Traffic Prediction Using Machine Learning Overview This project aims to develop a robust traffic prediction model using machine learning techniques. By leveraging historical traffic data and real-time inputs, the model forecasts traffic conditions, helping to manage vehicle movement, reduce congestion, and optimize routes.
Features Data Collection: Aggregates traffic data from various sources, including sensors, GPS, and historical records. Preprocessing: Cleans and preprocesses the data to handle missing values, outliers, and noise. Modeling: Implements machine learning algorithms such as Random Forest, Support Vector Regression, and Neural Networks to predict traffic flow and congestion. Evaluation: Uses metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to evaluate model performance. Visualization: Provides visual insights through graphs and charts to understand traffic patterns and predictions.
Installation Clone the repository: git clone https://github.com/ayush/TrafficPrediction_Proj.git
Navigate to the project directory: cd traffic-prediction
Install the required dependencies: pip install -r requirements.txt
Usage Prepare your dataset and place it in the data directory. Run the preprocessing script: python preprocess.py
Train the model: python train.py
Make predictions: python predict.py
Contributing Contributions are welcome! Please fork the repository and submit a pull request.
License This project is licensed under the MIT License.
Feel free to customize this description to better fit your project’s specifics and goals. Good luck with your traffic prediction project! 🚗📈