“A Theoretical and Practical Study of Linear Regression”

This article provides a self-contained description of linear regression, covering both the necessary linear algebra concepts and their implementation in Python. Linear regression remains one of the most interpretable and widely used tools in the data scientist’s toolbox. By mastering both its theoretical foundations and practical applications, one can build robust and explainable models.

In this paper, we explain the fundamentals of linear regression, outline how it works, and guide the reader through the implementation process step by step. We also discuss essential techniques such as feature scaling and gradient descent, which are crucial for improving model accuracy and efficiency. Whether applied to business trend analysis or broader data science applications, this paper serves as a comprehensive introduction to linear regression for beginners and practitioners alike.

Linear Regression, Regression Analysis, Statistical Modeling, Predictive Modeling, Machine Learning, Least Squares Method, Model Evaluation, Data Analysis, Regression Theory

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