This project involves building a sales prediction model with exploratory data analysis. It utilizes features such as advertising spending on TV, radio, and newspapers to predict sales. The model employs machine learning techniques, specifically a random forest algorithm, to make these predictions.
By analyzing historical data on advertising expenditures and corresponding sales figures, the model learns patterns and relationships to forecast future sales accurately.
The end result is a tool that businesses can use to anticipate sales outcomes based on their advertising strategies, helping them make informed decisions and optimize their marketing efforts.