Analyzing and Predicting the Sales Forecasting using Modified Random Forest and Decision Tree Algorithm

Every element of life is evolving as a consequence of machine learning, which has also had a substantial effect on real-world circumstances. There are transformational uses for machine learning in every area, including education, healthcare, engineering, commerce, entertainment, and transportation, to mention a few. The normal method of establishing sales and marketing objectives without considering the purchasing patterns of customers does not assist organization’s in keeping up with the speed of the competitive market. Significant developments have occurred in the industry of sales and marketing due to advances in machine learning. Due to these advancements, it is now feasible to predict a variety of crucial aspects, including the demographics of the target market and sales estimates for the next years. This makes it easy for the sales team to develop company-expansion plans. This goal of the proposed system is to provide a formula for estimating Big Mart Companies' future sales based on their past sales. Machine learning models including XG BOOST, Decision Tree Regression, Lasso Regression, Ridge Regression, and Linear Regression. The prediction takes into account data elements like age group, gender, job status, product category, etc.