-4d90550d17f4602e089-17b7 20220523051220361 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 05 30 2022 11 6 Assistant Professor, Department of Computer Engineering, Sanjivani COE, Kopargaon Savitribai Phule Pune University, Pune (Maharashtra), India. Dr. Gunjal Sanjay Nana Dr. D.B Kshirsagar HOD and Professor, Department of Computer Engineering, Sanjivani COE, Kopargaon, Savitribai Phule Pune University, Pune (Maharashtra), India. Dr. B.J Dange Associate Professor, Department of Computer Engineering, Sanjivani COE, Kopargaon, Savitribai Phule Pune University, Pune (Maharashtra), India. Dr. H.E Khodke Assistant Professor, Department of Computer Engineering Dept, Sanjivani COE, Kopargaon, Savitribai Phule Pune University, Pune (Maharashtra), India. Dr. C.S Kulkarni HOD and Associate Professor, Department of Computer Engineering, VPKBIET, Baramati, Savitribai Phule Pune University, Pune (Maharashtra), India. The amounts of data predicted to increase at an exponential rate in the future. The modifications are essential to meet transaction speeds as well as the anticipated growth in data and customer behaviors. The information derived from prior data is extensively relied upon by the majority of companies. One of the primary goals of the suggested system is to identify a reliable sales trend prediction mechanism that is executed using machine learning techniques in order to maximize income. Sales forecasting advises managers about how to manage a company's employees, working capital and assets. It's a requirement for strategic planning and decision-making in the corporate world. Reasonable forecasts enable the company to increase market growth while increasing revenue generating. Operations, marketing, sales, production, and finance all use sales predictions as inputs in their decision-making processes. The concept of sales data and sales forecast has been examined in the suggested system. Machine learning algorithms such as GLL (Generalized Linear Model), GBT (Gradient Boosted Trees), and Decision Trees were used to develop the model, and the optimum model for prediction was established based on the results analysis. A best-fit prediction model for anticipating sales trends is offered based on a performance review. The effectiveness and accuracy of the prediction and forecasting approaches used are discussed in the findings. The Gradient Boost Algorithm has been demonstrated to be the best fit model for forecasting and predicting future sales. The sales projection is done using Gradient Boosted Trees, which predicts which product will be sold in what quantity in the future. 05 30 2022 69 75 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F9916.0511622 https://www.ijitee.org/portfolio-item/f99160511622/