Predict Heart Disease with KNN in Python [Machine Learning Tutorial]

In this hands-on Project Lab, Dataquest’s Senior Content Developer, Anna Strahl, walks you through how to build a K-Nearest Neighbors classifier to predict the likelihood of heart disease based on patient data.

Whether you're new to machine learning or looking to sharpen your skills, you'll learn how to:

Perform exploratory data analysis
Clean and prepare a real-world medical dataset
Build and tune a KNN model with scikit-learn
Evaluate model performance with accuracy scores and confusion matrices
Visualize correlations and insights from the data

Throughout the session, Anna breaks down each step of the machine learning workflow, shares practical tips, and answers live questions from learners.

This project is ideal for learners familiar with Python, Pandas, NumPy, Matplotlib, Seaborn, and basic ML concepts.

🔗 Try this project on Dataquest:
https://www.dataquest.io/projects/gui...

Project Brief: 7:15
Load the data: 8:53
Exploratory data analysis (EDA): 13:00
Data cleaning: 25:38
Building the model: 32:48
Evaluating the model: 46:06
Q&A: 54:00

#MachineLearning #knn #pythonprojects #Python #DataScience #HealthcareAnalytics