/Sales-Prediction-with-EDA

This project builds a sales prediction model using machine learning, emphasizing TV, radio, and newspaper advertising expenditures. By analyzing historical data, the model forecasts future sales with a random forest algorithm, aiding businesses in optimizing marketing strategies and anticipating sales outcomes.

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

Sales-Prediction-with-EDA

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.