Titanic Classification with Logistic Regression (Accuracy, Precision, Recall, F1)

Introduction

For Day 2, I switched to classification with the Titanic dataset.

This dataset is the “Hello World” of ML classification: predicting survival based on passenger features.

Why It Matters

Binary classification problems are everywhere: fraud vs not fraud, spam vs not spam, churn vs no churn. Titanic survival is just a teaching ground.

Approach

• Dataset: Titanic (Seaborn)
• Features: sex, age, fare, class, embarked
• Model: Logistic Regression
• Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
• Visualization: Confusion Matrix

Results

The model correctly picked up obvious signals like sex (women had higher survival) and class (first class had better survival).

Takeaways

• Accuracy isn’t the only metric: precision and recall tell a deeper story.
• Logistic Regression is simple but powerful for binary problems.
• Visualizations like confusion matrices make results tangible.

Artifacts

• Notebook on GitHub

Video walkthrough