How Do You Actually Improve a Machine Learning Model? | Kaggle Notebook Breakdown | Week 4

What does it actually take to improve a Machine Learning model beyond simply trying different algorithms? In this video, I break down a Kaggle notebook on airline passenger satisfaction and explore the techniques used to investigate data, engineer features, compare models, and improve predictive performance. We'll cover: Exploratory data analysis and missing-value imputation Statistical testing: ANOVA, correlation, and feature relationships CatBoost and hyperparameter tuning with RandomizedSearchCV Feature engineering and transformations Categorical encoding, including target encoding and Weight of Evidence (WOE) Feature interactions, clustering, and dimensionality reduction with PCA Cross-validation and feature selection The focus is on understanding what each technique does, why it matters, how it works, and when it is worth using—not just running code.