A Web Application for Customer Segment based Black Friday Sales Forecast

The study shows how factors internal to the sales forecasting process can introduce bias. Black Friday is upon us, and retailers everywhere stock up on inventory at dramatic discounts. The products aren't accessible to some clients due to poor arrangements, despite good sales. Managing a small staff and keeping potential customers in mind is more of a concern for shopkeepers. Despite some attempts at effective inventory management and sales forecast, the results have not been favorable. A prediction model has proven effective in managing this issue. By analyzing the previous spending patterns of clients, a precise and efficient algorithm can predict future spending. A variety of data analysis strategies, including regressors and classifiers, are employed to evaluate specificity and accuracy. Then, different algorithms are used to implement machine learning techniques. An extensive report of sales for Black Friday 2019 has been compiled. To calculate the volume, we took into account various factors, including age group, occupation, product category, city, etc. A store that wishes to predict sales could do so from this information. We have deployed our Flask-based user interface after determining which model is the best fit.