DataExploration.ipynb

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        "id": "I21VtSiqrOaG"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/data.csv')\n",
        "\n",
        "df.drop('EmpID', axis = 1, inplace = True)\n",
        "df.drop('Emp Name', axis = 1, inplace = True)\n",
        "df.drop('Attrition ', axis = 1,inplace = True)\n",
        "df"
      ],
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "     S.No        DOJ                 Designation Grade       Location  \\\n",
              "0       1  22-Nov-10             Sales Executive    E1      Bhavnagar   \n",
              "1       2   5-Jul-10  Technical Services Officer    M2      Jayanagar   \n",
              "2       3  20-Oct-10             Sales Executive    E1         Indore   \n",
              "3       4   1 May 08                  AM - Sales    M1  Admin Gujarat   \n",
              "4       5   3 Mar 10            Sales Executive     E1   Jagdishpuram   \n",
              "..    ...        ...                         ...   ...            ...   \n",
              "322   323  26-May-08                  AM - Sales    M1         Guntur   \n",
              "323   324  13-Sep-12             Sales Executive    E1         Guntur   \n",
              "324   325  12-Dec-05          Sr Sales Executive    E2         Guntur   \n",
              "325   326  21-Jul-04                  AM - Sales    M1         Guntur   \n",
              "326   327  22-Feb-13             Sales Executive    E1          Hinoo   \n",
              "\n",
              "    In Active Date  Tenure  Gender  Education  Age  Last Rating  \\\n",
              "0        26-May-12   1. 6   Female  Bachelors   27            4   \n",
              "1         6-Apr-12   1. 9     Male        MBA   36            2   \n",
              "2         7-Apr-12   1. 5     Male  Bachelors   24            4   \n",
              "3         3 Apr 12  3 .11     Male  Bachelors   33            2   \n",
              "4        10 Apr 12   2 .1     Male  Bachelors   24            4   \n",
              "..             ...     ...     ...        ...  ...          ...   \n",
              "322      15 Mar 13   4 .9     Male  Bachelors   37            2   \n",
              "323       7 Mar 13   0. 5     Male  Bachelors   25            3   \n",
              "324      12 Mar 13   7 .3     Male  Bachelors   28            1   \n",
              "325      31 Mar 13   8 .8     Male  Bachelors   36            3   \n",
              "326       8 Mar 13   0. 0     Male  Bachelors   27            4   \n",
              "\n",
              "     Monthly Income Engagement Score (% Satisfaction) Marital Status     Zone  \\\n",
              "0             25535                                2%       Divorced     West   \n",
              "1             52155                               45%        Single     South   \n",
              "2             26438                               18%       Divorced  CENTRAL   \n",
              "3             54311                               82%        Single      West   \n",
              "4             26702                               49%       Divorced    North   \n",
              "..              ...                               ...            ...      ...   \n",
              "322           53841                               69%       Divorced    South   \n",
              "323           25813                               11%        Married    South   \n",
              "324           36498                               11%       Divorced    South   \n",
              "325           51796                               82%       Divorced    South   \n",
              "326           26678                               49%       Divorced     East   \n",
              "\n",
              "                                            Remarks  \n",
              "0                           Issues with the Manager  \n",
              "1    More Challenging Job Roles/ Higher designation  \n",
              "2                           Issues with the Manager  \n",
              "3                                    Lack of Growth  \n",
              "4                           Issues with the Manager  \n",
              "..                                              ...  \n",
              "322                                  Lack of Growth  \n",
              "323                                  Lack of Growth  \n",
              "324                                  Lack of Growth  \n",
              "325                  Termination - Poor Performance  \n",
              "326                         Issues with the Manager  \n",
              "\n",
              "[327 rows x 16 columns]"
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              "      <th></th>\n",
              "      <th>S.No</th>\n",
              "      <th>DOJ</th>\n",
              "      <th>Designation</th>\n",
              "      <th>Grade</th>\n",
              "      <th>Location</th>\n",
              "      <th>In Active Date</th>\n",
              "      <th>Tenure</th>\n",
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              "      <th>Engagement Score (% Satisfaction)</th>\n",
              "      <th>Marital Status</th>\n",
              "      <th>Zone</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>22-Nov-10</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>E1</td>\n",
              "      <td>Bhavnagar</td>\n",
              "      <td>26-May-12</td>\n",
              "      <td>1. 6</td>\n",
              "      <td>Female</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>27</td>\n",
              "      <td>4</td>\n",
              "      <td>25535</td>\n",
              "      <td>2%</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>West</td>\n",
              "      <td>Issues with the Manager</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5-Jul-10</td>\n",
              "      <td>Technical Services Officer</td>\n",
              "      <td>M2</td>\n",
              "      <td>Jayanagar</td>\n",
              "      <td>6-Apr-12</td>\n",
              "      <td>1. 9</td>\n",
              "      <td>Male</td>\n",
              "      <td>MBA</td>\n",
              "      <td>36</td>\n",
              "      <td>2</td>\n",
              "      <td>52155</td>\n",
              "      <td>45%</td>\n",
              "      <td>Single</td>\n",
              "      <td>South</td>\n",
              "      <td>More Challenging Job Roles/ Higher designation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>20-Oct-10</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>E1</td>\n",
              "      <td>Indore</td>\n",
              "      <td>7-Apr-12</td>\n",
              "      <td>1. 5</td>\n",
              "      <td>Male</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>26438</td>\n",
              "      <td>18%</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>CENTRAL</td>\n",
              "      <td>Issues with the Manager</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1 May 08</td>\n",
              "      <td>AM - Sales</td>\n",
              "      <td>M1</td>\n",
              "      <td>Admin Gujarat</td>\n",
              "      <td>3 Apr 12</td>\n",
              "      <td>3 .11</td>\n",
              "      <td>Male</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>33</td>\n",
              "      <td>2</td>\n",
              "      <td>54311</td>\n",
              "      <td>82%</td>\n",
              "      <td>Single</td>\n",
              "      <td>West</td>\n",
              "      <td>Lack of Growth</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>3 Mar 10</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>E1</td>\n",
              "      <td>Jagdishpuram</td>\n",
              "      <td>10 Apr 12</td>\n",
              "      <td>2 .1</td>\n",
              "      <td>Male</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>26702</td>\n",
              "      <td>49%</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>North</td>\n",
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              "      <th>322</th>\n",
              "      <td>323</td>\n",
              "      <td>26-May-08</td>\n",
              "      <td>AM - Sales</td>\n",
              "      <td>M1</td>\n",
              "      <td>Guntur</td>\n",
              "      <td>15 Mar 13</td>\n",
              "      <td>4 .9</td>\n",
              "      <td>Male</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>37</td>\n",
              "      <td>2</td>\n",
              "      <td>53841</td>\n",
              "      <td>69%</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>South</td>\n",
              "      <td>Lack of Growth</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>323</th>\n",
              "      <td>324</td>\n",
              "      <td>13-Sep-12</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>E1</td>\n",
              "      <td>Guntur</td>\n",
              "      <td>7 Mar 13</td>\n",
              "      <td>0. 5</td>\n",
              "      <td>Male</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>25813</td>\n",
              "      <td>11%</td>\n",
              "      <td>Married</td>\n",
              "      <td>South</td>\n",
              "      <td>Lack of Growth</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>325</td>\n",
              "      <td>12-Dec-05</td>\n",
              "      <td>Sr Sales Executive</td>\n",
              "      <td>E2</td>\n",
              "      <td>Guntur</td>\n",
              "      <td>12 Mar 13</td>\n",
              "      <td>7 .3</td>\n",
              "      <td>Male</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>36498</td>\n",
              "      <td>11%</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>South</td>\n",
              "      <td>Lack of Growth</td>\n",
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              "      <td>326</td>\n",
              "      <td>21-Jul-04</td>\n",
              "      <td>AM - Sales</td>\n",
              "      <td>M1</td>\n",
              "      <td>Guntur</td>\n",
              "      <td>31 Mar 13</td>\n",
              "      <td>8 .8</td>\n",
              "      <td>Male</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>36</td>\n",
              "      <td>3</td>\n",
              "      <td>51796</td>\n",
              "      <td>82%</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>South</td>\n",
              "      <td>Termination - Poor Performance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>326</th>\n",
              "      <td>327</td>\n",
              "      <td>22-Feb-13</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>E1</td>\n",
              "      <td>Hinoo</td>\n",
              "      <td>8 Mar 13</td>\n",
              "      <td>0. 0</td>\n",
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              "      <td>East</td>\n",
              "      <td>Issues with the Manager</td>\n",
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              "<p>327 rows × 16 columns</p>\n",
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              "      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-29f92906-e246-414c-976e-80f4afdbfb7b 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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"Grade\"] == 'E1', \"Grade\"]= 1\n",
        "df.loc[df[\"Grade\"] == 'E2', \"Grade\"]= 2\n",
        "df.loc[df[\"Grade\"] == 'M1', \"Grade\"]= 3\n",
        "df.loc[df[\"Grade\"] == 'M2', \"Grade\"]= 4\n",
        "df.loc[df[\"Grade\"] == 'M3', \"Grade\"]= 5\n",
        "df.loc[df[\"Grade\"] == 'M4', \"Grade\"]= 6\n",
        "df.loc[df[\"Grade\"] == 'CX0', \"Grade\"]= 7\n",
        "df['Tenure'] = df['Tenure'].str.replace(r'\\s+', '', regex=True)  # Remove extra spaces\n",
        "df['Marital Status'] = df['Marital Status'].str.replace(r'\\s+', '', regex=True)  # Remove extra spaces\n",
        "df['Education'] = df['Education'].str.replace(r'\\s+', '', regex=True)  # Remove extra spaces\n",
        "\n",
        "\n",
        "df['Tenure'] = df['Tenure'].astype(float)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "id": "h3BMoX3V9wvX",
        "outputId": "75305806-50ae-4440-db5b-1bd9b599751b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     S.No        DOJ                 Designation Grade       Location  \\\n",
              "0       1  22-Nov-10             Sales Executive     1      Bhavnagar   \n",
              "1       2   5-Jul-10  Technical Services Officer     4      Jayanagar   \n",
              "2       3  20-Oct-10             Sales Executive     1         Indore   \n",
              "3       4   1 May 08                  AM - Sales     3  Admin Gujarat   \n",
              "4       5   3 Mar 10            Sales Executive      1   Jagdishpuram   \n",
              "..    ...        ...                         ...   ...            ...   \n",
              "322   323  26-May-08                  AM - Sales     3         Guntur   \n",
              "323   324  13-Sep-12             Sales Executive     1         Guntur   \n",
              "324   325  12-Dec-05          Sr Sales Executive     2         Guntur   \n",
              "325   326  21-Jul-04                  AM - Sales     3         Guntur   \n",
              "326   327  22-Feb-13             Sales Executive     1          Hinoo   \n",
              "\n",
              "    In Active Date  Tenure  Gender  Education  Age  Last Rating  \\\n",
              "0        26-May-12    1.60  Female  Bachelors   27            4   \n",
              "1         6-Apr-12    1.90    Male        MBA   36            2   \n",
              "2         7-Apr-12    1.50    Male  Bachelors   24            4   \n",
              "3         3 Apr 12    3.11    Male  Bachelors   33            2   \n",
              "4        10 Apr 12    2.10    Male  Bachelors   24            4   \n",
              "..             ...     ...     ...        ...  ...          ...   \n",
              "322      15 Mar 13    4.90    Male  Bachelors   37            2   \n",
              "323       7 Mar 13    0.50    Male  Bachelors   25            3   \n",
              "324      12 Mar 13    7.30    Male  Bachelors   28            1   \n",
              "325      31 Mar 13    8.80    Male  Bachelors   36            3   \n",
              "326       8 Mar 13    0.00    Male  Bachelors   27            4   \n",
              "\n",
              "     Monthly Income Engagement Score (% Satisfaction) Marital Status     Zone  \\\n",
              "0             25535                                2%       Divorced     West   \n",
              "1             52155                               45%         Single    South   \n",
              "2             26438                               18%       Divorced  CENTRAL   \n",
              "3             54311                               82%         Single     West   \n",
              "4             26702                               49%       Divorced    North   \n",
              "..              ...                               ...            ...      ...   \n",
              "322           53841                               69%       Divorced    South   \n",
              "323           25813                               11%        Married    South   \n",
              "324           36498                               11%       Divorced    South   \n",
              "325           51796                               82%       Divorced    South   \n",
              "326           26678                               49%       Divorced     East   \n",
              "\n",
              "                                            Remarks  \n",
              "0                           Issues with the Manager  \n",
              "1    More Challenging Job Roles/ Higher designation  \n",
              "2                           Issues with the Manager  \n",
              "3                                    Lack of Growth  \n",
              "4                           Issues with the Manager  \n",
              "..                                              ...  \n",
              "322                                  Lack of Growth  \n",
              "323                                  Lack of Growth  \n",
              "324                                  Lack of Growth  \n",
              "325                  Termination - Poor Performance  \n",
              "326                         Issues with the Manager  \n",
              "\n",
              "[327 rows x 16 columns]"
            ],
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>S.No</th>\n",
              "      <th>DOJ</th>\n",
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              "      <th>Marital Status</th>\n",
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              "      <th>Remarks</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>22-Nov-10</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>1</td>\n",
              "      <td>Bhavnagar</td>\n",
              "      <td>26-May-12</td>\n",
              "      <td>1.60</td>\n",
              "      <td>Female</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>27</td>\n",
              "      <td>4</td>\n",
              "      <td>25535</td>\n",
              "      <td>2%</td>\n",
              "      <td>Divorced</td>\n",
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              "      <td>Issues with the Manager</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5-Jul-10</td>\n",
              "      <td>Technical Services Officer</td>\n",
              "      <td>4</td>\n",
              "      <td>Jayanagar</td>\n",
              "      <td>6-Apr-12</td>\n",
              "      <td>1.90</td>\n",
              "      <td>Male</td>\n",
              "      <td>MBA</td>\n",
              "      <td>36</td>\n",
              "      <td>2</td>\n",
              "      <td>52155</td>\n",
              "      <td>45%</td>\n",
              "      <td>Single</td>\n",
              "      <td>South</td>\n",
              "      <td>More Challenging Job Roles/ Higher designation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>20-Oct-10</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>1</td>\n",
              "      <td>Indore</td>\n",
              "      <td>7-Apr-12</td>\n",
              "      <td>1.50</td>\n",
              "      <td>Male</td>\n",
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              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>26438</td>\n",
              "      <td>18%</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>CENTRAL</td>\n",
              "      <td>Issues with the Manager</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1 May 08</td>\n",
              "      <td>AM - Sales</td>\n",
              "      <td>3</td>\n",
              "      <td>Admin Gujarat</td>\n",
              "      <td>3 Apr 12</td>\n",
              "      <td>3.11</td>\n",
              "      <td>Male</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>33</td>\n",
              "      <td>2</td>\n",
              "      <td>54311</td>\n",
              "      <td>82%</td>\n",
              "      <td>Single</td>\n",
              "      <td>West</td>\n",
              "      <td>Lack of Growth</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>3 Mar 10</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>1</td>\n",
              "      <td>Jagdishpuram</td>\n",
              "      <td>10 Apr 12</td>\n",
              "      <td>2.10</td>\n",
              "      <td>Male</td>\n",
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              "      <td>4</td>\n",
              "      <td>26702</td>\n",
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              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>323</td>\n",
              "      <td>26-May-08</td>\n",
              "      <td>AM - Sales</td>\n",
              "      <td>3</td>\n",
              "      <td>Guntur</td>\n",
              "      <td>15 Mar 13</td>\n",
              "      <td>4.90</td>\n",
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              "      <td>37</td>\n",
              "      <td>2</td>\n",
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              "      <td>69%</td>\n",
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              "      <td>Lack of Growth</td>\n",
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              "    <tr>\n",
              "      <th>323</th>\n",
              "      <td>324</td>\n",
              "      <td>13-Sep-12</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>1</td>\n",
              "      <td>Guntur</td>\n",
              "      <td>7 Mar 13</td>\n",
              "      <td>0.50</td>\n",
              "      <td>Male</td>\n",
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              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>25813</td>\n",
              "      <td>11%</td>\n",
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              "      <td>South</td>\n",
              "      <td>Lack of Growth</td>\n",
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              "      <th>324</th>\n",
              "      <td>325</td>\n",
              "      <td>12-Dec-05</td>\n",
              "      <td>Sr Sales Executive</td>\n",
              "      <td>2</td>\n",
              "      <td>Guntur</td>\n",
              "      <td>12 Mar 13</td>\n",
              "      <td>7.30</td>\n",
              "      <td>Male</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>36498</td>\n",
              "      <td>11%</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>South</td>\n",
              "      <td>Lack of Growth</td>\n",
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              "    <tr>\n",
              "      <th>325</th>\n",
              "      <td>326</td>\n",
              "      <td>21-Jul-04</td>\n",
              "      <td>AM - Sales</td>\n",
              "      <td>3</td>\n",
              "      <td>Guntur</td>\n",
              "      <td>31 Mar 13</td>\n",
              "      <td>8.80</td>\n",
              "      <td>Male</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>36</td>\n",
              "      <td>3</td>\n",
              "      <td>51796</td>\n",
              "      <td>82%</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>South</td>\n",
              "      <td>Termination - Poor Performance</td>\n",
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              "    <tr>\n",
              "      <th>326</th>\n",
              "      <td>327</td>\n",
              "      <td>22-Feb-13</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>1</td>\n",
              "      <td>Hinoo</td>\n",
              "      <td>8 Mar 13</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>26678</td>\n",
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              "      <td>East</td>\n",
              "      <td>Issues with the Manager</td>\n",
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              "<p>327 rows × 16 columns</p>\n",
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              "  .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-a3da355d-8d78-4063-a095-06fde00b0f40 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.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3KVkAyrMDv3U",
        "outputId": "590f9bc5-4a2b-42f9-8ec5-967775264daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['S.No', 'DOJ', 'Designation', 'Grade', 'Location', 'In Active Date',\n",
              "       'Tenure', 'Gender', 'Education', 'Age', 'Last Rating', 'Monthly Income',\n",
              "       'Engagement Score (% Satisfaction)', 'Marital Status', 'Zone',\n",
              "       'Remarks'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "q2oyV1DSCgxz",
        "outputId": "628779aa-60e7-451c-8ee7-e79504d6f586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             S.No      Tenure         Age  Last Rating  Monthly Income\n",
              "count  327.000000  327.000000  327.000000   327.000000      327.000000\n",
              "mean   164.000000    1.821957   28.541284     3.229358    32592.948012\n",
              "std     94.540996    1.748788    5.048596     1.038814    15995.368859\n",
              "min      1.000000    0.000000   24.000000     1.000000    25010.000000\n",
              "25%     82.500000    0.300000   25.000000     2.000000    25704.500000\n",
              "50%    164.000000    1.400000   27.000000     3.000000    26389.000000\n",
              "75%    245.500000    2.700000   28.500000     4.000000    31916.000000\n",
              "max    327.000000    8.800000   54.000000     5.000000   233954.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a87e843b-f6b4-4adb-a584-e0cbbd1a563f\" 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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>S.No</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Age</th>\n",
              "      <th>Last Rating</th>\n",
              "      <th>Monthly Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>327.000000</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>327.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>164.000000</td>\n",
              "      <td>1.821957</td>\n",
              "      <td>28.541284</td>\n",
              "      <td>3.229358</td>\n",
              "      <td>32592.948012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>94.540996</td>\n",
              "      <td>1.748788</td>\n",
              "      <td>5.048596</td>\n",
              "      <td>1.038814</td>\n",
              "      <td>15995.368859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>25010.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>82.500000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>25704.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>164.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>26389.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>245.500000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>28.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>31916.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>327.000000</td>\n",
              "      <td>8.800000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>233954.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a87e843b-f6b4-4adb-a584-e0cbbd1a563f')\"\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",
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              "  </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-a87e843b-f6b4-4adb-a584-e0cbbd1a563f 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-a87e843b-f6b4-4adb-a584-e0cbbd1a563f');\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-b3fbcd37-6e16-4b27-b367-4cce8e45d1f3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b3fbcd37-6e16-4b27-b367-4cce8e45d1f3')\"\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-b3fbcd37-6e16-4b27-b367-4cce8e45d1f3 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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_edu = df['Education'].unique()\n",
        "unique_remark = df['Remarks'].unique()\n",
        "unique_desig = df['Designation'].unique()\n",
        "unique_location = df['Location'].unique()\n",
        "unique_MR = df['Marital Status'].unique()\n",
        "\n",
        "\n",
        "print(unique_edu)\n",
        "print()\n",
        "print(unique_remark)\n",
        "print()\n",
        "\n",
        "print(unique_desig)\n",
        "print()\n",
        "\n",
        "print(unique_location)\n",
        "print()\n",
        "\n",
        "print(unique_MR)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myz1RFqZDCAp",
        "outputId": "b759c1c3-95a8-4c18-d1ef-2fbdd6742dfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bachelors' 'MBA']\n",
            "\n",
            "['Issues with the Manager'\n",
            " 'More Challenging Job Roles/ Higher designation' 'Lack of Growth'\n",
            " 'Better Salary' 'Absconding' 'Termination - Theft' 'Reasons not known'\n",
            " 'Higher education' 'Health Reasons' 'Termination - Poor Performance']\n",
            "\n",
            "['Sales Executive' 'Technical Services Officer' 'AM - Sales'\n",
            " 'Sales Executive ' 'Sr Sales Executive' 'Sr Sales Executive '\n",
            " ' Sales Executive' 'Regional Manager' 'Zonal Manager'\n",
            " 'National Sales Head']\n",
            "\n",
            "['Bhavnagar' 'Jayanagar' 'Indore' 'Admin Gujarat' 'Jagdishpuram' 'Lalganj'\n",
            " 'Trombay' 'Sinahagad' 'Vasai' 'Kozhikode' 'Haridwar' 'Madurai' 'Jammu'\n",
            " 'Dehradun' 'Hinoo' 'Balasore' 'Bilaspur' 'Pune' 'Gurukul' 'ZO Kolkata'\n",
            " 'Jagatpur' 'Admin Maharastra' 'Patiala' 'Nasik' 'Trivandrum' 'Dumdum'\n",
            " 'Bally' 'Cuttack' 'Vellore' 'Vyara' 'ZO Mumbai' 'Rohini' 'HO Hyderabad'\n",
            " 'Varanasi' nan 'Koyambedu' 'Madurai (Thallakulam)' 'Siliguri' 'Kanpur'\n",
            " 'Lalpur' 'Raipur' 'Velachery' 'Najafgarh' 'Sultanpur' 'Gurubuxganj'\n",
            " 'Shimla' 'Anand (Ext Vadodara)' 'Shahpura' 'Gondal' 'Sonipat'\n",
            " 'Admin Delhi' 'Tilak Vihar' 'Jagdispuram' 'Magadi Road' 'ZO Delhi'\n",
            " 'Kalyan' 'Loni' 'Kolkata' 'Jhansi' 'Kolkatta' 'Allahabad' 'Mandi'\n",
            " 'Haldwani' 'Unchahar' 'Lambhua' 'Hadapsar' 'Devenahalli' 'Trichy'\n",
            " 'Mukundpur' 'Rajamundry' 'Ameethi' 'Ahmedabad' 'Kalewadi' 'MITHAPUR'\n",
            " 'HINOO' 'Udaipur' 'Hanuman Nagar' 'Indrapuri' 'Kangra' 'jagatpur'\n",
            " 'Hanuman nagar' 'Kashidih' 'Orissa' 'Bhopal Indrapuri'\n",
            " 'Jabalpur Alok Nagar' 'HO' 'Durg' 'Bangalore' 'Behala' 'Bachrawan'\n",
            " 'Vizag' 'Hadspar' 'Badarpur' 'Amritsar' 'Thallakulam' 'Agra' 'Vadodara'\n",
            " 'Barrackpore' 'Kasidih ' 'Danapur' 'Pondicherry' 'Admin Jharkhand'\n",
            " 'Garia' 'Barasat' 'Bhubaneswar' 'Daund' 'Trissur' 'Puri' 'Balasore '\n",
            " 'Anand' 'Coimbatore' 'Thrissur' 'Shapura' 'Alok Nagar'\n",
            " 'Madurai - Thallakulam' 'Jalandhar' 'Bandel' 'Salt Lake' 'Mithapur'\n",
            " 'Kodinar (Gondal Ext)' 'PURI' 'Santhome' 'Mysore ' 'Kasidih' 'Vijaywada'\n",
            " 'Delhi' 'Mysore' 'Jaipur' 'bbsr' 'Hadapsar (Pune)' 'Avadi' 'Koramangala'\n",
            " 'Chennai' 'Vasna' 'Rajkot' 'Badlapur' 'Anand (Vadodara Ext)'\n",
            " 'Navapur (Vyara Ext)' 'Kotra' 'Vijay Vihar' 'Guntur']\n",
            "\n",
            "['Divorced' 'Single' 'Married']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Tenure'].max())\n",
        "df['Tenure'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ee1jyC0DHuxm",
        "outputId": "0ffd13e6-d974-40ed-f913-a832a85d67e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8.8\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df['S.No'].values\n",
        "y = df['Tenure'].values\n",
        "\n",
        "# Calculate the minimum and maximum values of 'Tenure'\n",
        "min_tenure = np.min(y)\n",
        "max_tenure = np.max(y)\n",
        "\n",
        "# Generate evenly spaced y-axis ticks\n",
        "y_ticks = np.linspace(min_tenure, max_tenure, num=9)  # Adjust 'num' as needed\n",
        "\n",
        "# Create the scatter plot\n",
        "plt.plot(x, y, 'ro')\n",
        "\n",
        "# Set the y-axis ticks\n",
        "plt.yticks(y_ticks)\n",
        "\n",
        "# Labeling axes and adding a title\n",
        "plt.ylabel('Tenure')\n",
        "plt.xlabel('S.No')\n",
        "plt.title('Tenure of employees left')\n",
        "\n",
        "# Show the plot\n",
        "plt.grid(True)  # Optional: Add gridlines\n",
        "print(df['Tenure'].describe())\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "WCecYTU-Jcry",
        "outputId": "8c003c35-7e74-4af7-ef41-893cf2c35be1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    327.000000\n",
            "mean       1.821957\n",
            "std        1.748788\n",
            "min        0.000000\n",
            "25%        0.300000\n",
            "50%        1.400000\n",
            "75%        2.700000\n",
            "max        8.800000\n",
            "Name: Tenure, dtype: float64\n"
          ]
        },
        {
          "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": [
        "df1 = df.loc[(df['Tenure'] <= 2)]\n",
        "print(df1.shape)\n",
        "df2 = df.loc[(df['Tenure'] > 2)]\n",
        "print(df2.shape)\n",
        "#206/327 * 100 = 63 percent employees that left the company left within 2 years"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plm_vLCLY3dd",
        "outputId": "62518b9a-74a6-43c7-e9aa-8f0ff1d7a3b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(206, 16)\n",
            "(121, 16)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "206/327 * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkPLxQF8aTvP",
        "outputId": "1bc6c301-881f-41b6-d9e0-48baa7cda064"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "62.99694189602446"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_MBA = (df['Education'] == 'MBA').sum()\n",
        "count_Bac = (df['Education'] == 'Bachelors').sum()\n",
        "\n",
        "x = ['MBA', 'Bachelors']\n",
        "y = [count_MBA, count_Bac]\n",
        "\n",
        "plt.bar(x, y)\n",
        "plt.xlabel('Education Level')\n",
        "plt.ylabel('No of emloyees left')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "sQwgoOOHa88I",
        "outputId": "2c569d22-d263-459f-b740-e86d993a2ce7"
      },
      "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": [
        "df['Zone'] = df['Zone'].str.lower()\n",
        "df\n",
        "\n",
        "count_west = (df['Zone']== 'west').sum()\n",
        "count_south = (df['Zone'] == 'south').sum()\n",
        "count_north = (df['Zone'] == 'north').sum()\n",
        "count_central = (df['Zone'] == 'central').sum()\n",
        "count_east = (df['Zone'] == 'east').sum()\n",
        "\n",
        "\n",
        "print(f'{count_west}, {count_south},{count_north},{count_central},{count_east}')\n",
        "\n",
        "x = ['west', 'south', 'north','central','east']\n",
        "y = [count_west,\n",
        "count_south,\n",
        "count_north,\n",
        "count_central,\n",
        "count_east]\n",
        "plt.bar(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Jq0VXYZodpnd",
        "outputId": "39dd74b4-42dc-4ed9-9039-2d5cff4eed3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51, 100,72,29,58\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 5 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "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": [
        "\n",
        "\n",
        "count_1 = (df['Grade']== 1).sum()\n",
        "count_2 = (df['Grade'] == 2).sum()\n",
        "count_3 = (df['Grade'] == 3).sum()\n",
        "count_4 = (df['Grade'] == 4).sum()\n",
        "count_5 = (df['Grade'] == 5 ).sum()\n",
        "\n",
        "\n",
        "print(f'{count_1}, {count_2},{count_3},{count_4},{count_5}')\n",
        "\n",
        "x = ['1', '2', '3','4','5']\n",
        "y = [count_1,\n",
        "count_2,\n",
        "count_3,\n",
        "count_4,\n",
        "count_5]\n",
        "plt.bar(x,y)\n",
        "plt.xlabel('Grade of employees that left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "IPj_9TO_8LRX",
        "outputId": "14a4d782-3936-4c2d-fe42-a6ce7903955d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "235, 34,22,27,5\n"
          ]
        },
        {
          "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": [
        "count_D = (df['Marital Status']== 'Divorced').sum()\n",
        "count_M = (df['Marital Status'] == 'Married').sum()\n",
        "count_S = (df['Marital Status'] == 'Single').sum()\n",
        "\n",
        "\n",
        "print(f'{count_D}, {count_M},{count_S}')\n",
        "\n",
        "x = ['Divorced', 'Married', 'Single']\n",
        "y = [count_D,\n",
        "count_M,\n",
        "count_south]\n",
        "plt.bar(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "pEdoXP4b9wgQ",
        "outputId": "013ef687-127c-4129-fd52-a28d7e034634"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "118, 103,106\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 3 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "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": [
        "\n",
        "x = df['S.No'].values\n",
        "y = df['Monthly Income'].values\n",
        "\n",
        "# Calculate the minimum and maximum values of 'Tenure'\n",
        "min_tenure = np.min(y)\n",
        "max_tenure = np.max(y)\n",
        "\n",
        "# Generate evenly spaced y-axis ticks\n",
        "y_ticks = np.linspace(min_tenure, max_tenure, num=9)  # Adjust 'num' as needed\n",
        "\n",
        "# Create the scatter plot\n",
        "plt.plot(x, y, 'ro')\n",
        "\n",
        "# Set the y-axis ticks\n",
        "plt.yticks(y_ticks)\n",
        "\n",
        "# Labeling axes and adding a title\n",
        "plt.xlabel('S.No')\n",
        "plt.ylabel('Monthly Income')\n",
        "\n",
        "# Show the plot\n",
        "plt.grid(True)  # Optional: Add gridlines\n",
        "plt.show()\n",
        "df['Monthly Income'].describe()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "Esn10MnGA3V4",
        "outputId": "3b80423f-975e-4b0d-9cab-b2ab916b5d62"
      },
      "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": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       327.000000\n",
              "mean      32592.948012\n",
              "std       15995.368859\n",
              "min       25010.000000\n",
              "25%       25704.500000\n",
              "50%       26389.000000\n",
              "75%       31916.000000\n",
              "max      233954.000000\n",
              "Name: Monthly Income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "75 percent of the employess who left had monthly income under 32k."
      ],
      "metadata": {
        "id": "z4F5lS_Cl6io"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "x = df['S.No'].values\n",
        "y = df['Age'].values\n",
        "\n",
        "# Calculate the minimum and maximum values of 'Tenure'\n",
        "min_tenure = np.min(y)\n",
        "max_tenure = np.max(y)\n",
        "\n",
        "# Generate evenly spaced y-axis ticks\n",
        "y_ticks = np.linspace(min_tenure, max_tenure, num=9)  # Adjust 'num' as needed\n",
        "\n",
        "# Create the scatter plot\n",
        "plt.plot(x, y, 'ro')\n",
        "\n",
        "# Set the y-axis ticks\n",
        "plt.yticks(y_ticks)\n",
        "\n",
        "# Labeling axes and adding a title\n",
        "plt.xlabel('S.No')\n",
        "plt.ylabel('Age')\n",
        "\n",
        "# Show the plot\n",
        "plt.grid(True)  # Optional: Add gridlines\n",
        "plt.show()\n",
        "df['Age'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "HtzisUFBjAai",
        "outputId": "685ee97c-5603-419d-f6b0-a763fdc125fd"
      },
      "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": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    327.000000\n",
              "mean      28.541284\n",
              "std        5.048596\n",
              "min       24.000000\n",
              "25%       25.000000\n",
              "50%       27.000000\n",
              "75%       28.500000\n",
              "max       54.000000\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "75 percent employess who left were under the age of 28 years while some are scattered in the age of 28 to 46\n"
      ],
      "metadata": {
        "id": "EXMr1ugPm0sM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = df['Remarks'].unique()\n",
        "y = []\n",
        "for x in arr:\n",
        "  j = (df['Remarks']== x).sum()\n",
        "  y.append(j)\n",
        "\n",
        "plt.figure(figsize=(10, 6))  # Adjust the figure size as needed\n",
        "plt.bar(arr, y)\n",
        "plt.xticks(rotation=90)  # Rotate x-axis labels by 90 degrees\n",
        "plt.xlabel('Remarks')     # Label for the x-axis\n",
        "plt.ylabel('Count')       # Label for the y-axis\n",
        "plt.title('Remarks Distribution')  # Title for the plot\n",
        "#plt.tight_layout()  # Ensure labels fit within the figure boundaries\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "id": "BB5Q0X80nE-o",
        "outputId": "2b2c0811-2707-4c00-a043-5970122c7273"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = (df['Gender']=='Male').sum()\n",
        "y = (df['Gender']=='Female').sum()\n",
        "print(\"Male - \", end = '')\n",
        "print(x)\n",
        "print(\"Female - \", end = '')\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9ieyQOnpBuv",
        "outputId": "4052f549-7015-4c8c-a198-813101a6b2b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Male - 309\n",
            "Female - 18\n"
          ]
        }
      ]
    }
  ]
}