Logistic Regression Binary Classifier

Objective

This project demonstrates how to build a binary classifier using Logistic Regression in Python. The model predicts outcomes for a binary classification problem using features from a dataset.

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Tools & Libraries

• Python
• Scikit-learn
• Pandas
• Matplotlib

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Dataset

You can use any binary classification dataset for this task.
Example: Breast Cancer Wisconsin Dataset.

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Project Steps / Mini Guide

1. Choose a binary classification dataset.
2. Train/test split the dataset and standardize features.
3. Fit a Logistic Regression model to the training data.
4. Evaluate the model using:
   • Confusion matrix
   • Precision
   • Recall
   • ROC-AUC
5. Tune the classification threshold and explain the sigmoid function.

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Key Concepts You’ll Learn

• Binary classification
• Evaluation metrics (precision, recall, ROC-AUC)
• Sigmoid function and threshold tuning

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Interview Questions

1. How does logistic regression differ from linear regression?
2. What is the sigmoid function?
3. What is precision vs recall?
4. What is the ROC-AUC curve?
5. What is the confusion matrix?
6. What happens if classes are imbalanced?
7. How do you choose the threshold?
8. Can logistic regression be used for multi-class problems?

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How to Run

1. Clone the repository:

   git clone <repository-url>