Building Linear Regression from Scratch for YouTube Ad Revenue Forecasting

Overview

This project explores the implementation of linear regression from scratch to predict YouTube ad revenue based on key video performance metrics. Instead of relying on pre-built libraries like sklearn, the model is developed using fundamental mathematical concepts, including gradient descent and cost function optimization.

By implementing the model from first principles, this project provides a deeper understanding of regression analysis while demonstrating its practical application in digital marketing analytics.

Objectives

• Data Exploration & Preprocessing – Analyze and prepare YouTube ad revenue data for modeling.
• Mathematical Foundations – Implement core components of linear regression, including hypothesis function, cost function, and gradient descent using NumPy.
• Model Training & Evaluation – Train the model on real-world data and evaluate its performance using R², RMSE, and MAE.
• Comparative Analysis – Benchmark the custom model against sklearn's LinearRegression to assess performance.

Results & Insights

The model demonstrated strong predictive capability, with key findings including:

• Accurate revenue predictions using a well-optimized regression approach.
• Performance improvements over sklearn's implementation, showcasing the benefits of custom optimization.
• Actionable insights into ad revenue trends based on video engagement metrics.

Performance Comparison

The following scatter plot compares the predictions of the custom linear regression model (blue) and sklearn's LinearRegression model (red) against actual revenue values.

Key Learnings

This project reinforced critical machine learning principles, including:

• The significance of feature selection and preprocessing in predictive modeling.
• The impact of gradient descent optimization on model efficiency and accuracy.
• The importance of evaluating multiple error metrics to assess model reliability.

Conclusion

By developing a regression model from scratch, this project provides a hands-on understanding of fundamental ML concepts and their applications in data-driven marketing strategies. The insights gained from this approach can further enhance predictive modeling techniques for revenue forecasting.