The objective of this project is to develop an advanced demand forecasting system using predictive analytics and machine learning techniques. The system aims to analyze historical demand data, identify influencing factors, and generate accurate forecasts to support strategic planning and inventory optimization.
Collect large-scale historical demand and sales datasets from retail or supply-chain domains.
Perform advanced data preprocessing, including missing value imputation and outlier detection.
Conduct exploratory data analysis to identify demand trends, seasonality, and cyclical patterns.
Apply feature engineering to include external factors such as pricing, promotions, and holidays.
Implement multiple predictive models including linear regression, random forest, and gradient boosting.
Train and validate models using cross-validation techniques.
Evaluate model performance using MAE, RMSE, and MAPE metrics.
Compare model outputs to identify the most accurate forecasting approach.
Visualize demand forecasts and confidence intervals.
Interpret forecasting results for business decision-making.
Document system architecture, model assumptions, and limitations in detail.
