Store Sales Prediction Using Machine Learning and AI

Computer Engineering

Sales forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting using various methods like machine learning techniques, time series analysis, deep learning models, artificial neural networks, etc. In this work, an intelligent demand forecasting system is developed. This improved model is based on analyzing and interpreting the historical data by using different forecasting methods, including time series analysis techniques, support vector regression algorithms, and deep learning models. The organization sells gifts primarily on the online platform. The customers who make a purchase consume directly for themselves. Small businesses buy in bulk and sell to other customers through the retail outlet channel. The data contains information about more than 1000 stores in the country and there are more than 1000000 entries in the data. We are required to predict the sales for the Store-Day level for one month

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