• T.Naga Navya
• Bandarupalli Sruthi
• Devarala Sukanya
• karumudi Himavarsha
• Tiriveedhi Srilakshmi Navya Sri

Sales forecasting plays a critical role in various business applications such as retail industry. The enormous growth of digital data has fastened the technological advancements. Sales forecasting using machine learning is a technique where models are trained with historical data to make future predictions. we have various sales forecasting techniques using predictive analytics and machine learning algorithms. Among the various methodologies , in this paper we chose to use Random Forest Regressor for predicting future sales. Random Forest Regressor was chosen for this analysis because. Results show that this algorithm can accurately predict sales with high accuracy . Because Random Forest performs better than other models and is versatile enough to handle huge datasets, it was chosen for this investigation. Our study's findings demonstrate that the Random Forest Regressor has a high degree of accuracy when predicting future sales. This capability enables organizations to make more informed decisions, manage inventory levels, and improve overall operational efficiency by giving them access to accurate forecasts.

Sales Forecasting, Predictive Analytics, Machine Learning, Retail -industry ,Random Forest algorithm, Future sales, Ensemble learning

"Sales Forecasting Using Predictive Analytics and Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.541-547, November-2024, Available :http://www.jetir.org/papers/JETIRGO06058.pdf

"Sales Forecasting Using Predictive Analytics and Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp541-547, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06058.pdf