Abstract:
"Sales forecasts help businesses make better decisions based on future revenue, which will help them to Forecast likely profit or loss in a designated period.
Aiming to discover competitive new products, sales forecasting has been playing an increasingly important role in real-world E-Commerce systems. Current methods either only utilize historical sales records with time series-based models, or train powerful classifiers with subtle feature engineering.
Machine Learning allows businesses to create more advanced forecasting models that utilize a larger data set with minimal human effort. Companies can improve their products and services based on consumer needs by applying machine lSalesning algorithms to their data.
Companies use machine learning algorithms to forecast sales and revenue. That is done by predicting consumer behavior with data from past transactions. By doing this, companies can create accurate forecasts and prepare for future events.
Machine Learning in sales forecasting is the process of training a model to take sales activity data, Learning how each input contributes to a weighted output, and use the model to predict outcomes based on previously unseen and real-time performance data. marketers can analyze past customer behavior and make predictions about what they might do in the future. This allows marketers to create customized campaigns that are more likely to resonate with each individual customer.
Machine Learning model predictions allow businesses to make highly accurate guesses as to the likely outcomes of a question based on historical data.
Grasal is a web application that is created using machine learning to help businesses to predict their future sales."
