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To build an end-to-end machine learning pipeline, you need to automate data exploration, feature engineering, model training, hyperparameter tuning, and deployment. This process ensures robust execution by iteratively refining the methodology across the entire ML lifecycle.

The best way to track machine learning experiments is by integrating systematic tracking tools like MLflow or W&B. This records model selections, cross-validation metrics, and hyperparameter optimization iterations to ensure rigorous methodology and reproducible results.

Feature engineering handles missing values through comprehensive profiling, data scaling, and encoding techniques. Creating new features from raw data ensures the predictive model receives structured inputs, directly improving algorithm performance during training.

Yes, you can use this approach for customer churn prediction by performing thorough EDA, engineering relevant features, and training XGBoost. Tracking experiments with MLflow ensures systematic monitoring of the model selection and tuning process.

Yes, the workflow includes statistical analysis and data visualization for EDA. It provides guidance on appropriate statistical tests and visualization techniques to comprehensively profile data, analyze missing values, and understand distributions before modeling.