Welcome to the Titanic Survival Prediction project! This repository explores the challenge of predicting passenger survival on the Titanic using machine learning techniques. Leveraging the historical Titanic dataset, this project aims to build and evaluate models that can accurately predict whether a passenger survived the tragic sinking.
Project Overview The Titanic dataset provides various features such as age, sex, passenger class, fare, and more, which are used to predict survival outcomes. This project encompasses data preprocessing, exploratory data analysis, feature engineering, and the application of several machine learning algorithms.
Key Features Data Exploration: Detailed analysis of the dataset, including visualization of key features and insights into survival patterns. Feature Engineering: Techniques for transforming raw data into meaningful features to improve model performance. Machine Learning Models: Implementation and evaluation of multiple classification algorithms, including Logistic Regression, Random Forest, and Gradient Boosting. Model Evaluation: Performance metrics such as accuracy, precision, recall, and F1 score to assess model effectiveness. Technologies Used Programming Language: Python Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn Tools: Jupyter Notebooks for code and analysis