Welcome to Building ML Pipelines! 🚀

In this tutorial, you’ll build a complete machine learning pipeline using Scikit-Learn. We’ll start from a raw tabular dataset and end with a tuned model wrapped in a reusable Pipeline. Along the way, you’ll learn how to handle preprocessing, train/test splits, cross-validation, hyperparameter tuning, and evaluation in a clean and structured way.

What You’ll Build

We’ll build a classification model on a real-world style dataset using Scikit-Learn pipelines. You’ll use the Wine dataset to predict wine class based on chemical properties.

What Tools You’ll Use

• Python - The programming language
• Pandas - For data manipulation
• NumPy - For numerical operations
• Scikit-Learn - Pipeline, ColumnTransformer, GridSearchCV

Tutorial Structure

This tutorial is divided into 7 interactive pages (approximately 30 minutes):

1. Setup and Dataset (5 min) - Install dependencies and explore the dataset
2. Quick Baseline Model (4 min) - Build a simple model without pipelines
3. Adding Preprocessing (5 min) - Use ColumnTransformer for feature preprocessing
4. Building the Full Pipeline (5 min) - Combine preprocessing and model
5. Cross-Validation (4 min) - Use cross-validation for better evaluation
6. Hyperparameter Tuning (5 min) - Optimize model parameters with GridSearchCV
7. Evaluation and Saving (2 min) - Evaluate the final model and save it for reuse

Interactive Features

Throughout this tutorial, you’ll experience:

• 🎬 Animated Concepts - Step-by-step visualizations of ML pipeline processes
• 📊 Animated Diagrams - Interactive system architecture
• 💻 Live Code Runner - Edit and run Python code directly in the browser
• 📑 Tabbed Panes - Compare different approaches side-by-side
• ✅ Knowledge Checks - Test your understanding
• 🎯 Interactive Activities - Hands-on practice with concepts

Prerequisites

Before starting, you should have:

• Comfortable with Python basics
• Used Pandas and NumPy at least once
• Knows what a classification model is, but may still be wiring things manually
• New to doing things “the Scikit-Learn way” with Pipeline, ColumnTransformer, and GridSearchCV

Don’t worry if you’re not an expert - we’ll explain concepts as we go!

Estimated Time

⏱️ 30 minutes to complete all 7 pages

You can take breaks between pages and resume anytime. Your progress will be tracked as you navigate through the tutorial.

---

---

What is a Machine Learning Pipeline?

Quick Preview: A machine learning pipeline is a way to chain together multiple steps of data processing and model training. Instead of manually calling fit_transform on each preprocessing step and then training the model, a pipeline automates this process and ensures consistency between training and prediction.

Why it matters: Pipelines make your code cleaner, prevent data leakage, and make it easier to deploy models to production. They’re the standard way to build ML systems in Scikit-Learn.

Ready to start building? Click the button above to begin your ML pipeline journey!