Authors - Lakshya Khanna, Shriniwas Mahajan, Varun Kadu, Sudhanshu Maurya, Firdous Sadaf M. Ismail, Rachit Garg Abstract - Sales forecasting assumes a significant part in essential navigation and asset allotment for organizations across different businesses. Realizing the patterns can change and help in the plan of market procedure, particularly now, when the Electronic Vehicles (EV) market is at its pinnacle. In this paper, we investigate the utilization of measurable models and some high-level AI procedures, specifically Random Forest and Long Short-Term Memory (LSTM) models, for anticipating sales information patterns. The review plans to assess the exhibition and dependability of these models in estimating sales data, utilizing genuine world datasets spreading over several years. Execution assessment of the models is led utilizing measurements like Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared. Also, stability analysis is performed to evaluate the unwavering quality of each model in catching and foreseeing exact patterns. The discoveries of the exploration feature the viability of the measurable models and ML models in anticipating sales data patterns. The two kinds of models show promising execution, with the LSTM model areas of strength for displaying in catching transient conditions and long-haul designs in the sales data. In any case, contrasts in execution and strength between the models are noticed, giving important experiences to choosing the most appropriate determining approach in view of explicit business prerequisites.
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