An interactive machine learning web application that predicts passenger survival on the RMS Titanic using advanced feature engineering and Random Forest classification.
This project combines historical data analysis with modern machine learning to predict survival outcomes on the Titanic. Built with Streamlit, it provides an intuitive interface for exploring the tragic events of April 15, 1912, through the lens of data science.
- 🎯 Real-time Predictions: Input passenger details and get instant survival predictions
- 📊 Interactive Visualizations: Explore data with dynamic Plotly charts
- 🔍 Feature Analysis: Deep dive into factors affecting survival rates
- 📚 Historical Context: Learn about the Titanic disaster with educational content
- 🎨 Modern UI: Clean, responsive interface with custom styling
Try the App - Experience the predictor in action!
| Metric | Score |
|---|---|
| Accuracy | 85.4% |
| Precision | 83.2% |
| Recall | 87.6% |
| F1-Score | 85.3% |
- Frontend: Streamlit
- Machine Learning: scikit-learn (Random Forest)
- Data Processing: pandas, numpy
- Visualizations: Plotly Express
- Deployment: Streamlit Community Cloud