Logistic_Regression_Model.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
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      "source": [
        "data = pd.read_csv('/content/adult.csv')\n",
        "data.head()"
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              "   Age          Workclass  fnlgwt   Education  Education Num  \\\n",
              "0   39          State-gov   77516   Bachelors             13   \n",
              "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
              "2   38            Private  215646     HS-grad              9   \n",
              "3   53            Private  234721        11th              7   \n",
              "4   28            Private  338409   Bachelors             13   \n",
              "\n",
              "        Marital Status          Occupation    Relationship    Race      Sex  \\\n",
              "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
              "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
              "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
              "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
              "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
              "\n",
              "   Capital Gain  Capital Loss  Hours/Week         Country Above/Below 50k  \n",
              "0          2174             0          40   United-States           <=50K  \n",
              "1             0             0          13   United-States           <=50K  \n",
              "2             0             0          40   United-States           <=50K  \n",
              "3             0             0          40   United-States           <=50K  \n",
              "4             0             0          40            Cuba           <=50K  "
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              "      <td>338409</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 2
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.combine import SMOTEENN\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import  classification_report, ConfusionMatrixDisplay"
      ],
      "metadata": {
        "id": "5OPhCQm0VivD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['interaction_Education Num_Hours/Week'] = data['Education Num'] * data['Hours/Week']\n",
        "data['interaction_Education Num_Age'] = data['Education Num'] * data['Age']"
      ],
      "metadata": {
        "id": "idHNhXfHWT9K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''features = ['interaction_Education Num_Hours/Week','interaction_Education Num_Age', 'Capital Gain']'''\n",
        "features = ['interaction_Education Num_Hours/Week', 'interaction_Education Num_Age', 'Capital Gain', 'fnlgwt']\n",
        "x = data.drop('Above/Below 50k', axis=1)\n",
        "y = data['Above/Below 50k']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle = True)\n",
        "\n",
        "print(f\"Data size: {len(x)}\\nTraining data size: {len(x_train)}\\nTesting data size: {len(x_test)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2L9XQbKVtpU",
        "outputId": "93f4795b-ea3a-4e11-ff63-96bf8e549ada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data size: 32561\n",
            "Training data size: 26048\n",
            "Testing data size: 6513\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# preprocessing\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Select only numerical features for scaling\n",
        "numerical_features = x_train.select_dtypes(include=['number']).columns\n",
        "\n",
        "# Fit and transform on numerical features only\n",
        "x_train_transformed = scaler.fit_transform(x_train[numerical_features])\n",
        "x_test_transformed = scaler.transform(x_test[numerical_features])"
      ],
      "metadata": {
        "id": "tZq8x0fLV3CT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression(multi_class='multinomial')\n",
        "lr.fit(x_train_transformed, y_train)\n",
        "y_hat_lr = lr.predict(x_test_transformed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "krxRamgUVtmz",
        "outputId": "6aff7e2e-c295-4875-aed1-52f772240833"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1237: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, binary problems will be fit as proper binary  logistic regression models (as if multi_class='ovr' were set). Leave it to its default value to avoid this warning.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "acc = accuracy_score(y_test, y_hat_lr)\n",
        "print(f\"Model accuracy was {acc*100:.2f}%\")\n",
        "\n",
        "report = classification_report(y_test, y_hat_lr)\n",
        "print(report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUWMsFSkV7mq",
        "outputId": "ee845807-13ff-4996-f222-7ab1b0a67b7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy was 81.36%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       <=50K       0.83      0.95      0.89      4925\n",
            "        >50K       0.72      0.38      0.50      1588\n",
            "\n",
            "    accuracy                           0.81      6513\n",
            "   macro avg       0.78      0.67      0.69      6513\n",
            "weighted avg       0.80      0.81      0.79      6513\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_predictions(y_test, y_hat_lr, cmap=plt.cm.Blues)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "zIhsziXQV7jz",
        "outputId": "344fe5c5-566b-4e9d-89db-fa7406474429"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}