Task 4 - Classification with Logistic Regression

Objective

Build a binary classifier using Logistic Regression.

Dataset

Breast Cancer Wisconsin Dataset from Scikit-Learn.

Steps Performed

1. Loaded dataset
2. Performed train-test split
3. Standardized features
4. Trained Logistic Regression model
5. Evaluated using:
   • Confusion Matrix
   • Precision
   • Recall
   • ROC-AUC Score
6. Tuned classification threshold
7. Visualized Sigmoid Function

Tools Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-Learn

Results

The model achieved high classification performance on the Breast Cancer dataset and demonstrated the effectiveness of Logistic Regression for binary classification tasks.