Grid Search Optimization (GSO) Based Future Sales Prediction for Big Mart

Abstract

In retailer domain predicting sales before actual sales plays a vital role for any retailer company like Big Mart or Mall for maintaining a successful business. Traditional forecasting models such as statistical model is commonly used as methodology for future sales prediction, but these techniques takes much more time to estimate the sales, also they are not capable to handle the non-linear data. Therefore, Machine Learning(ML) techniques are employed to handle both non-linear and linear data. ML techniques can also efficiently large volume of data like Big Mart dataset, containing large number of customer data and individual data item's attribute. A retailer company wants a model that can predict accurate sales so that it can keep track of customers future demand and update in advance the sale inventory. In this work, we propose a Grid Search Optimization (GSO) technique to optimize the parameters and select the best tuning hyper parameters, further ensemble with Xgboost techniques for forecasting the future sales of a retailer company such as Big Mart and we found our model produces the better result.

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