Linear Regression

This chapter provides an insightful journey into linear regression, a fundamental technique in machine learning. It begins with an exploration of simple linear regression through a fabricated dataset, gradually transitioning to multiple linear regression (MLR) with a real-world Kaggle competition. The chapter emphasizes the importance of understanding and implementing key concepts like data visualization, training and testing sets, p-value, loss function, feature selection, and normalization in the context of MLR. Practical application is a central theme, with the chapter offering hands-on guidance on using KNIME, a powerful tool for machine learning. Readers are introduced to workflow creation in KNIME, starting from data acquisition to model training and testing. Special attention is given to understanding and interpreting statistical measures like p-value and coefficient of determination (R2) in the context of linear regression models. The chapter delves into advanced topics such as feature engineering, overfitting, and model optimization techniques like forward selection. It also explores feature normalization methods, including Z-score and min-max scaling, to enhance model stability and performance. This comprehensive chapter aims to equip readers with both theoretical understanding and practical skills in applying linear regression in machine learning projects.