Smart City Traffic Management: Leveraging data science and machine learning to forecast traffic patterns, optimize signals, and plan infrastructure for intelligent and efficient urban transportation.
This project is aimed at predicting future website traffic using time series forecasting techniques. The project utilizes two popular models, Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM), to make accurate predictions based on historical website traffic data. The main goal is to assist website owners, administrators, and stakeholders in making informed decisions about resource allocation, server scaling, and content management.
- Time series data preprocessing and exploration
- Implementation of ARIMA and LSTM forecasting models
- Hyperparameter tuning for model optimization
- Performance evaluation using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE)
- Comparative analysis of ARIMA and LSTM models
- Clear and detailed documentation