HR_Transform.ipynb

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      "source": [
        "import pandas as pd"
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      "source": [
        "df = pd.read_csv('/content/HR Employee Attrition.csv')\n",
        "df"
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              "      Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
              "0      41       Yes      Travel_Rarely       1102                   Sales   \n",
              "1      49        No  Travel_Frequently        279  Research & Development   \n",
              "2      37       Yes      Travel_Rarely       1373  Research & Development   \n",
              "3      33        No  Travel_Frequently       1392  Research & Development   \n",
              "4      27        No      Travel_Rarely        591  Research & Development   \n",
              "...   ...       ...                ...        ...                     ...   \n",
              "1465   36        No  Travel_Frequently        884  Research & Development   \n",
              "1466   39        No      Travel_Rarely        613  Research & Development   \n",
              "1467   27        No      Travel_Rarely        155  Research & Development   \n",
              "1468   49        No  Travel_Frequently       1023                   Sales   \n",
              "1469   34        No      Travel_Rarely        628  Research & Development   \n",
              "\n",
              "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
              "0                    1          2  Life Sciences              1   \n",
              "1                    8          1  Life Sciences              1   \n",
              "2                    2          2          Other              1   \n",
              "3                    3          4  Life Sciences              1   \n",
              "4                    2          1        Medical              1   \n",
              "...                ...        ...            ...            ...   \n",
              "1465                23          2        Medical              1   \n",
              "1466                 6          1        Medical              1   \n",
              "1467                 4          3  Life Sciences              1   \n",
              "1468                 2          3        Medical              1   \n",
              "1469                 8          3        Medical              1   \n",
              "\n",
              "      EmployeeNumber  ...  RelationshipSatisfaction StandardHours  \\\n",
              "0                  1  ...                         1            80   \n",
              "1                  2  ...                         4            80   \n",
              "2                  4  ...                         2            80   \n",
              "3                  5  ...                         3            80   \n",
              "4                  7  ...                         4            80   \n",
              "...              ...  ...                       ...           ...   \n",
              "1465            2061  ...                         3            80   \n",
              "1466            2062  ...                         1            80   \n",
              "1467            2064  ...                         2            80   \n",
              "1468            2065  ...                         4            80   \n",
              "1469            2068  ...                         1            80   \n",
              "\n",
              "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0                    0                  8                      0   \n",
              "1                    1                 10                      3   \n",
              "2                    0                  7                      3   \n",
              "3                    0                  8                      3   \n",
              "4                    1                  6                      3   \n",
              "...                ...                ...                    ...   \n",
              "1465                 1                 17                      3   \n",
              "1466                 1                  9                      5   \n",
              "1467                 1                  6                      0   \n",
              "1468                 0                 17                      3   \n",
              "1469                 0                  6                      3   \n",
              "\n",
              "     WorkLifeBalance  YearsAtCompany YearsInCurrentRole  \\\n",
              "0                  1               6                  4   \n",
              "1                  3              10                  7   \n",
              "2                  3               0                  0   \n",
              "3                  3               8                  7   \n",
              "4                  3               2                  2   \n",
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              "1465               3               5                  2   \n",
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              "1467               3               6                  2   \n",
              "1468               2               9                  6   \n",
              "1469               4               4                  3   \n",
              "\n",
              "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                           0                     5  \n",
              "1                           1                     7  \n",
              "2                           0                     0  \n",
              "3                           3                     0  \n",
              "4                           2                     2  \n",
              "...                       ...                   ...  \n",
              "1465                        0                     3  \n",
              "1466                        1                     7  \n",
              "1467                        0                     3  \n",
              "1468                        0                     8  \n",
              "1469                        1                     2  \n",
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              "    [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-b3bd9d62-8f19-468f-b259-7f7d506e7e3e 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-b3bd9d62-8f19-468f-b259-7f7d506e7e3e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJZ5iBlow_Mv",
        "outputId": "20f4cb53-2882-4890-bdbb-2450709f6320"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1470 entries, 0 to 1469\n",
            "Data columns (total 35 columns):\n",
            " #   Column                    Non-Null Count  Dtype \n",
            "---  ------                    --------------  ----- \n",
            " 0   Age                       1470 non-null   int64 \n",
            " 1   Attrition                 1470 non-null   object\n",
            " 2   BusinessTravel            1470 non-null   object\n",
            " 3   DailyRate                 1470 non-null   int64 \n",
            " 4   Department                1470 non-null   object\n",
            " 5   DistanceFromHome          1470 non-null   int64 \n",
            " 6   Education                 1470 non-null   int64 \n",
            " 7   EducationField            1470 non-null   object\n",
            " 8   EmployeeCount             1470 non-null   int64 \n",
            " 9   EmployeeNumber            1470 non-null   int64 \n",
            " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
            " 11  Gender                    1470 non-null   object\n",
            " 12  HourlyRate                1470 non-null   int64 \n",
            " 13  JobInvolvement            1470 non-null   int64 \n",
            " 14  JobLevel                  1470 non-null   int64 \n",
            " 15  JobRole                   1470 non-null   object\n",
            " 16  JobSatisfaction           1470 non-null   int64 \n",
            " 17  MaritalStatus             1470 non-null   object\n",
            " 18  MonthlyIncome             1470 non-null   int64 \n",
            " 19  MonthlyRate               1470 non-null   int64 \n",
            " 20  NumCompaniesWorked        1470 non-null   int64 \n",
            " 21  Over18                    1470 non-null   object\n",
            " 22  OverTime                  1470 non-null   object\n",
            " 23  PercentSalaryHike         1470 non-null   int64 \n",
            " 24  PerformanceRating         1470 non-null   int64 \n",
            " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
            " 26  StandardHours             1470 non-null   int64 \n",
            " 27  StockOptionLevel          1470 non-null   int64 \n",
            " 28  TotalWorkingYears         1470 non-null   int64 \n",
            " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
            " 30  WorkLifeBalance           1470 non-null   int64 \n",
            " 31  YearsAtCompany            1470 non-null   int64 \n",
            " 32  YearsInCurrentRole        1470 non-null   int64 \n",
            " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
            " 34  YearsWithCurrManager      1470 non-null   int64 \n",
            "dtypes: int64(26), object(9)\n",
            "memory usage: 402.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48JmMy2jwzW1",
        "outputId": "fb02a16c-1727-410b-c43b-f022b5738487"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                         0\n",
              "Attrition                   0\n",
              "BusinessTravel              0\n",
              "DailyRate                   0\n",
              "Department                  0\n",
              "DistanceFromHome            0\n",
              "Education                   0\n",
              "EducationField              0\n",
              "EmployeeCount               0\n",
              "EmployeeNumber              0\n",
              "EnvironmentSatisfaction     0\n",
              "Gender                      0\n",
              "HourlyRate                  0\n",
              "JobInvolvement              0\n",
              "JobLevel                    0\n",
              "JobRole                     0\n",
              "JobSatisfaction             0\n",
              "MaritalStatus               0\n",
              "MonthlyIncome               0\n",
              "MonthlyRate                 0\n",
              "NumCompaniesWorked          0\n",
              "Over18                      0\n",
              "OverTime                    0\n",
              "PercentSalaryHike           0\n",
              "PerformanceRating           0\n",
              "RelationshipSatisfaction    0\n",
              "StandardHours               0\n",
              "StockOptionLevel            0\n",
              "TotalWorkingYears           0\n",
              "TrainingTimesLastYear       0\n",
              "WorkLifeBalance             0\n",
              "YearsAtCompany              0\n",
              "YearsInCurrentRole          0\n",
              "YearsSinceLastPromotion     0\n",
              "YearsWithCurrManager        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "irrelevant_cols = ['Over18', 'StandardHours', 'EmployeeCount']\n",
        "df = df.drop(irrelevant_cols, axis=1)"
      ],
      "metadata": {
        "id": "CXuPOtFHxCSI"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "qBwiHn2Fxi5n",
        "outputId": "f289d325-9942-4028-e0e3-d4e909f93b67"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
              "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
              "1   49        No  Travel_Frequently        279  Research & Development   \n",
              "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
              "3   33        No  Travel_Frequently       1392  Research & Development   \n",
              "4   27        No      Travel_Rarely        591  Research & Development   \n",
              "\n",
              "   DistanceFromHome  Education EducationField  EmployeeNumber  \\\n",
              "0                 1          2  Life Sciences               1   \n",
              "1                 8          1  Life Sciences               2   \n",
              "2                 2          2          Other               4   \n",
              "3                 3          4  Life Sciences               5   \n",
              "4                 2          1        Medical               7   \n",
              "\n",
              "   EnvironmentSatisfaction  ... PerformanceRating  RelationshipSatisfaction  \\\n",
              "0                        2  ...                 3                         1   \n",
              "1                        3  ...                 4                         4   \n",
              "2                        4  ...                 3                         2   \n",
              "3                        4  ...                 3                         3   \n",
              "4                        1  ...                 3                         4   \n",
              "\n",
              "   StockOptionLevel  TotalWorkingYears TrainingTimesLastYear  WorkLifeBalance  \\\n",
              "0                 0                  8                     0                1   \n",
              "1                 1                 10                     3                3   \n",
              "2                 0                  7                     3                3   \n",
              "3                 0                  8                     3                3   \n",
              "4                 1                  6                     3                3   \n",
              "\n",
              "  YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
              "0              6                   4                        0   \n",
              "1             10                   7                        1   \n",
              "2              0                   0                        0   \n",
              "3              8                   7                        3   \n",
              "4              2                   2                        2   \n",
              "\n",
              "   YearsWithCurrManager  \n",
              "0                     5  \n",
              "1                     7  \n",
              "2                     0  \n",
              "3                     0  \n",
              "4                     2  \n",
              "\n",
              "[5 rows x 32 columns]"
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "df.to_csv('hr_attrition_transform.csv')\n",
        "files.download('hr_attrition_transform.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "51vXID74yOu5",
        "outputId": "df7dbf56-7ae4-4f01-f440-ae9b5fe0099d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_fc98b93d-7734-4552-bc06-ee93b389201e\", \"hr_attrition_transform_.csv\", 222419)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}