Sales Prediction Using Machine Learning Techniques

This study presents a data-driven approach for predicting product sales based on advertising expenditure across TV, Radio, and Newspaper channels. The project utilizes Linear Regression to analyze relationships between variables and generate accurate sales predictions. Exploratory Data Analysis (EDA) was performed to identify patterns and correlations in the dataset. The results indicate that TV and Radio advertising significantly influence sales, while Newspaper has a lesser impact. The model was evaluated using R-squared (R²) and Mean Squared Error (MSE), demonstrating reliable performance. This work highlights the importance of machine learning in supporting data-driven decision-making and optimizing marketing strategies.