IMPLEMENTATION OF THE LINEAR REGRESSION METHOD TO PREDICT SALES RESULTS IN LOOKING FOR STUFF STORE

Abstract

This study presents the successful implementation of the linear regression method in an online store built using the CodeIgniter framework. The objective was to address the challenge of predicting sales results for the next 1 to 3 months and automating the recording of sales data without manual intervention. The study focused on facilitating the store owner in accurately predicting sales within the specified timeframe and streamlining the sales recording process. The implementation involved leveraging the capabilities of CodeIgniter to integrate the linear regression algorithm seamlessly into the store's data analytics system. Historical sales data, along with additional factors such as time, weather conditions, and promotional activities, were analyzed to develop a robust predictive model. The linear regression method yielded accurate predictions, providing valuable insights into sales trends, popular products, and optimal marketing strategies. The integration of CodeIgniter's features facilitated efficient data processing

and management, enabling the store owner to make informed decisions based on real- time sales predictions. This automated approach significantly enhanced operational

efficiency, reduced manual errors, and improved decision-making processes. Moreover, the user-friendly interface of CodeIgniter contributed to an enhanced customer experience by ensuring seamless navigation and accessibility of sales-related information. Overall, this study demonstrates the effectiveness of integrating the linear regression method using CodeIgniter, leading to improved sales prediction accuracy, streamlined operations, and enhanced customer satisfaction in the online retail environment.