Python Feature Scaling in SciKit-Learn (Normalization vs Standardization)

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 Today we take a look at how we can apply feature scaling to data sets within scikit-learn in python. This is useful when applying Normalization or standardization to data which allows for machine learning models to perform better. Dataset is available on my Github 🚀 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 Cross Validation: https://youtu.be/glLNo1ZnmPA Train Test Split: https://youtu.be/SjOfbbfI2qY Logistic Regression: https://youtu.be/aL21Y-u0SRs In this video, I walk you through feature scaling in Python using scikit-learn to improve your machine learning models. Feature scaling transforms numerical values to the same scale, making your data more consistent and your models more accurate. I cover two essential methods: normalization and standardization. Normalization scales data between 0 and 1 using the MinMaxScaler, which works by taking your value minus the minimum and dividing by the range. Standardization, on the other hand, creates a mean of zero and standard deviation of one using the StandardScaler, similar to a normal distribution. Unlike normalization, standardization can handle negative values and numbers larger than one, making it better suited for datasets with outliers. I demonstrate both techniques using real baseball statistics data with 465 players and 13 different features. You'll see exactly how to import the scalers from sklearn.preprocessing, fit and transform your data, and convert the results back into clean pandas dataframes. I also compare the results side by side so you can see how each method changes your data differently, including examining the mean, standard deviation, minimum, and maximum values before and after scaling. TIMESTAMPS 00:00 Introduction to Feature Scaling 01:35 Setting Up Data with Pandas 03:00 Data Overview and Preparation 04:40 Creating X1 and X2 Variables 05:17 Standard Scaler Implementation 06:45 Reviewing Standardized Data 08:15 Min Max Scaler Implementation 09:27 Comparing Standardization vs Normalization 10:45 Final Results and Wrap Up 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.