Building a Machine Learning Pipeline with Python and Scikit-Learn | Step-by-Step Tutorial

Video Description

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-automations-4579 Welcome to our comprehensive tutorial on building powerful machine learning pipelines using Python and Scikit-Learn! In this video, we will guide you through the entire process of creating a robust machine learning pipeline, from data preprocessing to model evaluation Code: https://ryanandmattdatascience.com/scikit-learn-pipelines/ 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord: https://discord.com/invite/F7dxbvHUhg 📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT Scikit-Learn and Machine Learning Playlist: https://www.youtube.com/playlist?list=PLcQVY5V2UY4LNmObS0gqNVyNdVfXnHwu8 Simple Imputer: https://youtu.be/0Hsj0h-tXAY Column Transformer: https://youtu.be/yBLNbeKbFKI Voting Classifier: https://youtu.be/PZoeGriONbU In this video, I walk you through implementing pipelines in Python using scikit-learn, covering everything from basic Pipeline to more advanced column transformations. We start with a simple example using make_pipeline to handle data imputation and logistic regression, then level up to a more complex scenario that processes both numeric and categorical features using Pipeline, ColumnTransformer, and a decision tree classifier. I explain why pipelines are essential for machine learning projects—they prevent data leakage, allow you to save and reuse workflows, and organize your preprocessing steps so the output of one step becomes the input for the next. You'll see exactly how to implement SimpleImputer, StandardScaler, OneHotEncoder, and multiple estimators within a single pipeline structure. I also demonstrate how to split data properly with train_test_split before building pipelines, how to access pipeline steps using named_steps, and even how to save and load your pipelines using joblib for future use. By the end of this tutorial, you'll understand the difference between make_pipeline and Pipeline, know when to use ColumnTransformer for mixed data types, and be able to build production-ready ML pipelines that keep your code clean and maintainable. TIMESTAMPS 00:00 Introduction to Pipelines 01:05 Importing Libraries & Data Setup 02:15 Splitting X and Y Data 03:18 Train Test Split 05:05 Adding Simple Imputer 05:52 Logistic Regression Model 06:22 Make Pipeline Implementation 07:32 Fitting the Pipeline 08:15 Pipeline Scoring 09:17 Named Steps Feature 10:20 Advanced Pipeline Setup 11:42 Working with Numeric & Categorical Data 13:32 Defining Numeric & Categorical Columns 14:15 Building Numeric Pipeline 15:40 Building Categorical Pipeline 17:30 One Hot Encoder Explanation 18:40 Column Transformer Setup 21:50 Decision Tree Classifier 23:15 Final Pipeline Assembly 24:30 Fitting & Scoring Final Model 25:40 Reviewing Complete Pipeline Code 27:50 Saving Pipeline with Joblib OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/ Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.