Sales Prediction Using Machine Learning and Python

In today's competitive market, being able to accurately predict sales is very important for managing inventory, planning revenue, and making strategic decisions. This project shows how to use machine learning to predict the sales and revenue of cricket products in real time. Brand, Product, Price, and Discount are some of the features used in this study. All of these have a direct effect on how customers buy things. We created and tested several machine learning models, such as Linear Regression, Decision Tree Regressor, Random Forest Regressor, and Support Vector Regressor (SVR). We chose SVR as the best model because it was better at predicting sales trends than the others. The project includes both console-based interaction and a graphical user interface (GUI) using Tkinter, which makes it easy for business owners and shopkeepers to use. The GUI lets users enter information about a product and see predicted sales and graphical analysis right away. The project was turned into a standalone .exe application so that the solution could be used on different computers without having to install Python. This project not only automates sales forecasting, but it also gives you information that can help you set better prices and discounts