PREDICTION OF SALES BASED ON AN EFFECTIVE ADVERTISING MEDIA SALE DATA: A PYTHON IMPLEMENTATION APPROACH

Sales forecasting is an essential task in the retail management field. Intelligent forecasting using machine learning techniques can help discover the selection of feature variables that influence prediction of sales growth. A Python program implementation is adopted to compute; develop and visualizing forecasting model of historical sales data based on advertising media opted to do the effective sales promotion. Python supports working on predictive algorithms through accessing from Python libraries. For this purpose, it relies on the past observations based transaction data set file as an input to produce output without worrying about the underlying mechanism. The results indicated that TV media is the feature variable that influences the prediction of sales of linear regression model. Regression results of OLS model type are displayed with coefficient values to substitute in the linear regression equation. Seaborne library of Python is used to generate the visualization of charts and graphs.