PCA Analysis in Python Explained (Scikit - Learn)

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 guide on Principal Component Analysis (PCA). In this video, we will go over what PCA is and why it's essential in data analysis and dimensionality reduction and How to perform PCA step-by-step with practical examples in Python. Code: https://ryanandmattdatascience.com/principal-component-analysis-scikit-learn/ 🚀 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 Hyperparameter Tuning: https://youtu.be/LrCylIe0RJM Titanic Data Science Project: https://youtu.be/6IGx7ZZdS74 Cross Validation: https://youtu.be/glLNo1ZnmPA In this video, I break down how to implement Principal Component Analysis (PCA) in Python using scikit-learn. PCA is essential when working with high-dimensional datasets that contain many features—not all of which contribute meaningful variance to your model. By reducing non-important features, PCA helps your machine learning models run faster and more efficiently. I walk you through two practical methods for applying PCA: specifying the percentage of variance you want to retain, or setting a specific number of components. Using real MLB team statistics from 2022, I demonstrate the complete workflow from start to finish—including data preprocessing, train-test splits, feature scaling with StandardScaler, and finally implementing PCA. You'll see why scaling is critical before applying PCA, especially when dealing with features on vastly different scales. Throughout the tutorial, I create multiple visualizations to help you understand explained variance ratios and how components contribute to your dataset. I show examples with 95% variance retention, two-component models, and even three-dimensional visualizations to see how teams separate based on playoff qualification. By the end of this video, you'll understand exactly when and how to use PCA in your own machine learning projects, plus you'll have access to all the code on my GitHub repository. TIMESTAMPS 00:00 Introduction to PCA 00:56 Setting Up Jupyter Notebook 01:42 Loading Baseball Dataset 02:46 Data Preprocessing & Dropping Columns 03:28 Setting Up X and y Variables 04:22 Train Test Split 05:11 Standard Scaling Implementation 07:32 Converting to DataFrame 08:23 Implementing PCA 09:42 Variance Ratio Visualization 10:52 PCA with 95% Variance 12:10 PCA with 2 Components 13:22 Visualizing 2D Separation 14:10 PCA with 3 Components 15:00 Summary & Key Takeaways 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.