The Traffic Predictor is an AI-powered system designed to forecast traffic flow using LSTM (Long Short-Term Memory) networks and real-time data. This project aims to address urban traffic congestion by analyzing historical traffic patterns, weather conditions, and external factors to provide accurate traffic predictions.

Key Features:

Uses LSTM neural networks to predict future traffic density based on past trends.

Provides forecasts for specific roads, intersections, and time intervals.

Integrates weather data (rain, temperature, humidity, etc.) to improve prediction accuracy.

Considers external influences like holidays, events, and road closures.

Fetches data from real-time APIs, traffic cameras, and sensors.

Cleans and normalizes data using Pandas, NumPy, and Scikit-learn for preprocessing.

Developed a Gradio interface for easy user interaction with the ML model.

Provides visualizations for traffic trends and congestion hotspots.

Allows users to input historical traffic and weather data to get real-time predictions.

Implements LSTM (Long Short-Term Memory) neural networks for time-series forecasting.

Trains models using historical data to recognize patterns in traffic congestion.