Day_07_assignment.ipynb

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  "metadata": {
    "colab": {
      "name": "Day_07_assignment.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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      "cell_type": "code",
      "metadata": {
        "id": "dKKSE9npFVUv",
        "colab_type": "code",
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      },
      "source": [
        "import numpy as nm\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "dataset1 = pd.read_csv(\"/general_data.csv\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iRzNo8wtGv2x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "9ab64d43-5cc9-4634-db32-d6358139a6a2"
      },
      "source": [
        "dataset1.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Attrition</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>Department</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EducationField</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>Over18</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>StandardHours</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
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              "      <td>51</td>\n",
              "      <td>No</td>\n",
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              "      <td>2</td>\n",
              "      <td>Life Sciences</td>\n",
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              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>Research Scientist</td>\n",
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              "      <th>2</th>\n",
              "      <td>32</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>4</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>Married</td>\n",
              "      <td>193280</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>38</td>\n",
              "      <td>No</td>\n",
              "      <td>Non-Travel</td>\n",
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              "      <td>5</td>\n",
              "      <td>Life Sciences</td>\n",
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              "      <td>32</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
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              "      <td>1</td>\n",
              "      <td>Medical</td>\n",
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              "      <td>5</td>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age Attrition  ... YearsSinceLastPromotion YearsWithCurrManager\n",
              "0   51        No  ...                       0                    0\n",
              "1   31       Yes  ...                       1                    4\n",
              "2   32        No  ...                       0                    3\n",
              "3   38        No  ...                       7                    5\n",
              "4   32        No  ...                       0                    4\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ey1NTXkjInM0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "5f228360-8d6c-4dfe-f985-fa7e9fa04013"
      },
      "source": [
        "dataset1.isnull()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</table>\n",
              "<p>4410 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Age  Attrition  ...  YearsSinceLastPromotion  YearsWithCurrManager\n",
              "0     False      False  ...                    False                 False\n",
              "1     False      False  ...                    False                 False\n",
              "2     False      False  ...                    False                 False\n",
              "3     False      False  ...                    False                 False\n",
              "4     False      False  ...                    False                 False\n",
              "...     ...        ...  ...                      ...                   ...\n",
              "4405  False      False  ...                    False                 False\n",
              "4406  False      False  ...                    False                 False\n",
              "4407  False      False  ...                    False                 False\n",
              "4408  False      False  ...                    False                 False\n",
              "4409  False      False  ...                    False                 False\n",
              "\n",
              "[4410 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UcT94e3Is02",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "12c6aff5-04d8-45d2-ccb0-aba3a2439503"
      },
      "source": [
        "dataset1.duplicated()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       False\n",
              "1       False\n",
              "2       False\n",
              "3       False\n",
              "4       False\n",
              "        ...  \n",
              "4405    False\n",
              "4406    False\n",
              "4407    False\n",
              "4408    False\n",
              "4409    False\n",
              "Length: 4410, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoatM427Ix0E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "1704f639-866b-44e7-a5cf-4449a6fc8b51"
      },
      "source": [
        "dataset1.drop_duplicates()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Attrition</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>Department</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
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              "      <th>EmployeeID</th>\n",
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              "      <th>JobLevel</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>Over18</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>StandardHours</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>51</td>\n",
              "      <td>No</td>\n",
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              "      <td>Sales</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>Healthcare Representative</td>\n",
              "      <td>Married</td>\n",
              "      <td>131160</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>Single</td>\n",
              "      <td>41890</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>23</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Frequently</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>4</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>Married</td>\n",
              "      <td>193280</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>15</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38</td>\n",
              "      <td>No</td>\n",
              "      <td>Non-Travel</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>3</td>\n",
              "      <td>Human Resources</td>\n",
              "      <td>Married</td>\n",
              "      <td>83210</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>13.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>Single</td>\n",
              "      <td>23420</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4405</th>\n",
              "      <td>42</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>4406</td>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>Single</td>\n",
              "      <td>60290</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4406</th>\n",
              "      <td>29</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>4407</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>26790</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>15</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4407</th>\n",
              "      <td>25</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>4408</td>\n",
              "      <td>Male</td>\n",
              "      <td>2</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>Married</td>\n",
              "      <td>37020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4408</th>\n",
              "      <td>42</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Sales</td>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>4409</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>23980</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4409</th>\n",
              "      <td>40</td>\n",
              "      <td>No</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>28</td>\n",
              "      <td>3</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>4410</td>\n",
              "      <td>Male</td>\n",
              "      <td>2</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>54680</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4410 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Age Attrition  ... YearsSinceLastPromotion YearsWithCurrManager\n",
              "0      51        No  ...                       0                    0\n",
              "1      31       Yes  ...                       1                    4\n",
              "2      32        No  ...                       0                    3\n",
              "3      38        No  ...                       7                    5\n",
              "4      32        No  ...                       0                    4\n",
              "...   ...       ...  ...                     ...                  ...\n",
              "4405   42        No  ...                       0                    2\n",
              "4406   29        No  ...                       0                    2\n",
              "4407   25        No  ...                       1                    2\n",
              "4408   42        No  ...                       7                    8\n",
              "4409   40        No  ...                       3                    9\n",
              "\n",
              "[4410 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qrlx8NZI-Jt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset2 = dataset1.describe()"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfjkTcu2JuVu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "757fc3bc-0827-4182-ac5a-5b3a319dbe6c"
      },
      "source": [
        "dataset2"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeID</th>\n",
              "      <th>JobLevel</th>\n",
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              "      <th>NumCompaniesWorked</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>StandardHours</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.0</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4391.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.0</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4401.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>4410.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36.923810</td>\n",
              "      <td>9.192517</td>\n",
              "      <td>2.912925</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2205.500000</td>\n",
              "      <td>2.063946</td>\n",
              "      <td>65029.312925</td>\n",
              "      <td>2.694830</td>\n",
              "      <td>15.209524</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.793878</td>\n",
              "      <td>11.279936</td>\n",
              "      <td>2.799320</td>\n",
              "      <td>7.008163</td>\n",
              "      <td>2.187755</td>\n",
              "      <td>4.123129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.133301</td>\n",
              "      <td>8.105026</td>\n",
              "      <td>1.023933</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1273.201673</td>\n",
              "      <td>1.106689</td>\n",
              "      <td>47068.888559</td>\n",
              "      <td>2.498887</td>\n",
              "      <td>3.659108</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.851883</td>\n",
              "      <td>7.782222</td>\n",
              "      <td>1.288978</td>\n",
              "      <td>6.125135</td>\n",
              "      <td>3.221699</td>\n",
              "      <td>3.567327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</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>10090.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1103.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>29110.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</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>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2205.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>49190.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</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>14.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3307.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>83800.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.000000</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>29.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4410.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>199990.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>17.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Age  ...  YearsWithCurrManager\n",
              "count  4410.000000  ...           4410.000000\n",
              "mean     36.923810  ...              4.123129\n",
              "std       9.133301  ...              3.567327\n",
              "min      18.000000  ...              0.000000\n",
              "25%      30.000000  ...              2.000000\n",
              "50%      36.000000  ...              3.000000\n",
              "75%      43.000000  ...              7.000000\n",
              "max      60.000000  ...             17.000000\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBa2Z6prJxzK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_median = dataset1.median()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWsIXRSiKBlv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "b1303fc2-8c85-41c7-e788-8f17161a0783"
      },
      "source": [
        "dataset_median"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                           36.0\n",
              "DistanceFromHome               7.0\n",
              "Education                      3.0\n",
              "EmployeeCount                  1.0\n",
              "EmployeeID                  2205.5\n",
              "JobLevel                       2.0\n",
              "MonthlyIncome              49190.0\n",
              "NumCompaniesWorked             2.0\n",
              "PercentSalaryHike             14.0\n",
              "StandardHours                  8.0\n",
              "StockOptionLevel               1.0\n",
              "TotalWorkingYears             10.0\n",
              "TrainingTimesLastYear          3.0\n",
              "YearsAtCompany                 5.0\n",
              "YearsSinceLastPromotion        1.0\n",
              "YearsWithCurrManager           3.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIs0-sDvKC6p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_mode = dataset1.mode()"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8hgg-1uRKJjg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "aa0cffde-8cf4-497e-f98c-8590f0289df3"
      },
      "source": [
        "dataset_mode"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Attrition</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>Department</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
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              "      <th>JobLevel</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>Over18</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>StandardHours</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35.0</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>Life Sciences</td>\n",
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              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>Married</td>\n",
              "      <td>23420.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Y</td>\n",
              "      <td>11.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4405</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4406</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4406</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4407</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4407</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4408</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4408</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4409</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4409</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4410</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4410 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Age Attrition  ... YearsSinceLastPromotion YearsWithCurrManager\n",
              "0     35.0        No  ...                     0.0                  2.0\n",
              "1      NaN       NaN  ...                     NaN                  NaN\n",
              "2      NaN       NaN  ...                     NaN                  NaN\n",
              "3      NaN       NaN  ...                     NaN                  NaN\n",
              "4      NaN       NaN  ...                     NaN                  NaN\n",
              "...    ...       ...  ...                     ...                  ...\n",
              "4405   NaN       NaN  ...                     NaN                  NaN\n",
              "4406   NaN       NaN  ...                     NaN                  NaN\n",
              "4407   NaN       NaN  ...                     NaN                  NaN\n",
              "4408   NaN       NaN  ...                     NaN                  NaN\n",
              "4409   NaN       NaN  ...                     NaN                  NaN\n",
              "\n",
              "[4410 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZ-onrlCKU4N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_mean = dataset1.mean()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qk_MLlxXKf5C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "c6a240a1-ad90-49c0-af9c-f780366aaef3"
      },
      "source": [
        "dataset_mean"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                           36.923810\n",
              "DistanceFromHome               9.192517\n",
              "Education                      2.912925\n",
              "EmployeeCount                  1.000000\n",
              "EmployeeID                  2205.500000\n",
              "JobLevel                       2.063946\n",
              "MonthlyIncome              65029.312925\n",
              "NumCompaniesWorked             2.694830\n",
              "PercentSalaryHike             15.209524\n",
              "StandardHours                  8.000000\n",
              "StockOptionLevel               0.793878\n",
              "TotalWorkingYears             11.279936\n",
              "TrainingTimesLastYear          2.799320\n",
              "YearsAtCompany                 7.008163\n",
              "YearsSinceLastPromotion        2.187755\n",
              "YearsWithCurrManager           4.123129\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_MnIYqVKhSL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_skew = dataset1.skew()"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tC9H2U-KlRb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "a7079de3-552e-4fa0-a9dd-82bd709f1741"
      },
      "source": [
        "dataset_skew"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                        0.413005\n",
              "DistanceFromHome           0.957466\n",
              "Education                 -0.289484\n",
              "EmployeeCount              0.000000\n",
              "EmployeeID                 0.000000\n",
              "JobLevel                   1.024703\n",
              "MonthlyIncome              1.368884\n",
              "NumCompaniesWorked         1.026767\n",
              "PercentSalaryHike          0.820569\n",
              "StandardHours              0.000000\n",
              "StockOptionLevel           0.968321\n",
              "TotalWorkingYears          1.116832\n",
              "TrainingTimesLastYear      0.552748\n",
              "YearsAtCompany             1.763328\n",
              "YearsSinceLastPromotion    1.982939\n",
              "YearsWithCurrManager       0.832884\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aehzNSAiKnUl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_var = dataset1.var()"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aD5priWNK2DX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "ba830411-8066-44f4-b19b-d3c84b259f36"
      },
      "source": [
        "dataset_var"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                        8.341719e+01\n",
              "DistanceFromHome           6.569144e+01\n",
              "Education                  1.048438e+00\n",
              "EmployeeCount              0.000000e+00\n",
              "EmployeeID                 1.621042e+06\n",
              "JobLevel                   1.224760e+00\n",
              "MonthlyIncome              2.215480e+09\n",
              "NumCompaniesWorked         6.244436e+00\n",
              "PercentSalaryHike          1.338907e+01\n",
              "StandardHours              0.000000e+00\n",
              "StockOptionLevel           7.257053e-01\n",
              "TotalWorkingYears          6.056298e+01\n",
              "TrainingTimesLastYear      1.661465e+00\n",
              "YearsAtCompany             3.751728e+01\n",
              "YearsSinceLastPromotion    1.037935e+01\n",
              "YearsWithCurrManager       1.272582e+01\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1U1_hZRuK4mw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_kurt = dataset1.kurt()"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-cOcGHzK_rg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "2cce822e-d24f-4a1e-cc69-9ec473e5324b"
      },
      "source": [
        "dataset_kurt"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                       -0.405951\n",
              "DistanceFromHome          -0.227045\n",
              "Education                 -0.560569\n",
              "EmployeeCount              0.000000\n",
              "EmployeeID                -1.200000\n",
              "JobLevel                   0.395525\n",
              "MonthlyIncome              1.000232\n",
              "NumCompaniesWorked         0.007287\n",
              "PercentSalaryHike         -0.302638\n",
              "StandardHours              0.000000\n",
              "StockOptionLevel           0.361086\n",
              "TotalWorkingYears          0.912936\n",
              "TrainingTimesLastYear      0.491149\n",
              "YearsAtCompany             3.923864\n",
              "YearsSinceLastPromotion    3.601761\n",
              "YearsWithCurrManager       0.167949\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mAm7ZeiYLawz",
        "colab_type": "text"
      },
      "source": [
        "## Inference from the analysis:\n",
        "##### *All of the above variables show positive skewness, while Age, Mean_distance_from_home are leptokurtic and all the other variabes are platykurtic\n",
        "##### *The meann Monthly innnceom iqr is 54k suggesting the company wide attrition accross all income bands \n",
        "##### *Mean age from a new normal distribution with 13 years of IQR\n",
        "\n",
        "## Outliers:\n",
        "#### *There's no regressionn found while plotting age, MonthlyIncome, TotalWorkingHours and YearsAtCompany etc on a scatter plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLv0YKeOMhv-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "16a9b7bc-845a-41d5-f92d-9932cd48b3e7"
      },
      "source": [
        "box_plot = dataset1.Age\n",
        "plt.boxplot(box_plot)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7f6667bca8d0>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f6667bde2e8>,\n",
              "  <matplotlib.lines.Line2D at 0x7f6667bde668>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f6667bded68>],\n",
              " 'means': [],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f6667bde9e8>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7f6667bcaba8>,\n",
              "  <matplotlib.lines.Line2D at 0x7f6667bcaf28>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hd8AQOuMvhv",
        "colab_type": "text"
      },
      "source": [
        "#### Age is normally distributed without any outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reDTIVmJM17q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "c2511e70-1d1b-4c9c-aa53-ffefbcfbb8e2"
      },
      "source": [
        "box_plot = dataset1.MonthlyIncome\n",
        "plt.boxplot(box_plot)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7f66677106a0>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f666771d0f0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f666771d470>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f666771db70>],\n",
              " 'means': [],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f666771d7f0>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7f66677109b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f6667710d30>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l5rK9BNoM-MW",
        "colab_type": "text"
      },
      "source": [
        "#### Monthly Income is rightskewed with several outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13Si7jY2NGBg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "6cc66f8e-8a98-4871-c03a-1b88f3e14827"
      },
      "source": [
        "box_plot = dataset1.YearsAtCompany\n",
        "plt.boxplot(box_plot)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7f666767a358>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f666767ad30>,\n",
              "  <matplotlib.lines.Line2D at 0x7f66676860f0>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f66676867f0>],\n",
              " 'means': [],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f6667686470>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7f666767a630>,\n",
              "  <matplotlib.lines.Line2D at 0x7f666767a9b0>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Jc5D0FGNPzC",
        "colab_type": "text"
      },
      "source": [
        "#### Years at the company is also right skewed with several outliers"
      ]
    }
  ]
}