Sales Prediction Using Machine Learning

Predicting the sales or forecasting of the sales is very crucial in the food sector, and it has lately got immense popularity to enhance operations of the market.The industry has traditionally concentrated on a conventional statistical model but in the recent times, Machine Learning techniques have received more attention.This project will help to identify the critical features that can influence the sales and also a trial-and-error process is executed to find the best suitable algorithm for sales forecasting.Machine Learning Algorithms like Simple Linear Regression, Random Forest Regression, Gradient Boosting, XG Boost regression and were considered in this thesis, which they expected to perform well on the issues.Algorithms similar as Simple Linear Regression, Gradient Boosting Regression, XG Boost regression and Random Forest Regression are commonly known for performing better than others, this has been clearly shown that Random Forest Regression is the most applicable algorithm compared to the others.