Machine Learning

NYC Taxi Trip Duration Prediction Using Machine Learning

The NYC Taxi Trip Duration Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts taxi trip duration in New York City using the TLC dataset. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Decision Tree and Ridge Regression with feature engineering, achieving 0.562 R² score and 328.5 seconds RMSE.

The system utilizes the NYC TLC Yellow Taxi dataset containing over 1.4 million trip records with features including pickup/dropoff locations, timestamps, passenger counts, and vendor information. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time trip duration prediction, enabling ride-hailing services and fleet operators to optimize operations and improve passenger experience.

• Multi-Model Trip Duration Prediction
• Decision Tree (R²: 0.562)
• Ridge Regression (R²: 0.484)
• Haversine Distance Calculation
• Temporal Feature Engineering
• Interactive Visualizations
• Real-time Duration Predictions
• Model Performance Comparison
• Feature Importance Analysis
• Flask Web Application