Hands-On Machine Learning: Logistic Regression with Python and Scikit-Learn
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Whether you're a beginner or looking to refresh your knowledge, this machine learning tutorial has got you covered. We'll take you step by step through the concept of Logistic Regression, its mathematical foundation, and its practical applications.
After we take a look at how you can use scikit-learn to code the logistic regression and evaluate the model.
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In this machine learning tutorial, I walk you through building a logistic regression model in Python using scikit-learn (sklearn). We start by explaining the difference between linear regression and logistic regression, focusing on how logistic regression uses a sigmoid curve to make binary predictions (yes/no, 0/1).
I demonstrate the complete workflow using a real example with ultra marathon runner data, where we predict whether runners will complete a 50-mile race based on their average weekly training mileage. You'll learn how to handle categorical data using ordinal encoding, visualize your data with matplotlib and seaborn, split your dataset with train_test_split, fit the logistic regression model, and evaluate performance using confusion matrices and classification reports.
By the end of this tutorial, you'll understand exactly when to use **logistic regression**, how to implement it in Python, and how to interpret your model's accuracy using precision, recall, and F1 scores. All code is available in the description below so you can follow along and practice building your own logistic regression models.
TIMESTAMPS
00:00 Introduction to Logistic Regression
01:42 Logistic vs Linear Regression Explained
03:02 Setting Up the Code Environment
05:17 Encoding Categorical Data
07:32 Visualizing the Data with Plots
09:12 Train Test Split Setup
11:22 Building the Logistic Regression Model
13:27 Evaluating Model Performance
15:17 Final Review and Recap
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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.
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