PREDICTING THE FUTURE OF SALES: A MACHINE LEARNING ANALYSIS OF ROSSMAN STORE SALES

Predictive sales analysis based on previous data is crucial for organizations to make educated decisions and remain competitive. Machine learning is a powerful technology that can automate this process, producing more accurate and informed forecasts. Machine learning has revolutionized many sectors, including sales and marketing. Machine learning algorithms can forecast consumer behaviour and sales trends by analyzing data and discovering patterns, hidden patterns, and linkages. The purpose of this research is to propose an understanding of the usage of machine learning algorithms for predicting future sales of Big Mart enterprises based on past year sales. Utilizing machine learning methods like Linear Regression and Gradient Boost, a thorough study of sales forecasting is undertaken. The performance of the Linear Regression and Gradient Boosting methods was evaluated using metrics such as mean absolute error, mean squared error, R2 score, and Accuracy. The studys findings can help businesses make better informed decisions about resource allocation, production planning, and marketing methods. Overall, machine learning is a powerful tool for forecasting sales and can assist organizations in staying ahead of the curve in a fast changing industry.