LogisticRegression.ipynb

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      "source": [
        "import pandas as pd\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "import requests"
      ]
    },
    {
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      "source": [
        "data_file_url=\"https://docs.google.com/spreadsheets/d/e/2PACX-1vRxMOJNj9deCdSLIZ9BeOjXtclaOwhNT38Kfgzl6m7UF0jF2ONv_l0bLbSPDFTIknt7ZZ4xxrUELpG-/pub?output=csv\" #dataset url - published on the web for the assignment after preprocessing\n",
        "\n",
        "response = requests.get(data_file_url) #getting the data from the url\n",
        "with open('data.csv', 'wb') as file:\n",
        "    file.write(response.content) #writing the file content into a file called data.csv\n",
        "\n",
        "df = pd.read_csv(\"data.csv\")\n",
        "\n",
        "# Use a list to specify the column names\n",
        "X = df[[\n",
        "    \"Marital status\", \"Application mode\", \"Application order\", \"Course\", \"Attendance\",\n",
        "    \"Previous qualification\", \"Previous qualification (grade)\", \"Nationality\", \"Mother's qualification\",\n",
        "    \"Father's qualification\", \"Mother's occupation\", \"Father's occupation\", \"Admission grade\",\n",
        "    \"Displaced\", \"Educational special needs\", \"Debtor\", \"Tuition fees up to date\", \"Gender\",\n",
        "    \"Scholarship holder\", \"Age at enrollment\", \"International\", \"Curricular units 1st sem (credited)\",\n",
        "    \"Curricular units 1st sem (enrolled)\", \"Curricular units 1st sem (evaluations)\",\n",
        "    \"Curricular units 1st sem (approved)\", \"Curricular units 1st sem (grade)\",\n",
        "    \"Curricular units 1st sem (without evaluations)\", \"Curricular units 2nd sem (credited)\",\n",
        "    \"Curricular units 2nd sem (enrolled)\", \"Curricular units 2nd sem (evaluations)\",\n",
        "    \"Curricular units 2nd sem (approved)\", \"Curricular units 2nd sem (grade)\",\n",
        "    \"Curricular units 2nd sem (without evaluations)\", \"Unemployment rate\", \"Inflation rate\", \"GDP\"\n",
        "]]\n",
        "y = df['Target']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
      ],
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      "source": [
        "# Create a logistic regression model\n",
        "lr_model = LogisticRegression(max_iter=10000, multi_class='multinomial', solver='lbfgs', random_state=0)"
      ],
      "metadata": {
        "id": "QT1R0J4O68F8"
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      "source": [
        "# Fit the model to the training data\n",
        "lr_model.fit(X_train, y_train)"
      ],
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              "LogisticRegression(max_iter=10000, multi_class='multinomial', random_state=0)"
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      "source": [
        "# Make predictions on test data\n",
        "y_pred = lr_model.predict(X_test)\n",
        "\n",
        "# Evaluate model\n",
        "classification_rep = classification_report(y_test, y_pred)\n",
        "print(\"Classification Report:\\n\", classification_rep)"
      ],
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     Dropout       0.81      0.76      0.78       428\n",
            "    Enrolled       0.55      0.34      0.42       233\n",
            "    Graduate       0.81      0.95      0.87       667\n",
            "\n",
            "    accuracy                           0.78      1328\n",
            "   macro avg       0.72      0.68      0.69      1328\n",
            "weighted avg       0.76      0.78      0.76      1328\n",
            "\n"
          ]
        }
      ]
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