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AN EFFECTIVE APPROACH FOR SALES FORECASTING
Abstract: Sales Forecasting plays an important role in business. It provides appropriate and dependable information about the past and present events and likely future events. Nowadays, predicting sales has become a common method but not many known approaches are applied. Sales forecasting works as a vital data input to organizational development. Machine Learning techniques are very effective tools in extracting hidden knowledge from vast dataset to boost the precision and effectiveness of estimating sales. Supervised Machine Learning is being used by many organizations to identify and solve business problems. The proposed methodology is to use regression to do a comparative study on sales forecasting. Here, three regression techniques, which are Linear Regression, Ridge Regression, and LASSO Regression are discussed in detail. The accuracy in sales prediction offers a big positive impact on business. All the regression techniques are giving excellent outcome for R-squared and RMSE value. The perfect result would be an RMSE value of zero and R-squared value of 1, but that’s almost impossible in real economic datasets.
Keywords: Regression, Lasso, Ridge, Linear, Sales Forecasting