d7IAsWLND8+fN15pln1uvtHnQg7NixI+I5pPLy8qgPZs/jR+Mhqpio3iUpJdajQH2r3hX+MG6/dwEctR588EG9/vrrSkiovwf+D/qWxo0bp8aNG4f/5ebmHslxAQCA/6+iokLz5s2r19s86EcQxowZozvvvDP8eXl5edQjIRQKhT+eNm2aUlNTo3r8I6WysvL//teYcFQ9a4P6ssd5j6fvXSBazEz33HOPSktLYz2UBikzM1P5+fn1epsHfTULhUIRF/AjIQiC8Mepqanx+UrwPeaAY0hD+N4FDtPIkSM1ZMiQWA+jQXrggQfq9ekFiXcxAACipE2bNioqKorKsYIY/GcrCAJ17do1Jre9P926dVOPHj3q/XYJBABA1AwcODAqx3niiScO+n/MQRCoadOmbnlWVlb4Yp+QkFDr7xLYc9/s7Gw9/PDDatasWd0HHmVBEOihhx6KyW0TCACAqNn79TdJSUm1Pj2dl5enrKysiHU1QdClSxd16dJFgwcPVkJCgrp06RK+0A8dOlS9e/cObx8EgYYMGaKRI0cqKytLSUk/PHMeCoV09913a8iQIUpISNDgwYN19913q1GjRgqCQElJSRH7tmzZUnfccYeysrJ05513qmXLlho6dGh4fHl5eQqCQKFQSI0aNVJWVlZ4HLWpGVuXLl3CyzIyMva5Xe/evd1tSdKQIUOi+kuSDgW/STEK+E2K4DcpAj+I1/vxYwm/SREAANQZgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcJJiPYA9paamaubMmeGPAQDxhfvxhuOoCoQgCJSWlhbrYQAA6oj78YaDpxgAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4SbEeQEMTVO+SxXoQh2J3Ve0f45AE1btiPQQAiCoCIcqOK30l1kOos4wvpsR6CACAowRPMQAAAIdHEKIgNTVVM2fOjPUw6sTMtGPHDklSKBRSEAQxHlH8S01NjfUQAOCwEQhREASB0tLSYj2MOktPT4/1EAAARxmeYgAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcAgEAADgEAgAAMAhEAAAgEMgAAAAh0AAAAAOgQAAABwCAQAAOAQCAABwCAQAAOAQCAAAwCEQAACAQyAAAACHQAAAAA6BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAATlJddzQzSVJ5eXnUBgMAAI6smut2zXV8X+ocCBUVFZKk3Nzcuh4CAADESEVFhRo3brzP9YEdKCH2obq6WmvWrFFGRoaCIKjzAI925eXlys3N1erVq5WZmRnr4RxRx9JcpWNrvsy14TqW5stco8PMVFFRoZycHCUk7PuVBnV+BCEhIUFt2rSp6+5xJzMzs8F/Q9Y4luYqHVvzZa4N17E0X+Z6+Pb3yEENXqQIAAAcAgEAADgEwgGEQiGNHTtWoVAo1kM54o6luUrH1nyZa8N1LM2XudavOr9IEQAANFw8ggAAABwCAQAAOAQCAABwCAQAAOA0+EAYN26cfvSjHykjI0MtWrTQz372My1dujRim969eysIgoh/N910U8Q2q1atUv/+/ZWenq4WLVpo1KhR2rVrV8Q2xcXF6tGjh0KhkDp27KjJkycf6ek5DzzwgJvLKaecEl5fWVmpESNGqFmzZjruuON0+eWXa/369RHHiJe5nnDCCW6uQRBoxIgRkuL/vH7wwQe6+OKLlZOToyAINH369Ij1Zqbf/OY3at26tdLS0tS3b199/fXXEdts2rRJgwcPVmZmprKysjR8+HBt3bo1YpsFCxbo3HPPVWpqqnJzczVhwgQ3ltdee02nnHKKUlNT1bVrV73xxhv1NteqqiqNHj1aXbt2VaNGjZSTk6OrrrpKa9asiThGbd8P48ePj6u5StLVV1/t5tGvX7+IbeLlvB7MfGv7GQ6CQI888kh4m3g4twdzranP+9+JEyfqhBNOUGpqqvLz8/XJJ58c+qSsgSsoKLBJkybZwoULrbS01C666CLLy8uzrVu3hrc5//zz7frrr7e1a9eG/23ZsiW8fteuXdalSxfr27evff755/bGG29Ydna2jRkzJrzNN998Y+np6XbnnXfa4sWL7YknnrDExER7880363W+Y8eOtc6dO0fM5bvvvguvv+mmmyw3N9dmzZpl8+fPtx//+Md21llnxeVcN2zYEDHPd955xyTZ7NmzzSz+z+sbb7xh9957r02dOtUk2bRp0yLWjx8/3ho3bmzTp0+3L774wi655BJr166dbd++PbxNv379rHv37vbxxx/bhx9+aB07drSioqLw+i1btljLli1t8ODBtnDhQnvllVcsLS3Nnn766fA2c+bMscTERJswYYItXrzY7rvvPktOTrYvv/yyXuZaVlZmffv2tb/97W/21VdfWUlJifXs2dPOOOOMiGO0bdvWHnrooYjzvefPeTzM1cxs2LBh1q9fv4h5bNq0KWKbeDmvBzPfPee5du1ae+655ywIAlu+fHl4m3g4twdzramv+98pU6ZYSkqKPffcc7Zo0SK7/vrrLSsry9avX39Ic2rwgbC3DRs2mCR7//33w8vOP/98u/322/e5zxtvvGEJCQm2bt268LKnnnrKMjMzbceOHWZmdvfdd1vnzp0j9hs0aJAVFBREdwIHMHbsWOvevXut68rKyiw5Odlee+218LIlS5aYJCspKTGz+Jrr3m6//Xbr0KGDVVdXm1nDOq9737FWV1dbq1at7JFHHgkvKysrs1AoZK+88oqZmS1evNgk2aeffhreZubMmRYEgf33v/81M7M///nP1qRJk/B8zcxGjx5tJ598cvjzgQMHWv/+/SPGk5+fbzfeeGNU51ijtovI3j755BOTZN9++214Wdu2be2xxx7b5z7xMtdhw4bZgAED9rlPvJ5Xs4M7twMGDLALLrggYlk8ntu9rzX1ef/bs2dPGzFiRPjz3bt3W05Ojo0bN+6Q5tDgn2LY25YtWyRJTZs2jVj+0ksvKTs7W126dNGYMWO0bdu28LqSkhJ17dpVLVu2DC8rKChQeXm5Fi1aFN6mb9++EccsKChQSUnJkZrKPn399dfKyclR+/btNXjwYK1atUqS9Nlnn6mqqipinKeccory8vLC44y3udbYuXOnXnzxRV177bURfzysIZ3XPa1YsULr1q2LGFvjxo2Vn58fcS6zsrJ05plnhrfp27evEhISNG/evPA25513nlJSUsLbFBQUaOnSpdq8eXN4m6Pta7BlyxYFQaCsrKyI5ePHj1ezZs10+umn65FHHol4aDae5lpcXKwWLVro5JNP1s0336yNGzeG1zXk87p+/XrNmDFDw4cPd+vi7dzufa2pr/vfnTt36rPPPovYJiEhQX379j3kudb5jzXFo+rqav3qV7/S2WefrS5duoSXX3nllWrbtq1ycnK0YMECjR49WkuXLtXUqVMlSevWrYs4YZLCn69bt26/25SXl2v79u1KS0s7klMLy8/P1+TJk3XyySdr7dq1evDBB3Xuuedq4cKFWrdunVJSUtydasuWLQ84j5p1+9umvue6p+nTp6usrExXX311eFlDOq97qxlfbWPbc+wtWrSIWJ+UlKSmTZtGbNOuXTt3jJp1TZo02efXoOYY9a2yslKjR49WUVFRxB+xue2229SjRw81bdpUc+fO1ZgxY7R27Vo9+uijkuJnrv369dNll12mdu3aafny5brnnntUWFiokpISJSYmNtjzKknPP/+8MjIydNlll0Usj7dzW9u1pr7ufzdv3qzdu3fXus1XX311SPM4pgJhxIgRWrhwoT766KOI5TfccEP4465du6p169bq06ePli9frg4dOtT3MA9LYWFh+ONu3bopPz9fbdu21auvvhqzi1l9ePbZZ1VYWKicnJzwsoZ0XvGDqqoqDRw4UGamp556KmLdnXfeGf64W7duSklJ0Y033qhx48bF1a/mveKKK8Ifd+3aVd26dVOHDh1UXFysPn36xHBkR95zzz2nwYMHKzU1NWJ5vJ3bfV1r4s0x8xTDrbfeqn/961+aPXv2Af9MdX5+viRp2bJlkqRWrVq5V5rWfN6qVav9bpOZmRnTC3NWVpZOOukkLVu2TK1atdLOnTtVVlYWsc369esPOI+adfvbJlZz/fbbb/Xuu+/quuuu2+92Dem81oyvtrHtOfYNGzZErN+1a5c2bdoUlfNds76+1MTBt99+q3feeeeAfwI3Pz9fu3bt0sqVKyXF11z31L59e2VnZ0d83zak81rjww8/1NKlSw/4cywd3ed2X9ea+rr/zc7OVmJiYlTm2uADwcx06623atq0aXrvvffcw1C1KS0tlSS1bt1aktSrVy99+eWXET+UNXdQnTp1Cm8za9asiOO888476tWrV5RmUjdbt27V8uXL1bp1a51xxhlKTk6OGOfSpUu1atWq8Djjca6TJk1SixYt1L9///1u15DOa7t27dSqVauIsZWXl2vevHkR57KsrEyfffZZeJv33ntP1dXV4Vjq1auXPvjgA1VVVYW3eeedd3TyySerSZMm4W1i/TWoiYOvv/5a7777rpo1a3bAfUpLS5WQkBB+OD5e5rq3//znP9q4cWPE921DOa97evbZZ3XGGWeoe/fuB9z2aDy3B7rW1Nf9b0pKis4444yIbaqrqzVr1qxDn+shvaQxDt18883WuHFjKy4ujniLzLZt28zMbNmyZfbQQw/Z/PnzbcWKFfb6669b+/bt7bzzzgsfo+atJxdeeKGVlpbam2++ac2bN6/1rSejRo2yJUuW2MSJE2Py1r+77rrLiouLbcWKFTZnzhzr27evZWdn24YNG8zsh7fZ5OXl2XvvvWfz58+3Xr16Wa9eveJyrmY/vDo3Ly/PRo8eHbG8IZzXiooK+/zzz+3zzz83Sfboo4/a559/Hn7l/vjx4y0rK8tef/11W7BggQ0YMKDWtzmefvrpNm/ePPvoo4/sxBNPjHg7XFlZmbVs2dKGDh1qCxcutClTplh6erp7e1hSUpL9/ve/tyVLltjYsWOj/na4/c11586ddskll1ibNm2stLQ04ue45pXdc+fOtccee8xKS0tt+fLl9uKLL1rz5s3tqquuiqu5VlRU2MiRI62kpMRWrFhh7777rvXo0cNOPPFEq6ysDB8jXs7rgeZbY8uWLZaenm5PPfWU2z9ezu2BrjVm9Xf/O2XKFAuFQjZ58mRbvHix3XDDDZaVlRXx7oiD0eADQVKt/yZNmmRmZqtWrbLzzjvPmjZtaqFQyDp27GijRo2KeL+8mdnKlSutsLDQ0tLSLDs72+666y6rqqqK2Gb27Nl22mmnWUpKirVv3z58G/Vp0KBB1rp1a0tJSbHjjz/eBg0aZMuWLQuv3759u91yyy3WpEkTS09Pt0svvdTWrl0bcYx4mauZ2VtvvWWSbOnSpRHLG8J5nT17dq3fu8OGDTOzH97qeP/991vLli0tFApZnz593Ndh48aNVlRUZMcdd5xlZmbaNddcYxUVFRHbfPHFF3bOOedYKBSy448/3saPH+/G8uqrr9pJJ51kKSkp1rlzZ5sxY0a9zXXFihX7/Dmu+Z0Xn332meXn51vjxo0tNTXVTj31VPvd734XcVGNh7lu27bNLrzwQmvevLklJydb27Zt7frrr3d37PFyXg803xpPP/20paWlWVlZmds/Xs7tga41ZvV7//vEE09YXl6epaSkWM+ePe3jjz8+5Dnx554BAIDT4F+DAAAADh2BAAAAHAIBAAA4BAIAAHAIBAAA4BAIAADAIRAAAIBDIAAAAIdAABqYIAg0ffr0fa4vLi5WEATuj8YAwJ4IBCBKrr76agVBoJtuusmtGzFihIIg0NVXXx2123vggQd02mmnRe14+3Og6ADQ8BAIQBTl5uZqypQp2r59e3hZZWWlXn75ZeXl5cVwZABwaAgEIIp69Oih3NxcTZ06Nbxs6tSpysvL0+mnnx5etmPHDt12221q0aKFUlNTdc455+jTTz8Nr695GmDWrFk688wzlZ6errPOOktLly6VJE2ePFkPPvigvvjiCwVBoCAINHny5PD+33//vS699FKlp6frxBNP1D//+c9ax/u///1PmZmZ+vvf/x6xfPr06WrUqJEqKircPitXrlQQBJo6dap+8pOfKD09Xd27d1dJSUnEdnPmzFHv3r2Vnp6uJk2aqKCgQJs3bz6k+b/11ls6/fTTlZaWpgsuuEAbNmzQzJkzdeqppyozM1NXXnmltm3bFt6vurpa48aNU7t27ZSWlqbu3bu7uQE4OAQCEGXXXnutJk2aFP78ueee0zXXXBOxzd13361//OMfev755/Xvf/9bHTt2VEFBgTZt2hSx3b333qs//OEPmj9/vpKSknTttddKkgYNGqS77rpLnTt31tq1a7V27VoNGjQovN+DDz6ogQMHasGCBbrooos0ePBgd2xJatSoka644oqI8UrSpEmT9POf/1wZGRn7nOe9996rkSNHqrS0VCeddJKKioq0a9cuSVJpaan69OmjTp06qaSkRB999JEuvvhi7d69+5Dm/8ADD+jJJ5/U3LlztXr1ag0cOFB//OMf9fLLL2vGjBl6++239cQTT4S3HzdunF544QX95S9/0aJFi3THHXdoyJAhev/99/c5DwD7cMh//xFArYYNG2YDBgywDRs2WCgUspUrV9rKlSstNTXVvvvuOxswYIANGzbMtm7dasnJyfbSSy+F9925c6fl5OTYhAkTzOz//kTuu+++G95mxowZJsm2b99uZmZjx4617t27u3FIsvvuuy/8+datW02SzZw5M+LYmzdvNjOzefPmWWJioq1Zs8bMzNavX29JSUlWXFwcccxp06aZmYX//PIzzzwTXr9o0SKTZEuWLDEzs6KiIjv77LNr/TrVdf7jxo0zSbZ8+fLwshtvvNEKCgrMzKyystLS09Nt7ty5Ebc3fPhwKyoqqnUsAPaNRxCAKGvevLn69++vyZMna9KkSerfv7+ys7PD65cvX66qqiqdffbZ4WXJycnq2bOnlixZEnGsbt26hT9u3bq1JGnDhg0HHMOe+zVq1EiZmZn73K9nz57q3Lmznn/+eUnSiy++qLZt2+q888476NvYe2w1jyDUpq7zb9mypdLT09W+ffuIZTW3uWzZMm3btk0//elPddxxx4X/vfDCC1q+fPl+5wLAS4r1AICG6Nprr9Wtt94qSZo4cWKdj5OcnBz+OAgCST88z34o+9Xsu7/9rrvuOk2cOFG//vWvNWnSJF1zzTXh26vL2NLS0g44xoOx923sb15bt26VJM2YMUPHH398xHahUCgq4wGOJTyCABwB/fr1086dO1VVVaWCgoKIdR06dFBKSormzJkTXlZVVaVPP/1UnTp1OujbSElJCT+nf7iGDBmib7/9Vn/605+0ePFiDRs27LCO161bN82aNavWddGa/946deqkUCikVatWqWPHjhH/cnNz63xc4FjFIwjAEZCYmBh+uDwxMTFiXaNGjXTzzTdr1KhRatq0qfLy8jRhwgRt27ZNw4cPP+jbOOGEE7RixQqVlpaqTZs2ysjIqPP/lJs0aaLLLrtMo0aN0oUXXqg2bdrU6Tg1xowZo65du+qWW27RTTfdpJSUFM2ePVu/+MUvlJ2dHZX57y0jI0MjR47UHXfcoerqap1zzjnasmWL5syZo8zMzMOOHuBYQyAAR0hmZuY+140fP17V1dUaOnSoKioqdOaZZ+qtt95SkyZNDvr4l19+efithmVlZZo0adJh/SKm4cOH6+WXXw6/U+JwnHTSSXr77bd1zz33qGfPnkpLS1N+fr6KiookRWf+tXn44YfVvHlzjRs3Tt98842ysrLUo0cP3XPPPYc9J+BYE5iZxXoQAGLvr3/9q+644w6tWbNGKSkpsR4OgBjjEQTgGLdt2zatXbtW48eP14033kgcAJDEixSBY96ECRN0yimnqFWrVhozZkyshwPgKMFTDAAAwOERBAAA4BAIAADAIRAAAIBDIAAAAIdAAAAADoEAAAAcAgEAADgEAgAAcP4fqOC3WLEM0vgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ay = sns.boxplot(x = \"Gender\", y = \"MonthlyIncome\", data=df)\n",
        "plt.show(ay)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "uFAezrtAidOH",
        "outputId": "cf65fbb4-10a3-4f73-a35e-6d6f3f8b6d06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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t2l2zNi0tTT4+Po4bLMbExOjFF1/UlStXVK1aNUlSYmKimjdvrtq1aztqkpKSNG7cOMd2EhMTFRMT4/6dqeIMw1Bubq6n23BJ27ZtZbPZSp30b7PZ1LZtW12+fNkDnZVfQEAAE11RpVksFj3yyCMlLlkjSQ8//DDvD0jycGjKzs7WkSNHHMvHjh1TWlqa6tSp4/j4tt1u1/vvv6958+aVeH5ycrJ2796t+++/XzVr1lRycrLGjx+vJ5980hGIBg0apOnTpys+Pl6TJ0/WwYMHtXDhQi1YsMCxnbFjx6pLly6aN2+e4uLitGLFCqWkpDhdlgDukZubW+YnJisqu92uuLg4T7fhsg0bNigwMNDTbQAeU1RUpEWLFpU6tmjRIvXo0UM+PhXi5AxuII/+H5CSkqL27durffv2kn6an9S+fXtNnTrVUVP8qaSBAweWeL7VatWKFSvUpUsXtW7dWn/84x81fvx4p7ATHBysTZs26dixY7rjjjv0/PPPa+rUqY7LDUjSPffco+XLl+v1119Xu3bt9H//939as2aN2rRpcwP3HgDgLXbt2lXmvSV37dp1kzuCN7IYfE7aLex2u4KDg5WVlSWbzebpdrxWRT49V+z8+fMaMmSIJMnHx0fLly9XcHCwh7sqH07Poao7evRoqZe0KfbWW2+pSZMmN7Ej3CzX8/e7QsxpQuVhsVgq/Gmg4vlykvTYY4/xgQGgEijrEjPF44QmcIIWKIfiI04AKjYuOQAzCE0AgCqP26jADEITAKDKu9b0Xqb/QiI0AQCgM2fOlGscVQOhCQBQ5XF6DmYQmgAAVV5MTIz8/f1LHfP39+cOEZBEaAIAQJLk51f6VXiuth5VD6EJAFDl7d69Wzk5OaWO5eTkaPfu3Te5I3gjQhMAoMqLjo5WUFBQqWNBQUGKjo6+yR3BGxGaAABVnsViUXh4eKljt956KxPBIYnQBACAMjIy9NVXX5U6lp6eroyMjJvcEbwRoQkAUOVFRESUeXouIiLiJncEb0RoAgBUeRkZGcrOzi51LDs7myNNkERoAgAAMIXQBACo8iIjIxUVFVXqWNu2bRUZGXmTO4I34opdAAC3MAxDubm5nm7DZePGjdOIESOcbs5rsVg0duzYCr1fAQEBfPrPTQhNAAC3yM3NVa9evTzdhlsZhqH4+HhPt1EuGzZsUGBgoKfbqBQ4PQcAAGACR5oAAG4REBCgDRs2eLqNcsnNzdXDDz8sSfqf//kfderUycMdlV9AQICnW6g0CE0AALewWCyV6jRQp06dKtX+oPw4PQcAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEzwamnbs2KE+ffooPDxcFotFa9ascRofOnSoLBaL06Nnz55ONRcuXNATTzwhm82mWrVqKT4+XtnZ2U41+/fvV6dOnRQQEKCIiAjNmTOnRC/vv/++WrRooYCAAEVFRWn9+vVu318AAFBxeTQ0Xbp0Se3atdPixYuvWtOzZ0+dPXvW8Xjvvfecxp944gkdOnRIiYmJWrt2rXbs2KFnnnnGMW6329WjRw9FRkYqNTVVc+fO1bRp0/T66687anbu3KmBAwcqPj5ee/fuVd++fdW3b18dPHjQ/TsNAAAqJD9PvnivXr3Uq1evMmusVqtCQ0NLHfviiy+0ceNG7dmzR3feeack6a9//at+85vf6E9/+pPCw8O1bNky5efn6+2335a/v79at26ttLQ0zZ8/3xGuFi5cqJ49e2rixImSpJkzZyoxMVGLFi3SkiVLSn3tvLw85eXlOZbtdvt17z8AAKg4vH5O07Zt21S/fn01b95co0aN0vfff+8YS05OVq1atRyBSZK6d+8uHx8f7d6921HTuXNn+fv7O2piY2OVnp6uH374wVHTvXt3p9eNjY1VcnLyVftKSEhQcHCw4xEREeGW/QUAAN7Jq0NTz5499Y9//ENJSUl65ZVXtH37dvXq1UuFhYWSpMzMTNWvX9/pOX5+fqpTp44yMzMdNSEhIU41xcvXqikeL82UKVOUlZXleJw8ebJ8OwsAALyaR0/PXcuAAQMcX0dFRalt27Zq0qSJtm3bpm7dunmws59OG1qtVo/2AAAAbh6vPtL0S7fddptuueUWHTlyRJIUGhqq8+fPO9UUFBTowoULjnlQoaGhOnfunFNN8fK1aq42lwoAAFQ9FSo0nTp1St9//73CwsIkSTExMbp48aJSU1MdNVu2bFFRUZGio6MdNTt27NCVK1ccNYmJiWrevLlq167tqElKSnJ6rcTERMXExNzoXQIAABWER0NTdna20tLSlJaWJkk6duyY0tLSlJGRoezsbE2cOFG7du3S8ePHlZSUpIceekhNmzZVbGysJKlly5bq2bOnnn76aX322Wf69NNPNWbMGA0YMEDh4eGSpEGDBsnf31/x8fE6dOiQVq5cqYULF2rChAmOPsaOHauNGzdq3rx5+vLLLzVt2jSlpKRozJgxN/17AgAAvJThQVu3bjUklXgMGTLEyMnJMXr06GHUq1fPqFatmhEZGWk8/fTTRmZmptM2vv/+e2PgwIFGUFCQYbPZjGHDhhk//vijU82+ffuM++67z7Barcatt95qzJ49u0Qvq1atMpo1a2b4+/sbrVu3NtatW3dd+5KVlWVIMrKysq7/G4EKJScnx+jSpYvRpUsXIycnx9PtAHAj3t9Vz/X8/bYYhmF4MLNVGna7XcHBwcrKypLNZvN0O7iBLl++7Li+2IYNGxQYGOjhjgC4C+/vqud6/n5XqDlNAAAAnkJoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJrgcmj7++GM9+eSTiomJ0enTpyVJ//znP/XJJ5+4rTkAAABv4VJo+te//qXY2FgFBgZq7969ysvLkyRlZWVp1qxZbm0QAADAG7gUmv7whz9oyZIleuONN1StWjXH+nvvvVeff/6525oDAADwFi6FpvT0dHXu3LnE+uDgYF28eLG8PQEAAHgdl0JTaGiojhw5UmL9J598ottuu63cTQEAAHgbl0LT008/rbFjx2r37t2yWCw6c+aMli1bphdeeEGjRo1yd48AAAAe5+fKk37/+9+rqKhI3bp1U05Ojjp37iyr1aoXXnhBzz33nLt7BAAA8DiXQpPFYtGLL76oiRMn6siRI8rOzlarVq0UFBTk7v4AAAC8gkuhqZi/v79atWrlrl4AAAC8lkuhKTc3V3/961+1detWnT9/XkVFRU7jXHYAAABUNi5NBI+Pj9ecOXMUGRmp3r1766GHHnJ6mLVjxw716dNH4eHhslgsWrNmjWPsypUrmjx5sqKiolSjRg2Fh4dr8ODBOnPmjNM2GjVqJIvF4vSYPXu2U83+/fvVqVMnBQQEKCIiQnPmzCnRy/vvv68WLVooICBAUVFRWr9+/fV9UwAAQKXm0pGmtWvXav369br33nvL9eKXLl1Su3btNHz4cD3yyCNOYzk5Ofr888/1v//7v2rXrp1++OEHjR07Vg8++KBSUlKcamfMmKGnn37asVyzZk3H13a7XT169FD37t21ZMkSHThwQMOHD1etWrX0zDPPSJJ27typgQMHKiEhQb1799by5cvVt29fff7552rTpk259hEAAFQOLoWmW2+91SmYuKpXr17q1atXqWPBwcFKTEx0Wrdo0SLdfffdysjIUMOGDR3ra9asqdDQ0FK3s2zZMuXn5+vtt9+Wv7+/WrdurbS0NM2fP98RmhYuXKiePXtq4sSJkqSZM2cqMTFRixYt0pIlS8q9nwAAoOJz6fTcvHnzNHnyZJ04ccLd/ZQpKytLFotFtWrVclo/e/Zs1a1bV+3bt9fcuXNVUFDgGEtOTlbnzp3l7+/vWBcbG6v09HT98MMPjpru3bs7bTM2NlbJyclX7SUvL092u93pAQAAKi+XjjTdeeedys3N1W233abq1as73X9Oki5cuOCW5n4uNzdXkydP1sCBA2Wz2Rzrf/e736lDhw6qU6eOdu7cqSlTpujs2bOaP3++JCkzM1ONGzd22lZISIhjrHbt2srMzHSs+3lNZmbmVftJSEjQ9OnT3bV7AADAy7kUmgYOHKjTp09r1qxZCgkJkcVicXdfTq5cuaLHHntMhmHotddecxqbMGGC4+u2bdvK399fzz77rBISEmS1Wm9YT1OmTHF6bbvdroiIiBv2egAAwLNcCk07d+5UcnKy2rVr5+5+SigOTCdOnNCWLVucjjKVJjo6WgUFBTp+/LiaN2+u0NBQnTt3zqmmeLl4HtTVaq42T0qSrFbrDQ1lAADAu7g0p6lFixa6fPmyu3spoTgwff3119q8ebPq1q17zeekpaXJx8dH9evXlyTFxMRox44dunLliqMmMTFRzZs3V+3atR01SUlJTttJTExUTEyMG/cGAABUZC6FptmzZ+v555/Xtm3b9P3337s8ITo7O1tpaWlKS0uTJB07dkxpaWnKyMjQlStX1L9/f6WkpGjZsmUqLCxUZmamMjMzlZ+fL+mnCdx//vOftW/fPn3zzTdatmyZxo8fryeffNIRiAYNGiR/f3/Fx8fr0KFDWrlypRYuXOh0am3s2LHauHGj5s2bpy+//FLTpk1TSkqKxowZ48q3BwAAVEaGCywWi2GxWAwfHx+nR/E6s7Zu3WpIKvEYMmSIcezYsVLHJBlbt241DMMwUlNTjejoaCM4ONgICAgwWrZsacyaNcvIzc11ep19+/YZ9913n2G1Wo1bb73VmD17doleVq1aZTRr1szw9/c3Wrdubaxbt+66vidZWVmGJCMrK+u6noeKJycnx+jSpYvRpUsXIycnx9PtAHAj3t9Vz/X8/XZpTtPWrVvdENekrl27yjCMq46XNSZJHTp00K5du675Om3bttXHH39cZs2jjz6qRx999JrbAgAAVZNLoalLly7u7gMAAMCruRSaJOnixYt666239MUXX0iSWrdureHDhys4ONhtzQEAAHgLlyaCp6SkqEmTJlqwYIEuXLigCxcuaP78+WrSpIk+//xzd/cIAADgcS4daRo/frwefPBBvfHGG/Lz+2kTBQUFGjFihMaNG6cdO3a4tUkAAABPcyk0paSkOAUmSfLz89OkSZN05513uq05AAAAb+HS6TmbzaaMjIwS60+ePKmaNWuWuykAAABv41JoevzxxxUfH6+VK1fq5MmTOnnypFasWKERI0Zo4MCB7u4RAADA41w6PfenP/1JFotFgwcPVkFBgSSpWrVqGjVqlGbPnu3WBgEAALyBS6HJ399fCxcuVEJCgo4ePSpJatKkiapXr+7W5gAAALyFS6EpKytLhYWFqlOnjqKiohzrL1y4ID8/P9lsNrc1CAAA4A1cmtM0YMAArVixosT6VatWacCAAeVuCgAAwNu4FJp2796t+++/v8T6rl27avfu3eVuCgAAwNu4FJry8vIcE8B/7sqVK7p8+XK5mwIAAPA2LoWmu+++W6+//nqJ9UuWLNEdd9xR7qYAAAC8jUsTwf/whz+oe/fu2rdvn7p16yZJSkpK0p49e7Rp0ya3NggAAOANXDrSdO+99yo5OVkRERFatWqVPvzwQzVt2lT79+9Xp06d3N0jAACAx7l0pEmSbr/9di1btsydvQAAAHgtl0NTUVGRjhw5ovPnz6uoqMhprHPnzuVuDAAAwJu4FJp27dqlQYMG6cSJEzIMw2nMYrGosLDQLc0BAAB4C5dC08iRI3XnnXdq3bp1CgsLk8VicXdfAAAAXsWl0PT111/r//7v/9S0aVN39wMAAOCVXPr0XHR0tI4cOeLuXgAAALyWS0eannvuOT3//PPKzMxUVFSUqlWr5jTetm1btzQHAADgLVwKTf369ZMkDR8+3LHOYrHIMAwmggMAgErJpdB07Ngxd/cBAADg1VwKTZGRke7uAwAAwKtdV2j64IMPTNU9+OCDLjUDAADgra4rNPXt2/eaNcxpAgAAldF1haZf3i4FAACgqnDpOk2XLl1ydx8AAABezaXQFBISouHDh+uTTz5xdz8AAABeyaXQ9O677+rChQt64IEH1KxZM82ePVtnzpxxd28AAABew6XQ1LdvX61Zs0anT5/WyJEjtXz5ckVGRqp3795avXq1CgoK3N0nAACAR7kUmorVq1dPEyZM0P79+zV//nxt3rxZ/fv3V3h4uKZOnaqcnJwyn79jxw716dNH4eHhslgsWrNmjdO4YRiaOnWqwsLCFBgYqO7du+vrr792qrlw4YKeeOIJ2Ww21apVS/Hx8crOznaq2b9/vzp16qSAgABFRERozpw5JXp5//331aJFCwUEBCgqKkrr16937ZsCAAAqpXKFpnPnzmnOnDlq1aqVfv/736t///5KSkrSvHnztHr16mteouDSpUtq166dFi9eXOr4nDlz9Je//EVLlizR7t27VaNGDcXGxio3N9dR88QTT+jQoUNKTEzU2rVrtWPHDj3zzDOOcbvdrh49eigyMlKpqamaO3eupk2bptdff91Rs3PnTg0cOFDx8fHau3ev+vbtq759++rgwYPl+fYAAIDKxHDBv/71L6N3795GtWrVjHbt2hl//etfjR9++MGp5siRI0a1atVMb1OS8e9//9uxXFRUZISGhhpz5851rLt48aJhtVqN9957zzAMwzh8+LAhydizZ4+jZsOGDYbFYjFOnz5tGIZhvPrqq0bt2rWNvLw8R83kyZON5s2bO5Yfe+wxIy4uzqmf6Oho49lnnzXdf1ZWliHJyMrKMv0cVEw5OTlGly5djC5duhg5OTmebgeAG/H+rnqu5++3S0eahg0bpvDwcH366adKS0vTmDFjVKtWLaea8PBwvfjiiy6HuWPHjikzM1Pdu3d3rAsODlZ0dLSSk5MlScnJyapVq5buvPNOR0337t3l4+Oj3bt3O2o6d+4sf39/R01sbKzS09P1ww8/OGp+/jrFNcWvU5q8vDzZ7XanBwAAqLxcuvfc2bNnVb169TJrAgMD9fLLL7vUlCRlZmZK+unyBj8XEhLiGMvMzFT9+vWdxv38/FSnTh2nmsaNG5fYRvFY7dq1lZmZWebrlCYhIUHTp093Yc8AAEBFZDo0/fJISllHVmw2m+sdVRBTpkzRhAkTHMt2u10REREe7AgAANxIpkNTrVq1ZLFYyqwxDMNt954LDQ2V9NNk87CwMMf6c+fO6fbbb3fUnD9/3ul5BQUFunDhguP5oaGhOnfunFNN8fK1aorHS2O1WmW1Wl3YMwAAUBGZDk1bt269kX2U0LhxY4WGhiopKckRkux2u3bv3q1Ro0ZJkmJiYnTx4kWlpqbqjjvukCRt2bJFRUVFio6OdtS8+OKLunLliqpVqyZJSkxMVPPmzVW7dm1HTVJSksaNG+d4/cTERMXExNykvQUAAN7OdGjq0qWL2188OztbR44ccSwfO3ZMaWlpqlOnjho2bKhx48bpD3/4g371q1+pcePG+t///V+Fh4c7LmXQsmVL9ezZU08//bSWLFmiK1euaMyYMRowYIDCw8MlSYMGDdL06dMVHx+vyZMn6+DBg1q4cKEWLFjgeN2xY8eqS5cumjdvnuLi4rRixQqlpKQ4XZYAAABUbS5NBJekixcv6rPPPtP58+dVVFTkNDZ48GBT20hJSdH999/vWC6eIzRkyBAtXbpUkyZN0qVLl/TMM8/o4sWLuu+++7Rx40YFBAQ4nrNs2TKNGTNG3bp1k4+Pj/r166e//OUvjvHg4GBt2rRJo0eP1h133KFbbrlFU6dOdbqW0z333KPly5frpZde0v/8z//oV7/6ldasWaM2bdq49L0BAACVj8UwDON6n/Thhx/qiSeeUHZ2tmw2m9NcJ4vFogsXLri1yYrAbrcrODhYWVlZVWIifFV2+fJl9erVS5K0YcMGBQYGergjAO7C+7vquZ6/3y5dp+n555/X8OHDlZ2drYsXL+qHH35wPKpiYAIAAJWfS6Hp9OnT+t3vfnfNazUBAABUFi6FptjYWKWkpLi7FwAAAK9leiL4Bx984Pg6Li5OEydO1OHDhxUVFeX4KH+xBx980H0dAgAAeAHToan4Y/4/N2PGjBLr3HVxSwAAAG9iOjT98rICAAAAVYlLc5r+8Y9/KC8vr8T6/Px8/eMf/yh3UwAAAN7GpdA0bNgwZWVllVj/448/atiwYeVuCgAAwNu4FJqKb8z7S6dOnVJwcHC5mwIAAPA213Ublfbt28tischisahbt27y8/vv0wsLC3Xs2DH17NnT7U0CAAB42nWFpuJP0KWlpSk2NlZBQUGOMX9/fzVq1Ej9+vVza4MAAADe4LpC08svvyxJatSokR5//HGnG+cCAABUZtcVmooNGTJE0k+fljt//nyJyxE0bNiw/J0BAAB4EZdC09dff63hw4dr586dTuuLJ4hzcUsAAFDZuBSahg4dKj8/P61du1ZhYWGlfpIO7mUYhnJzcz3dBiSnnwM/E+8REBDA7yIAN5RLoSktLU2pqalq0aKFu/vBVeTm5qpXr16ebgO/8PDDD3u6Bfx/GzZsUGBgoKfbAFCJuXSdplatWum7775zdy8AAABey6UjTa+88oomTZqkWbNmKSoqStWqVXMat9lsbmkOpcu+faAMH5d+dHAHw5CKCn762sdP4pSQx1iKChSU9p6n2wBQRbj0l7d79+6SpG7dujmtZyL4zWH4+Em+1a5diBvI39MNQJLh6QYAVCkuhaatW7e6uw8AAACv5lJo6tKli7v7AAAA8GouT4y5ePGi3nrrLX3xxReSpNatW2v48OHcsBcAAFRKLn16LiUlRU2aNNGCBQt04cIFXbhwQfPnz1eTJk30+eefu7tHAAAAj3PpSNP48eP14IMP6o033pCf30+bKCgo0IgRIzRu3Djt2LHDrU0CAAB4mkuhKSUlxSkwSZKfn58mTZqkO++8023NAUBVwBX/vQdX/PdO3nLFf5dCk81mU0ZGRokrgp88eVI1a9Z0S2MAUFVwxX/vxBX/vYe3XPHfpTlNjz/+uOLj47Vy5UqdPHlSJ0+e1IoVKzRixAgNHDjQ3T0CAAB4nEtHmv70pz/JYrFo8ODBKigokGEY8vf316hRozR79mx39wgAVcai+y7I6stlOz3FMKT8op++9vfhgv+elFdo0ZhP6ni6DScuhSZ/f38tXLhQCQkJOnr0qCSpSZMmql69ulubA4CqxupryOrr6S6qtgBPN4D/z/v+8XBdoWn48OGm6t5++22XmgEAAPBW1xWali5dqsjISLVv316G4X0JEAAA4Ea5rtA0atQovffeezp27JiGDRumJ598UnXqeNf5RgAAgBvhuj49t3jxYp09e1aTJk3Shx9+qIiICD322GP66KOPOPIEAAAqteu+5IDVatXAgQOVmJiow4cPq3Xr1vrtb3+rRo0aKTs72+0NNmrUSBaLpcRj9OjRkqSuXbuWGBs5cqTTNjIyMhQXF6fq1aurfv36mjhxogoKCpxqtm3bpg4dOshqtapp06ZaunSp2/cFAABUXC7fsFeSfHx8ZLFYZBiGCgsL3dWTkz179jht++DBg/r1r3+tRx991LHu6aef1owZMxzLP/8UX2FhoeLi4hQaGqqdO3fq7NmzGjx4sKpVq6ZZs2ZJko4dO6a4uDiNHDlSy5YtU1JSkkaMGKGwsDDFxsbekP0CAAAVy3UfacrLy9N7772nX//612rWrJkOHDigRYsWKSMjQ0FBQW5vsF69egoNDXU81q5dqyZNmqhLly6OmurVqzvV2Gw2x9imTZt0+PBhvfvuu7r99tvVq1cvzZw5U4sXL1Z+fr4kacmSJWrcuLHmzZunli1basyYMerfv78WLFjg9v0BAAAV03WFpt/+9rcKCwvT7Nmz1bt3b508eVLvv/++fvOb38jHx6WLi1+X/Px8vfvuuxo+fLjTPWiWLVumW265RW3atNGUKVOUk5PjGEtOTlZUVJRCQkIc62JjY2W323Xo0CFHTffu3Z1eKzY2VsnJyVftJS8vT3a73ekBAAAqr+s6PbdkyRI1bNhQt912m7Zv367t27eXWrd69Wq3NPdLa9as0cWLFzV06FDHukGDBikyMlLh4eHav3+/Jk+erPT0dEcPmZmZToFJkmM5MzOzzBq73a7Lly+Xer+bhIQETZ8+3Z27BwAAvNh1habBgwd79C7Db731lnr16qXw8HDHumeeecbxdVRUlMLCwtStWzcdPXpUTZo0uWG9TJkyRRMmTHAs2+12RURE3LDXAwAAnnXdF7f0lBMnTmjz5s3XPIoVHR0tSTpy5IiaNGmi0NBQffbZZ041586dkySFhoY6/lu87uc1NpvtqndVtlqtslqtLu0LAACoeG78RCQ3eeedd1S/fn3FxcWVWZeWliZJCgsLkyTFxMTowIEDOn/+vKMmMTFRNptNrVq1ctQkJSU5bScxMVExMTFu3AMAAFCRVYjQVFRUpHfeeUdDhgyRn99/D44dPXpUM2fOVGpqqo4fP64PPvhAgwcPVufOndW2bVtJUo8ePdSqVSs99dRT2rdvnz766CO99NJLGj16tONI0ciRI/XNN99o0qRJ+vLLL/Xqq69q1apVGj9+vEf2FwAAeJ8KEZo2b96sjIyMEjcM9vf31+bNm9WjRw+1aNFCzz//vPr166cPP/zQUePr66u1a9fK19dXMTExevLJJzV48GCn6zo1btxY69atU2Jiotq1a6d58+bpzTff5BpNAADAoVwXt7xZevToUeptWiIiIq76Cb6fi4yM1Pr168us6dq1q/bu3etyjwAAoHKrEEeaAAAAPI3QBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGCCV4emadOmyWKxOD1atGjhGM/NzdXo0aNVt25dBQUFqV+/fjp37pzTNjIyMhQXF6fq1aurfv36mjhxogoKCpxqtm3bpg4dOshqtapp06ZaunTpzdi962IYxn8XCq/w4MGj+FHaewQAbgA/TzdwLa1bt9bmzZsdy35+/215/PjxWrdund5//30FBwdrzJgxeuSRR/Tpp59KkgoLCxUXF6fQ0FDt3LlTZ8+e1eDBg1WtWjXNmjVLknTs2DHFxcVp5MiRWrZsmZKSkjRixAiFhYUpNjb25u5sGfLy8hxf19y3woOdAN4pLy9P1atX93QbACoxrw9Nfn5+Cg0NLbE+KytLb731lpYvX64HHnhAkvTOO++oZcuW2rVrlzp27KhNmzbp8OHD2rx5s0JCQnT77bdr5syZmjx5sqZNmyZ/f38tWbJEjRs31rx58yRJLVu21CeffKIFCxaUGZry8vKcgozdbnfzngMAAG/i9aHp66+/Vnh4uAICAhQTE6OEhAQ1bNhQqampunLlirp37+6obdGihRo2bKjk5GR17NhRycnJioqKUkhIiKMmNjZWo0aN0qFDh9S+fXslJyc7baO4Zty4cWX2lZCQoOnTp7t1X8titVodX//YboDkW+2mvTbgtQqvOI68/vw9AgA3gleHpujoaC1dulTNmzfX2bNnNX36dHXq1EkHDx5UZmam/P39VatWLafnhISEKDMzU5KUmZnpFJiKx4vHyqqx2+26fPmyAgMDS+1typQpmjBhgmPZbrcrIiKiXPtbFovF8t8F32qEJuAXnN4jFczP52PlFXqwEcCL/Py94C1zFr06NPXq1cvxddu2bRUdHa3IyEitWrXqqmHmZrFarfzLFoBb/PxU/5hP6nqwE8A7ecucRa/+9Nwv1apVS82aNdORI0cUGhqq/Px8Xbx40anm3LlzjjlQoaGhJT5NV7x8rRqbzebxYAYAALyHVx9p+qXs7GwdPXpUTz31lO644w5Vq1ZNSUlJ6tevnyQpPT1dGRkZiomJkSTFxMToj3/8o86fP6/69etLkhITE2Wz2dSqVStHzfr1651eJzEx0bENALjRfn7UetF938vq68FmAC+RV/jfI6/ecmbHq0PTCy+8oD59+igyMlJnzpzRyy+/LF9fXw0cOFDBwcGKj4/XhAkTVKdOHdlsNj333HOKiYlRx44dJUk9evRQq1at9NRTT2nOnDnKzMzUSy+9pNGjRzt+ACNHjtSiRYs0adIkDR8+XFu2bNGqVau0bt06T+46gCrk5/OxrL4iNAG/4C1zFr06NJ06dUoDBw7U999/r3r16um+++7Trl27VK9ePUnSggUL5OPjo379+ikvL0+xsbF69dVXHc/39fXV2rVrNWrUKMXExKhGjRoaMmSIZsyY4ahp3Lix1q1bp/Hjx2vhwoVq0KCB3nzzTa+6RhMAAPA8rw5NK1aUfRHHgIAALV68WIsXL75qTWRkZInTb7/UtWtX7d2716UeAQBA1VChJoIDAAB4CqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABggp+nG8D1sxQVyPB0E1WZYUhFBT997eMnWSye7acKsxT/HADgJiA0VUBBae95ugUAAKocTs8BAACYwJGmCiIgIEAbNmzwdBuQlJubq4cffliS9O9//1sBAQEe7giS+DkAuOEITRWExWJRYGCgp9vALwQEBPBzgVvlFVokZi16jGFI+UU/fe3vw5RFT/rpveBdCE0A4EXGfFLH0y0AuArmNAEAAJjAkSYA8DDmLHoP5ix6J2/5OXh1aEpISNDq1av15ZdfKjAwUPfcc49eeeUVNW/e3FHTtWtXbd++3el5zz77rJYsWeJYzsjI0KhRo7R161YFBQVpyJAhSkhIkJ/ff3d/27ZtmjBhgg4dOqSIiAi99NJLGjp06A3fRwBgzqJ3Ys4ifsmrT89t375do0eP1q5du5SYmKgrV66oR48eunTpklPd008/rbNnzzoec+bMcYwVFhYqLi5O+fn52rlzp/7+979r6dKlmjp1qqPm2LFjiouL0/3336+0tDSNGzdOI0aM0EcffXTT9hUAAHg3rz7StHHjRqflpUuXqn79+kpNTVXnzp0d66tXr67Q0NBSt7Fp0yYdPnxYmzdvVkhIiG6//XbNnDlTkydP1rRp0+Tv768lS5aocePGmjdvniSpZcuW+uSTT7RgwQLFxsbeuB0EAAAVhlcfafqlrKwsSVKdOs6fLlm2bJluueUWtWnTRlOmTFFOTo5jLDk5WVFRUQoJCXGsi42Nld1u16FDhxw13bt3d9pmbGyskpOTr9pLXl6e7Ha70wMAAFReXn2k6eeKioo0btw43XvvvWrTpo1j/aBBgxQZGanw8HDt379fkydPVnp6ulavXi1JyszMdApMkhzLmZmZZdbY7XZdvny51HPaCQkJmj59ulv3EQAAeK8KE5pGjx6tgwcP6pNPPnFa/8wzzzi+joqKUlhYmLp166ajR4+qSZMmN6yfKVOmaMKECY5lu92uiIiIG/Z6AADAsyrE6bkxY8Zo7dq12rp1qxo0aFBmbXR0tCTpyJEjkqTQ0FCdO3fOqaZ4uXge1NVqbDbbVT85YbVaZbPZnB4AAKDy8urQZBiGxowZo3//+9/asmWLGjdufM3npKWlSZLCwsIkSTExMTpw4IDOnz/vqElMTJTNZlOrVq0cNUlJSU7bSUxMVExMjJv2BAAAVHReHZpGjx6td999V8uXL1fNmjWVmZmpzMxMXb58WZJ09OhRzZw5U6mpqTp+/Lg++OADDR48WJ07d1bbtm0lST169FCrVq301FNPad++ffroo4/00ksvafTo0bJarZKkkSNH6ptvvtGkSZP05Zdf6tVXX9WqVas0fvx4j+07AADwLl4dml577TVlZWWpa9euCgsLczxWrlwpSfL399fmzZvVo0cPtWjRQs8//7z69eunDz/80LENX19frV27Vr6+voqJidGTTz6pwYMHa8aMGY6axo0ba926dUpMTFS7du00b948vfnmm1xuAAAAOHj1RHDDKPtO3xERESWuBl6ayMhIrV+/vsyarl27au/evdfVHwAAqDq8+kgTAACAtyA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAAT/DzdAKoWwzCUm5vr6TbK5ef9V/R9KRYQECCLxeLpNgDAqxGacFPl5uaqV69enm7DbR5++GFPt+AWGzZsUGBgoKfbAACvRmgCALgFR5K9E0eS3cdiGIbh6SYqA7vdruDgYGVlZclms3m6Ha9VGX6pGoahvLw8SZLVaq0Uv4z4pQp3uHz5cqU6klxZcCS5bNfz95sjTbipLBZLpXjzVq9e3dMtAABuMkITAMAtAgICtGHDBk+3US6V9Ugy3IPQBABwC44ko7LjOk0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYIKfpxuoLAzDkCTZ7XYPdwIAAMwq/rtd/He8LIQmN/nxxx8lSRERER7uBAAAXK8ff/xRwcHBZdZYDDPRCtdUVFSkM2fOqGbNmrJYLJ5uBzeY3W5XRESETp48KZvN5ul2ALgR7++qxTAM/fjjjwoPD5ePT9mzljjS5CY+Pj5q0KCBp9vATWaz2filClRSvL+rjmsdYSrGRHAAAAATCE0AAAAmEJoAF1itVr388suyWq2ebgWAm/H+xtUwERwAAMAEjjQBAACYQGgCAAAwgdAEAABgAqEJuIkaNWqkP//5z55uA8B1On78uCwWi9LS0jzdCjyI0IRKa+jQobJYLCUeR44c8XRrAG6C4t8BI0eOLDE2evRoWSwWDR069OY3hgqL0IRKrWfPnjp79qzTo3Hjxp5uC8BNEhERoRUrVujy5cuOdbm5uVq+fLkaNmzowc5QERGaUKlZrVaFhoY6PXx9ffWf//xHHTp0UEBAgG677TZNnz5dBQUFjudZLBb97W9/U+/evVW9enW1bNlSycnJOnLkiLp27aoaNWronnvu0dGjRx3POXr0qB566CGFhIQoKChId911lzZv3lxmfxcvXtSIESNUr1492Ww2PfDAA9q3b98N+34AVU2HDh0UERGh1atXO9atXr1aDRs2VPv27R3rNm7cqPvuu0+1atVS3bp11bt3b6f3d2kOHjyoXr16KSgoSCEhIXrqqaf03Xff3bB9gecRmlDlfPzxxxo8eLDGjh2rw4cP629/+5uWLl2qP/7xj051M2fO1ODBg5WWlqYWLVpo0KBBevbZZzVlyhSlpKTIMAyNGTPGUZ+dna3f/OY3SkpK0t69e9WzZ0/16dNHGRkZV+3l0Ucf1fnz57VhwwalpqaqQ4cO6tatmy5cuHDD9h+oaoYPH6533nnHsfz2229r2LBhTjWXLl3ShAkTlJKSoqSkJPn4+Ojhhx9WUVFRqdu8ePGiHnjgAbVv314pKSnauHGjzp07p8cee+yG7gs8zAAqqSFDhhi+vr5GjRo1HI/+/fsb3bp1M2bNmuVU+89//tMICwtzLEsyXnrpJcdycnKyIcl46623HOvee+89IyAgoMweWrdubfz1r391LEdGRhoLFiwwDMMwPv74Y8Nmsxm5ublOz2nSpInxt7/97br3F4CzIUOGGA899JBx/vx5w2q1GsePHzeOHz9uBAQEGN9++63x0EMPGUOGDCn1ud9++60hyThw4IBhGIZx7NgxQ5Kxd+9ewzAMY+bMmUaPHj2cnnPy5ElDkpGenn4jdwse5OfRxAbcYPfff79ee+01x3KNGjXUtm1bffrpp05HlgoLC5Wbm6ucnBxVr15dktS2bVvHeEhIiCQpKirKaV1ubq7sdrtsNpuys7M1bdo0rVu3TmfPnlVBQYEuX7581SNN+/btU3Z2turWreu0/vLly9c8LQDAvHr16ikuLk5Lly6VYRiKi4vTLbfc4lTz9ddfa+rUqdq9e7e+++47xxGmjIwMtWnTpsQ29+3bp61btyooKKjE2NGjR9WsWbMbszPwKEITKrUaNWqoadOmTuuys7M1ffp0PfLIIyXqAwICHF9Xq1bN8bXFYrnquuJfri+88IISExP1pz/9SU2bNlVgYKD69++v/Pz8UnvLzs5WWFiYtm3bVmKsVq1a5nYQgCnDhw93nE5fvHhxifE+ffooMjJSb7zxhsLDw1VUVKQ2bdqU+f7t06ePXnnllRJjYWFh7m0eXoPQhCqnQ4cOSk9PLxGmyuvTTz/V0KFD9fDDD0v66Zfq8ePHy+wjMzNTfn5+atSokVt7AeCsZ8+eys/Pl8ViUWxsrNPY999/r/T0dL3xxhvq1KmTJOmTTz4pc3sdOnTQv/71LzVq1Eh+fvwprSqYCI4qZ+rUqfrHP/6h6dOn69ChQ/riiy+0YsUKvfTSS+Xa7q9+9SutXr1aaWlp2rdvnwYNGnTVSaSS1L17d8XExKhv377atGmTjh8/rp07d+rFF19USkpKuXoB4MzX11dffPGFDh8+LF9fX6ex2rVrq27dunr99dd15MgRbdmyRRMmTChze6NHj9aFCxc0cOBA7dmzR0ePHtVHH32kYcOGqbCw8EbuCjyI0IQqJzY2VmvXrtWmTZt01113qWPHjlqwYIEiIyPLtd358+erdu3auueee9SnTx/FxsaqQ4cOV623WCxav369OnfurGHDhqlZs2YaMGCATpw44ZhDBcB9bDabbDZbifU+Pj5asWKFUlNT1aZNG40fP15z584tc1vh4eH69NNPVVhYqB49eigqKkrjxo1TrVq15OPDn9bKymIYhuHpJgAAALwdcRgAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJANyga9euGjdunKfbAHADEZoAVBqZmZkaO3asmjZtqoCAAIWEhOjee+/Va6+9ppycHE+3B6CC49bMACqFb775Rvfee69q1aqlWbNmKSoqSlarVQcOHNDrr7+uW2+9VQ8++KCn27yqwsJCWSwW7lsGeDHenQAqhd/+9rfy8/NTSkqKHnvsMbVs2VK33XabHnroIa1bt059+vSRJF28eFEjRoxQvXr1ZLPZ9MADD2jfvn2O7UybNk233367/vnPf6pRo0YKDg7WgAED9OOPPzpqLl26pMGDBysoKEhhYWGaN29eiX7y8vL0wgsv6NZbb1WNGjUUHR2tbdu2OcaXLl2qWrVq6YMPPlCrVq1ktVqVkZFx475BAMqN0ASgwvv++++1adMmjR49WjVq1Ci1xmKxSJIeffRRnT9/Xhs2bFBqaqo6dOigbt266cKFC47ao0ePas2aNVq7dq3Wrl2r7du3a/bs2Y7xiRMnavv27frPf/6jTZs2adu2bfr888+dXm/MmDFKTk7WihUrtH//fj366KPq2bOnvv76a0dNTk6OXnnlFb355ps6dOiQ6tev785vCwB3MwCggtu1a5chyVi9erXT+rp16xo1atQwatSoYUyaNMn4+OOPDZvNZuTm5jrVNWnSxPjb3/5mGIZhvPzyy0b16tUNu93uGJ84caIRHR1tGIZh/Pjjj4a/v7+xatUqx/j3339vBAYGGmPHjjUMwzBOnDhh+Pr6GqdPn3Z6nW7duhlTpkwxDMMw3nnnHUOSkZaW5p5vAoAbjjlNACqtzz77TEVFRXriiSeUl5enffv2KTs7W3Xr1nWqu3z5so4ePepYbtSokWrWrOlYDgsL0/nz5yX9dBQqPz9f0dHRjvE6deqoefPmjuUDBw6osLBQzZo1c3qdvLw8p9f29/dX27Zt3bOzAG44QhOACq9p06ayWCxKT093Wn/bbbdJkgIDAyVJ2dnZCgsLc5pbVKxWrVqOr6tVq+Y0ZrFYVFRUZLqf7Oxs+fr6KjU1Vb6+vk5jQUFBjq8DAwMdpw0BeD9CE4AKr27duvr1r3+tRYsW6bnnnrvqvKYOHTooMzNTfn5+atSokUuv1aRJE1WrVk27d+9Ww4YNJUk//PCDvvrqK3Xp0kWS1L59exUWFur8+fPq1KmTS68DwPswERxApfDqq6+qoKBAd955p1auXKkvvvhC6enpevfdd/Xll1/K19dX3bt3V0xMjPr27atNmzbp+PHj2rlzp1588UWlpKSYep2goCDFx8dr4sSJ2rJliw4ePKihQ4c6XSqgWbNmeuKJJzR48GCtXr1ax44d02effaaEhAStW7fuRn0LANxgHGkCUCk0adJEe/fu1axZszRlyhSdOnVKVqtVrVq10gsvvKDf/va3slgsWr9+vV588UUNGzZM3377rUJDQ9W5c2eFhISYfq25c+cqOztbffr0Uc2aNfX8888rKyvLqeadd97RH/7wBz3//PM6ffq0brnlFnXs2FG9e/d2964DuEkshmEYnm4CAADA23F6DgAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAAT/h85sqXFKb8i5QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ay = sns.boxplot(x = \"Gender\", y = \"MonthlyIncome\", hue = \"JobRole\", data=df, palette=\"Set3\")\n",
        "plt.show(ay)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "_EVIed84Adhv",
        "outputId": "98c99d50-9a7f-468c-d90b-158d2abd288c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating histogram for distribution of Age\n",
        "y = df.TotalWorkingYears\n",
        "n_bins = 6\n",
        "N, bins, patches = plt.hist(y, bins = n_bins, facecolor = 'Blue')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Gt5hE_Oukzcx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "0804bcf8-98c6-4c1f-ada6-ff901887cba2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.pie(df, values = df[\"MonthlyRate\"], names = df[\"JobRole\"],\n",
        "             title = \"MonthlyRate Based on JobRole\", hole = .5)\n",
        "fig.update_traces(textposition=\"inside\", textinfo=\"percent + label\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "zwchuzkpEe-H",
        "outputId": "ffaa8846-9080-47f3-d4b4-b5e0ed02815e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
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              "\n",
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              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
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              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Lzd2AcKpfMya"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}