Titanic Survival Prediction Model

I’m providing the classic Titanic passenger data in a single, well-structured CSV / Excel file and I want a solid machine-learning pipeline that predicts who survived the voyage. I’m not entirely sure how “clean” the file is—some columns might have missing values or inconsistencies—so your first step will be a quick exploratory pass and any essential preprocessing (handling nulls, encoding categoricals, feature scaling, sensible train-test split).

Once the data is in good shape, build and tune at least one supervised model (feel free to compare options such as logistic regression, random forest, gradient boosting, or XGBoost) and report the performance with clear metrics—accuracy plus precision/recall or AUC would be ideal.

Deliverables
• Jupyter notebook or Python script that walks through preprocessing, modeling, evaluation, and final prediction generation
• Clean, well-commented code that I can rerun on my machine (Pandas, scikit-learn or similar standard libraries)
• A short read-me explaining setup, decisions made, and how I can use the model to score new passenger records

I’m happy to discuss feature engineering ideas and iterate on anything that improves real-world predictive power. Looking forward to seeing how you tackle this classic challenge!