Demand Forecasting
The purpose of this project is to develop a preliminary machine learning model for demand forecasting using historical sales data. It includes data loading, basic preprocessing, exploratory data analysis, and a Random Forest model to predict units sold. However, the approach has limitations, particularly in handling temporal aspects which are crucial for accurate time series forecasting.
ML Model for Demand Forecasting
About this Dataset
One of the largest retail chains in the world wants to use its vast data source to build an efficient forecasting model to predict the sales for each SKU in its portfolio at its 76 different stores using historical sales data for the past 3 years on a week-on-week basis. Sales and promotional information is also available each week, both product and store-wise.
However, no other information regarding stores and products is available. Can we still accurately forecast the sales values for every such product/SKU-store combination for the next 12 weeks?