Titanic ML Project

Overview

The Titanic ML Project involved exploring, cleaning, and modeling the famous Titanic dataset to predict passenger survival.
The project demonstrates practical machine learning techniques using Python and popular data science libraries, emphasizing predictive modeling, data preprocessing, and evaluation metrics.

• Data Handling: Used Pandas and NumPy for data cleaning and preprocessing.
• Visualization: Applied Matplotlib and Seaborn to understand data patterns.
• Machine Learning: Built models with scikit-learn, including Logistic Regression, KNN, Decision Trees, and Random Forest.
• Evaluation: Measured performance using accuracy and F1-score metrics.

Achievements / Highlights

• Explored and cleaned real-world dataset for predictive analysis.
• Implemented multiple machine learning models to compare performance.
• Developed data visualizations to interpret feature importance and patterns.
• Evaluated models with reliable metrics and identified the best-performing algorithm.
• Structured project for reproducibility and learning, including modular code and documentation.