Customer Sale Analysis and Classification Using Machine Learning Algorithm

Large-scale sale Prediction involves analyzing the sales based on client interests and needs. Analyzing the product’s demand based on sales. This has further advantages for marketing and business, helping them to increase sales and productivity. To execute and forecast the sales, machine learning techniques were needed. Business intelligence is crucial for sales and product development. The forecasting of big-box store sales considers several variables, including historical sales data, consumer demographics, market trends, seasonal effects, promotional activities, and macroeconomic situations. Machine learning algorithms that predict sales have a significant impact on accuracy. The many elements can teach the models new things. Other methods for forecasting and analyzing sales include targeted marketing and consumer segmentation. To forecast and evaluate sales, linear regression models, decision trees, random forests, etc. will be helpful. Big Mart sales forecasting is necessary for business decision-making and market-condition alteration. The system being developed in this study is one that can forecast with accuracy. A few measures, including mean squared error, mean absolute error, and R squared, can be used to assess the performance of the model. After training and evaluation, the model may be used to forecast sales of new goods or shops. This model provided an accuracy of above 90%.