IBM_HR_Machine_Learning.ipynb

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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.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)\n",
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "ZySxveYiJQhg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"/content/HR-Employee-Attrition.csv\")"
      ],
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        "id": "gbjEXiDMJXy1"
      },
      "execution_count": null,
      "outputs": []
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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",
              "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
              "0                 1          2  Life Sciences              1               1   \n",
              "1                 8          1  Life Sciences              1               2   \n",
              "2                 2          2          Other              1               4   \n",
              "3                 3          4  Life Sciences              1               5   \n",
              "4                 2          1        Medical              1               7   \n",
              "\n",
              "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
              "0  ...                         1            80                 0   \n",
              "1  ...                         4            80                 1   \n",
              "2  ...                         2            80                 0   \n",
              "3  ...                         3            80                 0   \n",
              "4  ...                         4            80                 1   \n",
              "\n",
              "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
              "0                  8                      0               1               6   \n",
              "1                 10                      3               3              10   \n",
              "2                  7                      3               3               0   \n",
              "3                  8                      3               3               8   \n",
              "4                  6                      3               3               2   \n",
              "\n",
              "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                  4                        0                     5  \n",
              "1                  7                        1                     7  \n",
              "2                  0                        0                     0  \n",
              "3                  7                        3                     0  \n",
              "4                  2                        2                     2  \n",
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              "(1470, 35)"
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            "<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": [
        "def missingvalues(dataset):\n",
        "    return dataset.isnull().sum()\n",
        "missingvalues(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aYoR73mMJs8P",
        "outputId": "bccbd0c6-277c-4364-e301-75ff9cb61b0e"
      },
      "execution_count": null,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_rows = data.duplicated().any()\n",
        "duplicate_rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBl7McPaJs-k",
        "outputId": "d6292775-89d9-4c61-80dc-4e1617830b50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Machine Learning"
      ],
      "metadata": {
        "id": "jjBQeJdAXm2p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "le = LabelEncoder()\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['JobRole'] = le.fit_transform(data['JobRole'])\n",
        "data['Gender'] = le.fit_transform(data['Gender'])\n",
        "data['MaritalStatus'] = le.fit_transform(data['MaritalStatus'])\n",
        "data['OverTime'] = le.fit_transform(data['OverTime'])"
      ],
      "metadata": {
        "id": "nvVARY5vat1a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "KGLn6xMbqevG",
        "outputId": "f9797499-6296-4f2b-cc92-fd7d93d7c980"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age Attrition  BusinessTravel  DailyRate  Department  DistanceFromHome  \\\n",
              "0   41       Yes               2       1102           2                 1   \n",
              "1   49        No               1        279           1                 8   \n",
              "2   37       Yes               2       1373           1                 2   \n",
              "3   33        No               1       1392           1                 3   \n",
              "4   27        No               2        591           1                 2   \n",
              "\n",
              "   Education  EducationField  EmployeeCount  EmployeeNumber  ...  \\\n",
              "0          2               1              1               1  ...   \n",
              "1          1               1              1               2  ...   \n",
              "2          2               4              1               4  ...   \n",
              "3          4               1              1               5  ...   \n",
              "4          1               3              1               7  ...   \n",
              "\n",
              "   RelationshipSatisfaction  StandardHours  StockOptionLevel  \\\n",
              "0                         1             80                 0   \n",
              "1                         4             80                 1   \n",
              "2                         2             80                 0   \n",
              "3                         3             80                 0   \n",
              "4                         4             80                 1   \n",
              "\n",
              "   TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\n",
              "0                  8                      0                1               6   \n",
              "1                 10                      3                3              10   \n",
              "2                  7                      3                3               0   \n",
              "3                  8                      3                3               8   \n",
              "4                  6                      3                3               2   \n",
              "\n",
              "   YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                   4                        0                     5  \n",
              "1                   7                        1                     7  \n",
              "2                   0                        0                     0  \n",
              "3                   7                        3                     0  \n",
              "4                   2                        2                     2  \n",
              "\n",
              "[5 rows x 35 columns]"
            ],
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              "<p>5 rows × 35 columns</p>\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-6f67f56a-0a7d-488f-b308-e0a1c1029ea2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6f67f56a-0a7d-488f-b308-e0a1c1029ea2');\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",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
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              "    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",
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              "      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",
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              "        document.querySelector('#df-6ff49b66-cded-40b5-854e-da1777a589d0 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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=data['Attrition']\n",
        "X=data.drop(['EmployeeCount','Attrition','EmployeeNumber','Over18','StandardHours'],axis=1)"
      ],
      "metadata": {
        "id": "aF1wWpoebdix"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=1)"
      ],
      "metadata": {
        "id": "1n-PYbJzbilR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "standard_scaler = StandardScaler()\n",
        "\n",
        "X_train_standardized = standard_scaler.fit_transform(X_train)\n",
        "X_test_standardized = standard_scaler.transform(X_test)\n",
        "\n",
        "X_standardized = standard_scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "q17bRB-2bmvk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_max_scaler = MinMaxScaler()\n",
        "\n",
        "X_train_normalized = min_max_scaler.fit_transform(X_train)\n",
        "X_test_normalized = min_max_scaler.transform(X_test)\n",
        "\n",
        "X_normalized = min_max_scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "biJOuV3cbm7-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "X, y = make_classification(n_samples=1000, n_features=10, n_classes=2, random_state=42)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "def train_predict_evaluate(model, X_train, y_train, X_test, y_test):\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    print(\"Accuracy: \", accuracy_score(y_test, y_pred))\n",
        "    print(\"Precision: \", precision_score(y_test, y_pred))\n",
        "    print(\"Recall: \", recall_score(y_test, y_pred))\n",
        "    print(\"F1 Score: \", f1_score(y_test, y_pred))\n",
        "    print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
        "\n",
        "    fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "    plt.plot(fpr, tpr, color='green', label='ROC curve (area = %0.2f)' % roc_auc_score(y_test, y_pred))\n",
        "    plt.plot([0, 1], [0, 1], color='orange', linestyle='--')\n",
        "    plt.xlabel(\"False Positive Rate\")\n",
        "    plt.ylabel(\"True Positive Rate\")\n",
        "    plt.title(\"ROC Curve\")\n",
        "    plt.legend(loc=\"lower right\")\n",
        "    plt.show()\n",
        "\n",
        "model = LogisticRegression(max_iter=100000)\n",
        "train_predict_evaluate(model, X_train, y_train, X_test, y_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "jd8KdeLKhCOr",
        "outputId": "62b4f8ce-0c14-47ad-ccc8-7f98fc3b1ea0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.83\n",
            "Precision:  0.8666666666666667\n",
            "Recall:  0.8198198198198198\n",
            "F1 Score:  0.8425925925925926\n",
            "Confusion Matrix:\n",
            " [[75 14]\n",
            " [20 91]]\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": [
        "x = data.iloc[:,:].values\n",
        "x= np.delete(x,1,axis=1)\n",
        "print(x[:10,:])"
      ],
      "metadata": {
        "id": "j-ctdotFOpTQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfa921cc-0e13-4d6a-f0a5-402142e87b54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[41 2 1102 2 1 2 1 1 1 2 0 94 3 2 7 4 2 5993 19479 8 'Y' 1 11 3 1 80 0 8\n",
            "  0 1 6 4 0 5]\n",
            " [49 1 279 1 8 1 1 1 2 3 1 61 2 2 6 2 1 5130 24907 1 'Y' 0 23 4 4 80 1 10\n",
            "  3 3 10 7 1 7]\n",
            " [37 2 1373 1 2 2 4 1 4 4 1 92 2 1 2 3 2 2090 2396 6 'Y' 1 15 3 2 80 0 7\n",
            "  3 3 0 0 0 0]\n",
            " [33 1 1392 1 3 4 1 1 5 4 0 56 3 1 6 3 1 2909 23159 1 'Y' 1 11 3 3 80 0 8\n",
            "  3 3 8 7 3 0]\n",
            " [27 2 591 1 2 1 3 1 7 1 1 40 3 1 2 2 1 3468 16632 9 'Y' 0 12 3 4 80 1 6\n",
            "  3 3 2 2 2 2]\n",
            " [32 1 1005 1 2 2 1 1 8 4 1 79 3 1 2 4 2 3068 11864 0 'Y' 0 13 3 3 80 0 8\n",
            "  2 2 7 7 3 6]\n",
            " [59 2 1324 1 3 3 3 1 10 3 0 81 4 1 2 1 1 2670 9964 4 'Y' 1 20 4 1 80 3\n",
            "  12 3 2 1 0 0 0]\n",
            " [30 2 1358 1 24 1 1 1 11 4 1 67 3 1 2 3 0 2693 13335 1 'Y' 0 22 4 2 80 1\n",
            "  1 2 3 1 0 0 0]\n",
            " [38 1 216 1 23 3 1 1 12 4 1 44 2 3 4 3 2 9526 8787 0 'Y' 0 21 4 2 80 0\n",
            "  10 2 3 9 7 1 8]\n",
            " [36 2 1299 1 27 3 3 1 13 3 1 94 3 2 0 3 1 5237 16577 6 'Y' 0 13 3 2 80 2\n",
            "  17 3 2 7 7 7 7]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = data.Attrition\n",
        "y.head()"
      ],
      "metadata": {
        "id": "iOdFl66_QUbJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dd71f76-a059-4268-cba6-795b211f2a9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Yes\n",
              "1     No\n",
              "2    Yes\n",
              "3     No\n",
              "4     No\n",
              "Name: Attrition, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = LabelEncoder().fit_transform(y)\n",
        "print(y[:10])"
      ],
      "metadata": {
        "id": "ObZIN64QQUdT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec1b6092-ae78-409f-cf17-43b0585bb199"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[:,10]=LabelEncoder().fit_transform(x[:,10])\n",
        "x[:,20]=LabelEncoder().fit_transform(x[:,20])\n",
        "x[:,21]=LabelEncoder().fit_transform(x[:,21])\n",
        "print(x[:10,:])"
      ],
      "metadata": {
        "id": "Y8xT2EhJQUgB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04c5ef89-e9dd-4959-8f58-a370c8827ad8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[41 2 1102 2 1 2 1 1 1 2 0 94 3 2 7 4 2 5993 19479 8 0 1 11 3 1 80 0 8 0\n",
            "  1 6 4 0 5]\n",
            " [49 1 279 1 8 1 1 1 2 3 1 61 2 2 6 2 1 5130 24907 1 0 0 23 4 4 80 1 10 3\n",
            "  3 10 7 1 7]\n",
            " [37 2 1373 1 2 2 4 1 4 4 1 92 2 1 2 3 2 2090 2396 6 0 1 15 3 2 80 0 7 3\n",
            "  3 0 0 0 0]\n",
            " [33 1 1392 1 3 4 1 1 5 4 0 56 3 1 6 3 1 2909 23159 1 0 1 11 3 3 80 0 8 3\n",
            "  3 8 7 3 0]\n",
            " [27 2 591 1 2 1 3 1 7 1 1 40 3 1 2 2 1 3468 16632 9 0 0 12 3 4 80 1 6 3\n",
            "  3 2 2 2 2]\n",
            " [32 1 1005 1 2 2 1 1 8 4 1 79 3 1 2 4 2 3068 11864 0 0 0 13 3 3 80 0 8 2\n",
            "  2 7 7 3 6]\n",
            " [59 2 1324 1 3 3 3 1 10 3 0 81 4 1 2 1 1 2670 9964 4 0 1 20 4 1 80 3 12\n",
            "  3 2 1 0 0 0]\n",
            " [30 2 1358 1 24 1 1 1 11 4 1 67 3 1 2 3 0 2693 13335 1 0 0 22 4 2 80 1 1\n",
            "  2 3 1 0 0 0]\n",
            " [38 1 216 1 23 3 1 1 12 4 1 44 2 3 4 3 2 9526 8787 0 0 0 21 4 2 80 0 10\n",
            "  2 3 9 7 1 8]\n",
            " [36 2 1299 1 27 3 3 1 13 3 1 94 3 2 0 3 1 5237 16577 6 0 0 13 3 2 80 2\n",
            "  17 3 2 7 7 7 7]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_columns = [1, 3, 6, 14, 16]\n",
        "ct = ColumnTransformer(\n",
        "    transformers=[('encoder', OneHotEncoder(), categorical_columns)],\n",
        "    remainder='passthrough'\n",
        ")\n",
        "x_transformed = ct.fit_transform(x)\n",
        "print(x_transformed[:10, :])"
      ],
      "metadata": {
        "id": "JOSapGLuQUh1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa5e8db2-e01c-4203-bf96-d5a50a9583fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
            "  0.0 1.0 0.0 0.0 0.0 1.0 41 1102 1 2 1 1 2 0 94 3 2 4 5993 19479 8 0 1\n",
            "  11 3 1 80 0 8 0 1 6 4 0 5]\n",
            " [0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
            "  1.0 0.0 0.0 0.0 1.0 0.0 49 279 8 1 1 2 3 1 61 2 2 2 5130 24907 1 0 0 23\n",
            "  4 4 80 1 10 3 3 10 7 1 7]\n",
            " [0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0\n",
            "  0.0 0.0 0.0 0.0 0.0 1.0 37 1373 2 2 1 4 4 1 92 2 1 3 2090 2396 6 0 1 15\n",
            "  3 2 80 0 7 3 3 0 0 0 0]\n",
            " [0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
            "  1.0 0.0 0.0 0.0 1.0 0.0 33 1392 3 4 1 5 4 0 56 3 1 3 2909 23159 1 0 1\n",
            "  11 3 3 80 0 8 3 3 8 7 3 0]\n",
            " [0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0\n",
            "  0.0 0.0 0.0 0.0 1.0 0.0 27 591 2 1 1 7 1 1 40 3 1 2 3468 16632 9 0 0 12\n",
            "  3 4 80 1 6 3 3 2 2 2 2]\n",
            " [0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0\n",
            "  0.0 0.0 0.0 0.0 0.0 1.0 32 1005 2 2 1 8 4 1 79 3 1 4 3068 11864 0 0 0\n",
            "  13 3 3 80 0 8 2 2 7 7 3 6]\n",
            " [0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0\n",
            "  0.0 0.0 0.0 0.0 1.0 0.0 59 1324 3 3 1 10 3 0 81 4 1 1 2670 9964 4 0 1\n",
            "  20 4 1 80 3 12 3 2 1 0 0 0]\n",
            " [0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0\n",
            "  0.0 0.0 0.0 1.0 0.0 0.0 30 1358 24 1 1 11 4 1 67 3 1 3 2693 13335 1 0 0\n",
            "  22 4 2 80 1 1 2 3 1 0 0 0]\n",
            " [0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0\n",
            "  0.0 0.0 0.0 0.0 0.0 1.0 38 216 23 3 1 12 4 1 44 2 3 3 9526 8787 0 0 0\n",
            "  21 4 2 80 0 10 2 3 9 7 1 8]\n",
            " [0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0\n",
            "  0.0 0.0 0.0 0.0 1.0 0.0 36 1299 27 3 1 13 3 1 94 3 2 3 5237 16577 6 0 0\n",
            "  13 3 2 80 2 17 3 2 7 7 7 7]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)"
      ],
      "metadata": {
        "id": "HA5U3WLmQUk_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "metadata": {
        "id": "6MCkdo4rQUms"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression(solver='lbfgs')\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "print(\"Accuracy {0:.2f}%\".format(100*accuracy_score(y_pred, y_test)))\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "rDI9LfyAQUoi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38e546ad-fcd8-4566-92a1-86b724fc690c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 84.13%\n",
            "[[351  10]\n",
            " [ 60  20]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.97      0.91       361\n",
            "           1       0.67      0.25      0.36        80\n",
            "\n",
            "    accuracy                           0.84       441\n",
            "   macro avg       0.76      0.61      0.64       441\n",
            "weighted avg       0.82      0.84      0.81       441\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "y_pred_proba = model.predict_proba(x_test)[:, 1]\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
        "\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(fpr, tpr, label='ROC curve (AUC = {0:.4f})'.format(roc_auc))\n",
        "\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZJNGmCLzswiY",
        "outputId": "30c0acd4-a49a-482e-8ed0-3320d1418d7e"
      },
      "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": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier()\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "print(\"Accuracy {0:.2f}%\".format(100*accuracy_score(y_pred, y_test)))\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "fqKryZDqQUqj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f71a367-78fa-4491-cd38-6d975be693a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 84.35%\n",
            "[[360   1]\n",
            " [ 68  12]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      1.00      0.91       361\n",
            "           1       0.92      0.15      0.26        80\n",
            "\n",
            "    accuracy                           0.84       441\n",
            "   macro avg       0.88      0.57      0.59       441\n",
            "weighted avg       0.86      0.84      0.79       441\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob_rf = model.predict_proba(x_test)[:, 1]\n",
        "\n",
        "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, y_prob_rf)\n",
        "roc_auc_rf = roc_auc_score(y_test, y_prob_rf)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr_rf, tpr_rf, color='green', lw=2, label=f'AUC = {roc_auc_rf:.2f}')\n",
        "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate (FPR)')\n",
        "plt.ylabel('True Positive Rate (TPR)')\n",
        "plt.title('ROC Curve - Random Forest Classifier')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "print(\"AUC-ROC Score:\", roc_auc_rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "QWv8-NFQvpSH",
        "outputId": "6c9b5c44-8e70-4b06-fd7a-d686f3012116"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC-ROC Score: 0.7956890581717452\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "oe=OrdinalEncoder()"
      ],
      "metadata": {
        "id": "BVKaggM8QUtx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colname=data.select_dtypes(object).columns"
      ],
      "metadata": {
        "id": "7xF5iQRfQUvg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[colname]=oe.fit_transform(data[colname])\n",
        "data.describe().T"
      ],
      "metadata": {
        "id": "8w7jGC7DQUxN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1d6f5220-eaed-4bd5-8ed8-031d61c40959"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           count          mean          std     min      25%  \\\n",
              "Age                       1470.0     36.923810     9.135373    18.0    30.00   \n",
              "Attrition                 1470.0      0.161224     0.367863     0.0     0.00   \n",
              "BusinessTravel            1470.0      1.607483     0.665455     0.0     1.00   \n",
              "DailyRate                 1470.0    802.485714   403.509100   102.0   465.00   \n",
              "Department                1470.0      1.260544     0.527792     0.0     1.00   \n",
              "DistanceFromHome          1470.0      9.192517     8.106864     1.0     2.00   \n",
              "Education                 1470.0      2.912925     1.024165     1.0     2.00   \n",
              "EducationField            1470.0      2.247619     1.331369     0.0     1.00   \n",
              "EmployeeCount             1470.0      1.000000     0.000000     1.0     1.00   \n",
              "EmployeeNumber            1470.0   1024.865306   602.024335     1.0   491.25   \n",
              "EnvironmentSatisfaction   1470.0      2.721769     1.093082     1.0     2.00   \n",
              "Gender                    1470.0      0.600000     0.490065     0.0     0.00   \n",
              "HourlyRate                1470.0     65.891156    20.329428    30.0    48.00   \n",
              "JobInvolvement            1470.0      2.729932     0.711561     1.0     2.00   \n",
              "JobLevel                  1470.0      2.063946     1.106940     1.0     1.00   \n",
              "JobRole                   1470.0      4.458503     2.461821     0.0     2.00   \n",
              "JobSatisfaction           1470.0      2.728571     1.102846     1.0     2.00   \n",
              "MaritalStatus             1470.0      1.097279     0.730121     0.0     1.00   \n",
              "MonthlyIncome             1470.0   6502.931293  4707.956783  1009.0  2911.00   \n",
              "MonthlyRate               1470.0  14313.103401  7117.786044  2094.0  8047.00   \n",
              "NumCompaniesWorked        1470.0      2.693197     2.498009     0.0     1.00   \n",
              "Over18                    1470.0      0.000000     0.000000     0.0     0.00   \n",
              "OverTime                  1470.0      0.282993     0.450606     0.0     0.00   \n",
              "PercentSalaryHike         1470.0     15.209524     3.659938    11.0    12.00   \n",
              "PerformanceRating         1470.0      3.153741     0.360824     3.0     3.00   \n",
              "RelationshipSatisfaction  1470.0      2.712245     1.081209     1.0     2.00   \n",
              "StandardHours             1470.0     80.000000     0.000000    80.0    80.00   \n",
              "StockOptionLevel          1470.0      0.793878     0.852077     0.0     0.00   \n",
              "TotalWorkingYears         1470.0     11.279592     7.780782     0.0     6.00   \n",
              "TrainingTimesLastYear     1470.0      2.799320     1.289271     0.0     2.00   \n",
              "WorkLifeBalance           1470.0      2.761224     0.706476     1.0     2.00   \n",
              "YearsAtCompany            1470.0      7.008163     6.126525     0.0     3.00   \n",
              "YearsInCurrentRole        1470.0      4.229252     3.623137     0.0     2.00   \n",
              "YearsSinceLastPromotion   1470.0      2.187755     3.222430     0.0     0.00   \n",
              "YearsWithCurrManager      1470.0      4.123129     3.568136     0.0     2.00   \n",
              "\n",
              "                              50%       75%      max  \n",
              "Age                          36.0     43.00     60.0  \n",
              "Attrition                     0.0      0.00      1.0  \n",
              "BusinessTravel                2.0      2.00      2.0  \n",
              "DailyRate                   802.0   1157.00   1499.0  \n",
              "Department                    1.0      2.00      2.0  \n",
              "DistanceFromHome              7.0     14.00     29.0  \n",
              "Education                     3.0      4.00      5.0  \n",
              "EducationField                2.0      3.00      5.0  \n",
              "EmployeeCount                 1.0      1.00      1.0  \n",
              "EmployeeNumber             1020.5   1555.75   2068.0  \n",
              "EnvironmentSatisfaction       3.0      4.00      4.0  \n",
              "Gender                        1.0      1.00      1.0  \n",
              "HourlyRate                   66.0     83.75    100.0  \n",
              "JobInvolvement                3.0      3.00      4.0  \n",
              "JobLevel                      2.0      3.00      5.0  \n",
              "JobRole                       5.0      7.00      8.0  \n",
              "JobSatisfaction               3.0      4.00      4.0  \n",
              "MaritalStatus                 1.0      2.00      2.0  \n",
              "MonthlyIncome              4919.0   8379.00  19999.0  \n",
              "MonthlyRate               14235.5  20461.50  26999.0  \n",
              "NumCompaniesWorked            2.0      4.00      9.0  \n",
              "Over18                        0.0      0.00      0.0  \n",
              "OverTime                      0.0      1.00      1.0  \n",
              "PercentSalaryHike            14.0     18.00     25.0  \n",
              "PerformanceRating             3.0      3.00      4.0  \n",
              "RelationshipSatisfaction      3.0      4.00      4.0  \n",
              "StandardHours                80.0     80.00     80.0  \n",
              "StockOptionLevel              1.0      1.00      3.0  \n",
              "TotalWorkingYears            10.0     15.00     40.0  \n",
              "TrainingTimesLastYear         3.0      3.00      6.0  \n",
              "WorkLifeBalance               3.0      3.00      4.0  \n",
              "YearsAtCompany                5.0      9.00     40.0  \n",
              "YearsInCurrentRole            3.0      7.00     18.0  \n",
              "YearsSinceLastPromotion       1.0      3.00     15.0  \n",
              "YearsWithCurrManager          3.0      7.00     17.0  "
            ],
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
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              "  <tbody>\n",
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              "      <th>Age</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>36.923810</td>\n",
              "      <td>9.135373</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30.00</td>\n",
              "      <td>36.0</td>\n",
              "      <td>43.00</td>\n",
              "      <td>60.0</td>\n",
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              "    <tr>\n",
              "      <th>Attrition</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>0.161224</td>\n",
              "      <td>0.367863</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>BusinessTravel</th>\n",
              "      <td>1470.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>DailyRate</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>802.485714</td>\n",
              "      <td>403.509100</td>\n",
              "      <td>102.0</td>\n",
              "      <td>465.00</td>\n",
              "      <td>802.0</td>\n",
              "      <td>1157.00</td>\n",
              "      <td>1499.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Department</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>1.260544</td>\n",
              "      <td>0.527792</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>9.192517</td>\n",
              "      <td>8.106864</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>7.0</td>\n",
              "      <td>14.00</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>2.912925</td>\n",
              "      <td>1.024165</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EducationField</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>2.247619</td>\n",
              "      <td>1.331369</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EmployeeCount</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EmployeeNumber</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>1024.865306</td>\n",
              "      <td>602.024335</td>\n",
              "      <td>1.0</td>\n",
              "      <td>491.25</td>\n",
              "      <td>1020.5</td>\n",
              "      <td>1555.75</td>\n",
              "      <td>2068.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>2.721769</td>\n",
              "      <td>1.093082</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.490065</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HourlyRate</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>65.891156</td>\n",
              "      <td>20.329428</td>\n",
              "      <td>30.0</td>\n",
              "      <td>48.00</td>\n",
              "      <td>66.0</td>\n",
              "      <td>83.75</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JobInvolvement</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>2.729932</td>\n",
              "      <td>0.711561</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JobLevel</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>2.063946</td>\n",
              "      <td>1.106940</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JobRole</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>4.458503</td>\n",
              "      <td>2.461821</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>2.728571</td>\n",
              "      <td>1.102846</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MaritalStatus</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>1.097279</td>\n",
              "      <td>0.730121</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>6502.931293</td>\n",
              "      <td>4707.956783</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>2911.00</td>\n",
              "      <td>4919.0</td>\n",
              "      <td>8379.00</td>\n",
              "      <td>19999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MonthlyRate</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>14313.103401</td>\n",
              "      <td>7117.786044</td>\n",
              "      <td>2094.0</td>\n",
              "      <td>8047.00</td>\n",
              "      <td>14235.5</td>\n",
              "      <td>20461.50</td>\n",
              "      <td>26999.0</td>\n",
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              "    <tr>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>2.693197</td>\n",
              "      <td>2.498009</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>9.0</td>\n",
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              "    <tr>\n",
              "      <th>Over18</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OverTime</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>0.282993</td>\n",
              "      <td>0.450606</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>15.209524</td>\n",
              "      <td>3.659938</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12.00</td>\n",
              "      <td>14.0</td>\n",
              "      <td>18.00</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PerformanceRating</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>3.153741</td>\n",
              "      <td>0.360824</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>2.712245</td>\n",
              "      <td>1.081209</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StandardHours</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80.00</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80.00</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>0.793878</td>\n",
              "      <td>0.852077</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>11.279592</td>\n",
              "      <td>7.780782</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.00</td>\n",
              "      <td>40.0</td>\n",
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              "    <tr>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>2.799320</td>\n",
              "      <td>1.289271</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>6.0</td>\n",
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              "    <tr>\n",
              "      <th>WorkLifeBalance</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>2.761224</td>\n",
              "      <td>0.706476</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>4.0</td>\n",
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              "    <tr>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <td>1470.0</td>\n",
              "      <td>7.008163</td>\n",
              "      <td>6.126525</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.00</td>\n",
              "      <td>40.0</td>\n",
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              "    <tr>\n",
              "      <th>YearsInCurrentRole</th>\n",
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              "      <td>4.229252</td>\n",
              "      <td>3.623137</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.00</td>\n",
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              "    <tr>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
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              "      <td>2.187755</td>\n",
              "      <td>3.222430</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.00</td>\n",
              "      <td>17.0</td>\n",
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              "    }\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-b1e4463a-1762-4771-973c-cc5d6f1ede4c 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-b1e4463a-1762-4771-973c-cc5d6f1ede4c');\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-4317fc3a-9f70-4175-8d68-c172bcb3e81c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4317fc3a-9f70-4175-8d68-c172bcb3e81c')\"\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-4317fc3a-9f70-4175-8d68-c172bcb3e81c 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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features=data.iloc[:, 2:]\n",
        "features['Age']=data.iloc[:,0:1]\n",
        "target=data.iloc[:,1]"
      ],
      "metadata": {
        "id": "ZqR12aB_QUz_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features.head()"
      ],
      "metadata": {
        "id": "-p1WasDAQU2B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "acf9d07a-5577-401b-fe14-2c5999de9185"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BusinessTravel  DailyRate  Department  DistanceFromHome  Education  \\\n",
              "0               2       1102           2                 1          2   \n",
              "1               1        279           1                 8          1   \n",
              "2               2       1373           1                 2          2   \n",
              "3               1       1392           1                 3          4   \n",
              "4               2        591           1                 2          1   \n",
              "\n",
              "   EducationField  EmployeeCount  EmployeeNumber  EnvironmentSatisfaction  \\\n",
              "0               1              1               1                        2   \n",
              "1               1              1               2                        3   \n",
              "2               4              1               4                        4   \n",
              "3               1              1               5                        4   \n",
              "4               3              1               7                        1   \n",
              "\n",
              "   Gender  ...  StandardHours  StockOptionLevel  TotalWorkingYears  \\\n",
              "0       0  ...             80                 0                  8   \n",
              "1       1  ...             80                 1                 10   \n",
              "2       1  ...             80                 0                  7   \n",
              "3       0  ...             80                 0                  8   \n",
              "4       1  ...             80                 1                  6   \n",
              "\n",
              "   TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
              "0                      0                1               6                   4   \n",
              "1                      3                3              10                   7   \n",
              "2                      3                3               0                   0   \n",
              "3                      3                3               8                   7   \n",
              "4                      3                3               2                   2   \n",
              "\n",
              "   YearsSinceLastPromotion  YearsWithCurrManager  Age  \n",
              "0                        0                     5   41  \n",
              "1                        1                     7   49  \n",
              "2                        0                     0   37  \n",
              "3                        3                     0   33  \n",
              "4                        2                     2   27  \n",
              "\n",
              "[5 rows x 34 columns]"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>Department</th>\n",
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              "      <th>Education</th>\n",
              "      <th>EducationField</th>\n",
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              "      <th>EmployeeNumber</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
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              "      <th>...</th>\n",
              "      <th>StandardHours</th>\n",
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              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
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              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>1102</td>\n",
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              "      <th>1</th>\n",
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              "      <td>279</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>10</td>\n",
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              "      <td>49</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>1373</td>\n",
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              "      <td>37</td>\n",
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              "      <th>3</th>\n",
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              "      <td>1392</td>\n",
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              "      <td>33</td>\n",
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              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 34 columns</p>\n",
              "</div>\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",
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              "      padding: 0 0 0 0;\n",
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              "\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",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-aff1b265-b823-4a04-8de1-5df469a68722 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-aff1b265-b823-4a04-8de1-5df469a68722');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "            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",
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              "    </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-a5808080-82f7-4c81-b4a3-c1c773581706 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": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target.head()"
      ],
      "metadata": {
        "id": "8Q2dZrEfQU58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a965243-c6f6-4e6a-9a6a-c2c4c89de90d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.0\n",
              "1    0.0\n",
              "2    1.0\n",
              "3    0.0\n",
              "4    0.0\n",
              "Name: Attrition, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(features,target,test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "SmDCJjEPOpU_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "svm=SVC()"
      ],
      "metadata": {
        "id": "DI7kgyuuOpX7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score"
      ],
      "metadata": {
        "id": "vlUh9rLXOpaM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cr=classification_report\n",
        "ac=accuracy_score"
      ],
      "metadata": {
        "id": "L2mXfp22RsDc"
      },
      "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": {
        "id": "LGyY_FP-RsLN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6be4bfaa-61be-4b88-ec32-128aa33d3af8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.87      1.00      0.93       255\n",
            "         1.0       0.00      0.00      0.00        39\n",
            "\n",
            "    accuracy                           0.87       294\n",
            "   macro avg       0.43      0.50      0.46       294\n",
            "weighted avg       0.75      0.87      0.81       294\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": [
              "0.8673469387755102"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decision_scores = svm.decision_function(X_test)\n",
        "\n",
        "fpr_svm, tpr_svm, thresholds_svm = roc_curve(y_test, decision_scores)\n",
        "roc_auc_svm = roc_auc_score(y_test, decision_scores)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr_svm, tpr_svm, color='red', lw=2, label=f'AUC = {roc_auc_svm:.2f}')\n",
        "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate (FPR)')\n",
        "plt.ylabel('True Positive Rate (TPR)')\n",
        "plt.title('ROC Curve - Support Vector Machine (SVM)')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "print(\"AUC-ROC Score:\", roc_auc_svm)"
      ],
      "metadata": {
        "id": "6wpzVDBYRsRj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "b0c04c8b-03dd-453b-950c-6a14b2325376"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC-ROC Score: 0.537958773252891\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models = {\n",
        "    'Logistic Regression': LogisticRegression(solver='lbfgs'),\n",
        "    'Random Forest': RandomForestClassifier(),\n",
        "    'SVM': SVC()\n",
        "}\n",
        "\n",
        "results = []\n",
        "for model_name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    y_pred = model.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    confusion_matrix_data = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "    classification_report_dict = classification_report(y_test, y_pred, output_dict=True)\n",
        "\n",
        "    precision_score = classification_report_dict['macro avg']['precision']\n",
        "    recall_score = classification_report_dict['macro avg']['recall']\n",
        "    f1_score = classification_report_dict['macro avg']['f1-score']\n",
        "    support = classification_report_dict['macro avg']['support']\n",
        "\n",
        "    results.append({\n",
        "        'Model Name': model_name,\n",
        "        'Accuracy': accuracy,\n",
        "        'Precision': precision_score,\n",
        "        'Recall': recall_score,\n",
        "        'F1 Score': f1_score,\n",
        "        'Support': support,\n",
        "        'Confusion Matrix': confusion_matrix_data\n",
        "    })\n",
        "\n",
        "results_df = pd.DataFrame(results)\n",
        "\n",
        "print(results_df.to_string())"
      ],
      "metadata": {
        "id": "ULtEKG9NRsZX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4909be2-3102-418a-9670-766b4687cd42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_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",
            "/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": "stream",
          "name": "stdout",
          "text": [
            "            Model Name  Accuracy  Precision    Recall  F1 Score  Support     Confusion Matrix\n",
            "0  Logistic Regression  0.867347   0.433673  0.500000  0.464481      294  [[255, 0], [39, 0]]\n",
            "1        Random Forest  0.877551   0.839446  0.549321  0.557821      294  [[254, 1], [35, 4]]\n",
            "2                  SVM  0.867347   0.433673  0.500000  0.464481      294  [[255, 0], [39, 0]]\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models = {\n",
        "    'Logistic Regression': LogisticRegression(solver='lbfgs'),\n",
        "    'Random Forest': RandomForestClassifier(),\n",
        "    'SVM': SVC(probability=True)\n",
        "}\n",
        "\n",
        "results = []\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "for model_name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    y_pred = model.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    confusion_matrix_data = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "    classification_report_dict = classification_report(y_test, y_pred, output_dict=True)\n",
        "\n",
        "    precision_score = classification_report_dict['macro avg']['precision']\n",
        "    recall_score = classification_report_dict['macro avg']['recall']\n",
        "    f1_score = classification_report_dict['macro avg']['f1-score']\n",
        "    support = classification_report_dict['macro avg']['support']\n",
        "\n",
        "    fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:, 1])\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "\n",
        "    plt.plot(fpr, tpr, label=f'{model_name} (AUC = {roc_auc:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random Guessing')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 931
        },
        "id": "zjfavQYV9tqB",
        "outputId": "50283033-dc74-4249-d60c-17d76a7545e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_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",
            "/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",
            "/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": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.iloc[:, 2:]\n",
        "x['Age'] = data['Age']\n",
        "y = data['Attrition']\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "clf.fit(x, y)\n",
        "\n",
        "feature_importances = clf.feature_importances_\n",
        "feat_importances_rf = pd.Series(feature_importances, index=x.columns)\n",
        "top_feat_importances_rf = feat_importances_rf.nlargest(10)\n",
        "\n",
        "plt.figure(figsize=(6, 6))\n",
        "top_feat_importances_rf.sort_values().plot(kind='barh', color='skyblue')\n",
        "plt.xlabel('Feature Importance')\n",
        "plt.title('Top 10 Important Features Contributing to Attrition (Random Forest)')\n",
        "plt.show()\n",
        "\n",
        "log_reg = LogisticRegression(max_iter=1000, random_state=42)\n",
        "log_reg.fit(x, y)\n",
        "\n",
        "coefficients = log_reg.coef_[0]\n",
        "feat_importances_logreg = pd.Series(coefficients, index=x.columns)\n",
        "top_feat_importances_logreg = feat_importances_logreg.abs().nlargest(10)\n",
        "\n",
        "plt.figure(figsize=(6, 6))\n",
        "top_feat_importances_logreg.sort_values().plot(kind='barh', color='salmon')\n",
        "plt.xlabel('Coefficient Absolute Value')\n",
        "plt.title('Top 10 Features with the Highest Contributing to Attrition (Logistic Regression)')\n",
        "plt.show()\n",
        "\n",
        "common_features = set(top_feat_importances_rf.index).intersection(top_feat_importances_logreg.index)\n",
        "print(\"Common Features in Attrition Prediction:\")\n",
        "print(common_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "E1M5enb-pXWE",
        "outputId": "236f3e48-cb3f-40be-ca7c-7d36a39f82b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Common Features in Attrition Prediction:\n",
            "{'OverTime'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_reg = LogisticRegression(max_iter=1000, random_state=42)\n",
        "log_reg.fit(x, y)\n",
        "\n",
        "coefficients = log_reg.coef_[0]\n",
        "feat_importances_logreg = pd.Series(coefficients, index=x.columns)\n",
        "top_feat_importances_logreg = feat_importances_logreg.abs().nlargest(10)\n",
        "\n",
        "print(\"Top 10 Features with the Highest Contribution to Attrition (Logistic Regression):\")\n",
        "print(top_feat_importances_logreg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tps2K88RGXUJ",
        "outputId": "101b2b84-81c8-492b-9921-050cb45550ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 10 Features with the Highest Contribution to Attrition (Logistic Regression):\n",
            "OverTime                   0.212051\n",
            "YearsSinceLastPromotion    0.194392\n",
            "EnvironmentSatisfaction    0.187306\n",
            "JobSatisfaction            0.185697\n",
            "StockOptionLevel           0.175627\n",
            "MaritalStatus              0.169572\n",
            "JobInvolvement             0.155222\n",
            "YearsInCurrentRole         0.136985\n",
            "NumCompaniesWorked         0.130557\n",
            "YearsWithCurrManager       0.123762\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    }
  ]
}