Machine learning workflows often suffer from code duplication, data leakage, and hyperparameter tuning complexities. Scikit-Learn's Pipeline and ColumnTransformer modules simplify this by combining feature preprocessing and model estimation into an integrated, reproducible object.
This guide demonstrates a complete predictive modeling workflow using the classic Titanic: Machine Learning from Disaster dataset, covering exploratory data analysis, pipeline-based feature engineering, model selection, and cross-validation.
1. Environment Setup & Data Loading
Begin by importing the necessary libraries for data manipulation, visualization, preprocessing, modeling, and evaluation.
import pandas as pd
