This project predicts traffic density in urban areas using various features such as city, vehicle type, weather conditions, and more. It utilizes machine learning to provide accurate predictions, helping city planners and traffic management authorities make informed decisions.
- Python: Programming language used for data processing and machine learning.
- Pandas: For data manipulation and analysis.
- Scikit-learn: Machine learning library used for model training and evaluation.
- Flask: Web framework used to create a user-friendly interface for predictions.
- HTML/CSS: For designing the web interface.
- Pickle: For saving and loading the trained machine learning model.
- Model Optimization: Improve the model's accuracy by exploring different algorithms and hyperparameter tuning.
- Real-Time Data Integration: Incorporate real-time traffic data for more dynamic and responsive predictions.
- User Authentication: Add user authentication to enhance security and provide personalized experiences.
- API Development: Create an API for third-party integration, allowing other applications to utilize traffic predictions.
- Visualization: Enhance the web interface with data visualizations to better represent traffic trends and insights.