Machine Learning Based Smart Traffic Congestion Prediction System

The objective of this project is to build a smart traffic prediction system using machine learning that analyzes historical traffic data to forecast congestion levels, enabling authorities to manage traffic flow and reduce delays.

Collect traffic data such as vehicle count, time, and location Clean and preprocess large traffic datasets Perform exploratory data analysis to identify congestion patterns Apply regression and classification algorithms Train models to predict congestion intensity Evaluate prediction accuracy using suitable metrics Develop a web interface displaying traffic predictions Integrate data visualization tools Implement time-based forecasting logic Test system reliability and performance Prepare technical documentation and future scope