This repository presents a linear regression-based solution for predicting advertising sales. In the realm of marketing and advertising, understanding the impact of various factors on sales performance is crucial for optimizing marketing strategies and budget allocation. Leveraging historical data on advertising expenditures across different channels and corresponding sales figures, this project employs linear regression modeling to establish relationships between advertising spending and sales outcomes. By analyzing the coefficients of the regression model, advertisers can gain insights into the effectiveness of different advertising channels and make informed decisions to maximize sales revenue. The repository includes data preprocessing, model training, and evaluation, offering a practical framework for advertising sales prediction using linear regression techniques.
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