IBM_HR_Attrition_Predction.ipynb

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        "id": "aCkCNtB9fviC"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)\n",
        "from imblearn.over_sampling import SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3cuFz-EIfzMc"
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      "source": [
        "data=pd.read_csv(\"/content/train delete.csv\")"
      ]
    },
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              "   Age Attrition BusinessTravel  DailyRate              Department  \\\n",
              "0   41       Yes  Travel_Rarely       1102                   Sales   \n",
              "1   37       Yes  Travel_Rarely       1373  Research & Development   \n",
              "2   28       Yes  Travel_Rarely        103  Research & Development   \n",
              "3   36       Yes  Travel_Rarely       1218                   Sales   \n",
              "4   34       Yes  Travel_Rarely        699  Research & Development   \n",
              "\n",
              "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
              "0                 1          2  Life Sciences              1               1   \n",
              "1                 2          2          Other              1               4   \n",
              "2                24          3  Life Sciences              1              19   \n",
              "3                 9          4  Life Sciences              1              27   \n",
              "4                 6          1        Medical              1              31   \n",
              "\n",
              "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
              "0  ...                         1            80                 0   \n",
              "1  ...                         2            80                 0   \n",
              "2  ...                         2            80                 0   \n",
              "3  ...                         2            80                 0   \n",
              "4  ...                         3            80                 0   \n",
              "\n",
              "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
              "0                  8                      0               1               6   \n",
              "1                  7                      3               3               0   \n",
              "2                  6                      4               3               4   \n",
              "3                 10                      4               3               5   \n",
              "4                  8                      2               3               4   \n",
              "\n",
              "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                  4                        0                     5  \n",
              "1                  0                        0                     0  \n",
              "2                  2                        0                     3  \n",
              "3                  3                        0                     3  \n",
              "4                  2                        1                     3  \n",
              "\n",
              "[5 rows x 35 columns]"
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              "      <td>Travel_Rarely</td>\n",
              "      <td>699</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>6</td>\n",
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          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z_Ku_UggjgCq"
      },
      "outputs": [],
      "source": [
        "data = data.drop(['MonthlyRate', 'DailyRate','HourlyRate','Over18','EmployeeCount','StandardHours','EmployeeNumber'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
      "metadata": {
        "id": "nKpKQJPV-8QF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Attrition'] = le.fit_transform(data['Attrition'])\n",
        "data['BusinessTravel'] = le.fit_transform(data['BusinessTravel'])\n",
        "data['Department'] = le.fit_transform(data['Department'])\n",
        "data['EducationField'] = le.fit_transform(data['EducationField'])\n",
        "data['Gender'] = le.fit_transform(data['Gender'])\n",
        "data['JobRole'] = le.fit_transform(data['JobRole'])\n",
        "data['MaritalStatus'] = le.fit_transform(data['MaritalStatus'])\n",
        "data['OverTime'] = le.fit_transform(data['OverTime'])"
      ],
      "metadata": {
        "id": "W5duu3vZ_Jqx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features=data.drop('Attrition',axis=1)\n",
        "target=data['Attrition']"
      ],
      "metadata": {
        "id": "gizFbqXrQ5eg"
      },
      "execution_count": null,
      "outputs": []
    },
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        "features"
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        "outputId": "e0c6b25c-fc5a-430e-8456-552db12bd326"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  BusinessTravel  Department  DistanceFromHome  Education  \\\n",
              "0     41               2           2                 1          2   \n",
              "1     37               2           1                 2          2   \n",
              "2     28               2           1                24          3   \n",
              "3     36               2           2                 9          4   \n",
              "4     34               2           1                 6          1   \n",
              "..   ...             ...         ...               ...        ...   \n",
              "799   36               1           1                23          2   \n",
              "800   39               2           1                 6          1   \n",
              "801   27               2           1                 4          3   \n",
              "802   49               1           2                 2          3   \n",
              "803   34               2           1                 8          3   \n",
              "\n",
              "     EducationField  EnvironmentSatisfaction  Gender  JobInvolvement  \\\n",
              "0                 1                        2       0               3   \n",
              "1                 4                        4       1               2   \n",
              "2                 1                        3       1               2   \n",
              "3                 1                        3       1               2   \n",
              "4                 3                        2       1               3   \n",
              "..              ...                      ...     ...             ...   \n",
              "799               3                        3       1               4   \n",
              "800               3                        4       1               2   \n",
              "801               1                        2       1               4   \n",
              "802               3                        4       1               2   \n",
              "803               3                        2       1               4   \n",
              "\n",
              "     JobLevel  ...  PerformanceRating  RelationshipSatisfaction  \\\n",
              "0           2  ...                  3                         1   \n",
              "1           1  ...                  3                         2   \n",
              "2           1  ...                  3                         2   \n",
              "3           1  ...                  4                         2   \n",
              "4           1  ...                  3                         3   \n",
              "..        ...  ...                ...                       ...   \n",
              "799         2  ...                  3                         3   \n",
              "800         3  ...                  3                         1   \n",
              "801         2  ...                  4                         2   \n",
              "802         2  ...                  3                         4   \n",
              "803         2  ...                  3                         1   \n",
              "\n",
              "     StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
              "0                   0                  8                      0   \n",
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              "2                   0                  6                      4   \n",
              "3                   0                 10                      4   \n",
              "4                   0                  8                      2   \n",
              "..                ...                ...                    ...   \n",
              "799                 1                 17                      3   \n",
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              "801                 1                  6                      0   \n",
              "802                 0                 17                      3   \n",
              "803                 0                  6                      3   \n",
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              "     WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
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              "3                  3               5                   3   \n",
              "4                  3               4                   2   \n",
              "..               ...             ...                 ...   \n",
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              "0                          0                     5  \n",
              "1                          0                     0  \n",
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              "..                       ...                   ...  \n",
              "799                        0                     3  \n",
              "800                        1                     7  \n",
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              "802                        0                     8  \n",
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              "<p>804 rows × 27 columns</p>\n",
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              "    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-115e551f-03a4-4ac5-81e2-8286549fa176 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": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbfCgEjxjDUQ",
        "outputId": "afc32f14-938f-4dfb-fad4-faa5c2701c36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      1\n",
              "3      1\n",
              "4      1\n",
              "      ..\n",
              "799    0\n",
              "800    0\n",
              "801    0\n",
              "802    0\n",
              "803    0\n",
              "Name: Attrition, Length: 804, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "import pandas as pd\n",
        "\n",
        "target = pd.Series(target)\n",
        "\n",
        "smote = SMOTE(random_state=42)\n",
        "\n",
        "oversampled_features, oversampled_target = smote.fit_resample(features, target)\n",
        "\n",
        "print(\"Original data shape:\", features.shape, target.shape)\n",
        "print(\"Oversampled data shape:\", oversampled_features.shape, oversampled_target.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Omq5OQNZLyKz",
        "outputId": "06f27e6b-eacc-4a99-8ade-f2c6e516b260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original data shape: (804, 27) (804,)\n",
            "Oversampled data shape: (1134, 27) (1134,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(features,target,test_size=0.1,random_state=42)"
      ],
      "metadata": {
        "id": "mEPvCMO-jDQ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "svm=SVC()"
      ],
      "metadata": {
        "id": "W3foL9cnjDOG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report,accuracy_score"
      ],
      "metadata": {
        "id": "3DDiCn2CjDLs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cr=classification_report\n",
        "ac=accuracy_score"
      ],
      "metadata": {
        "id": "fwkEdSchjDJG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svm.fit(X_train,y_train)\n",
        "y_pred=svm.predict(X_test)\n",
        "print(classification_report(y_test,y_pred))\n",
        "ac(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4nVSkFLjDG6",
        "outputId": "9b708bf7-befd-48ae-bcf9-c87f5a70944c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      1.00      0.84        59\n",
            "           1       0.00      0.00      0.00        22\n",
            "\n",
            "    accuracy                           0.73        81\n",
            "   macro avg       0.36      0.50      0.42        81\n",
            "weighted avg       0.53      0.73      0.61        81\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7283950617283951"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svm.score(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oytk4LFdjDEY",
        "outputId": "5f327b1a-b6cb-408d-fc00-70cbdc508418"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7026279391424619"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oFzSv1B_gr9I"
      },
      "outputs": [],
      "source": [
        "def predict():\n",
        "    a1=eval(input(\"Enter age:\"))\n",
        "    a2=eval(input(\"Enter Travel Class:\"))\n",
        "    a3=eval(input(\"Enter Department No:\"))\n",
        "    a4=eval(input(\"Enter Distance:\"))\n",
        "    a5=eval(input(\"Enter Educational Level:\"))\n",
        "    a6=eval(input(\"Enter Education Field:\"))\n",
        "    a7=eval(input(\"Enter Environment satisfaction Level:\"))\n",
        "    a8=eval(input(\"Enter Gender:\"))\n",
        "    a9=eval(input(\"Enter job involment:\"))\n",
        "    a10=eval(input(\"Enter job level:\"))\n",
        "    a11=eval(input(\"Enter job role:\"))\n",
        "    a12=eval(input(\"Enter satisfaction level:\"))\n",
        "    a13=eval(input(\"Enter marital status:\"))\n",
        "    a26=eval(input(\"Enter salary:\"))\n",
        "    a14=eval(input(\"Enter number of companies worked:\"))\n",
        "    a15=eval(input(\"Enter Overtime:\"))\n",
        "    a27=eval(input(\"Enter salary hike:\"))\n",
        "    a16=eval(input(\"Enter employee performance rating:\"))\n",
        "    a17=eval(input(\"Enter relationship stisfaction level:\"))\n",
        "    a18=eval(input(\"Enter stock option level:\"))\n",
        "    a19=eval(input(\"Enter years of work experience:\"))\n",
        "    a20=eval(input(\"Enter trainning time last year:\"))\n",
        "    a21=eval(input(\"Enter level of work life balance:\"))\n",
        "    a22=eval(input(\"Enter year at company worked:\"))\n",
        "    a23=eval(input(\"Enter years in current role:\"))\n",
        "    a24=eval(input(\"Enter years since last performance:\"))\n",
        "    a25=eval(input(\"Enter years with current manager:\"))\n",
        "\n",
        "    newob=[a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13,a26,a14,a15,a27,a16,a17,a18,a19,a20,a21,a22,a23,a24,a25]\n",
        "    ans=svm.predict([newob])[0]\n",
        "    if ans==1:\n",
        "        print(\"Employee will leave the company\")\n",
        "    else:\n",
        "        print(\"Employee will stay\")\n",
        "        return ans\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4ybqQQwgsAk",
        "outputId": "4c4047d5-7990-43d6-affb-aaea054a59ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter age:28\n",
            "Enter Travel Class:2\n",
            "Enter Department No:1\n",
            "Enter Distance:24\n",
            "Enter Educational Level:3\n",
            "Enter Education Field:0\n",
            "Enter Environment satisfaction Level:3\n",
            "Enter Gender:1\n",
            "Enter job involment:2\n",
            "Enter job level:1\n",
            "Enter job role:2\n",
            "Enter satisfaction level:3\n",
            "Enter marital status:2\n",
            "Enter salary:2028\n",
            "Enter number of companies worked:5\n",
            "Enter Overtime:1\n",
            "Enter salary hike:14\n",
            "Enter employee performance rating:3\n",
            "Enter relationship stisfaction level:2\n",
            "Enter stock option level:0\n",
            "Enter years of work experience:6\n",
            "Enter trainning time last year:4\n",
            "Enter level of work life balance:3\n",
            "Enter year at company worked:4\n",
            "Enter years in current role:2\n",
            "Enter years since last performance:0\n",
            "Enter years with current manager:3\n",
            "Employee will stay\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        " predict()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "original_values = {\n",
        "    'Attrition': ['Yes', 'No'],\n",
        "    'BusinessTravel': ['Travel_Rarely', 'Travel_Frequently', 'Non-Travel'],\n",
        "    'Department': ['Sales', 'Research & Development', 'Human Resources'],\n",
        "    'EducationField': ['Life Sciences', 'Medical', 'Marketing', 'Technical Degree', 'Other'],\n",
        "    'Gender': ['Male', 'Female'],\n",
        "    'JobRole': ['Sales Executive', 'Research Scientist', 'Laboratory Technician', 'Manufacturing Director', 'Healthcare Representative', 'Manager', 'Sales Representative', 'Research Director', 'Human Resources'],\n",
        "    'MaritalStatus': ['Single', 'Married', 'Divorced'],\n",
        "    'OverTime': ['Yes', 'No']\n",
        "}\n",
        "\n",
        "transformed_values = {\n",
        "    'Attrition': le.fit_transform(original_values['Attrition']),\n",
        "    'BusinessTravel': le.fit_transform(original_values['BusinessTravel']),\n",
        "    'Department': le.fit_transform(original_values['Department']),\n",
        "    'EducationField': le.fit_transform(original_values['EducationField']),\n",
        "    'Gender': le.fit_transform(original_values['Gender']),\n",
        "    'JobRole': le.fit_transform(original_values['JobRole']),\n",
        "    'MaritalStatus': le.fit_transform(original_values['MaritalStatus']),\n",
        "    'OverTime': le.fit_transform(original_values['OverTime'])\n",
        "}\n",
        "\n",
        "for column_name in original_values:\n",
        "    print(f\"Original values for {column_name}: {original_values[column_name]}\")\n",
        "    print(f\"Transformed values for {column_name}: {list(transformed_values[column_name])}\\n\")\n"
      ],
      "metadata": {
        "id": "aQNL9wbXZLrh"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}