Overview: This project dives deep into the world of machine learning through the lens of the Titanic dataset, offering a step-by-step guide to predict passenger survival.

Functionality: Features an end-to-end machine learning workflow including data preprocessing, feature engineering, model selection, and hyperparameter tuning. Employs ensemble techniques and advanced optimization to refine prediction accuracy.

Technical Highlights: Utilizes Python, Pandas, and Scikit-learn to dissect and manipulate the dataset, ensuring a thorough understanding and application of machine learning concepts. Integration of visualization tools and ensemble methods enhances model performance and insight extraction.

Key Features:

• Detailed dataset analysis and visualization to uncover patterns and relationships.
• Strategic data cleaning and preprocessing for optimal model input.
• Application of machine learning pipelines for streamlined model evaluation and tuning.
• Advanced model optimization through RandomSearchCV, GridSearchCV and BayesSearchCV complemented by ensemble techniques for superior accuracy.
• Comprehensive evaluation metrics for robust model assessment.

More Details: This project not only aims to predict survival on the Titanic but also serves as a blueprint for tackling similar machine learning challenges.

1. Click here for the full manual: link to notebook