Sales prediction involves using historical sales data and other relevant features to forecast future sales. Accurate predictions help businesses with inventory management, marketing strategies, and resource allocation. This project leverages machine learning techniques to develop sales prediction models and provides a foundation for understanding and implementing such solutions.
- Data Preprocessing: Data cleaning, feature engineering, and handling missing values.
- Machine Learning Models: Employing various regression algorithms (e.g., Linear Regression, Decision Trees, Random Forest) for sales prediction.
- Model Evaluation: Assessing model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) score.
- Time Series Analysis: Exploring time series forecasting techniques for sales prediction in temporal data.
- Visualization: Visualizing historical sales trends and model predictions.
- Documentation: Providing detailed documentation, code comments, and usage examples.