To build a market demand forecasting platform that analyzes historical sales data to predict future demand trends. The system will help businesses optimize inventory management, reduce stockouts, and improve supply chain decision-making through data-driven forecasting techniques.
Collect historical sales data from retail datasets.
Clean and normalize time-series data.
Perform exploratory analysis to identify seasonal trends.
Implement time-series forecasting models (ARIMA, Prophet).
Evaluate forecast accuracy using RMSE and MAE.
Visualize predicted vs actual sales trends.
Analyze impact of promotions and festivals on demand.
Build dashboard showing monthly and quarterly forecasts.
Design alerts for predicted demand spikes.
Study correlation between external factors and sales.
Suggest inventory optimization strategies.
Prepare final documentation with graphical insights and conclusions.
