Day 17 | Heart Disease Prediction using KNN Algorithm | Machine Learning in Python

In this video, we implement the K-Nearest Neighbors (KNN) Classification Algorithm to predict heart disease using a real-world dataset in Python. You will learn how to build a machine learning model step by step, including data preprocessing, model training, prediction, and evaluation using the KNN algorithm. This tutorial is perfect for engineering students, beginners in machine learning, and data science enthusiasts who want to understand how classification algorithms work in real-world healthcare datasets. 📌 Topics Covered Introduction to KNN Classification Understanding the Heart Disease Dataset Data preprocessing and feature selection Training the KNN model Predicting heart disease Model accuracy and evaluation 🧑‍💻 Tools & Libraries Used Python Pandas NumPy Scikit-learn Matplotlib / Seaborn 📂 Dataset & Code GitHub Link: https://github.com/DataLearnm/Datasets.git 🎓 Who Should Watch? Data Science Beginners Machine Learning Students Engineering Students Anyone learning Python for ML If you enjoy learning Machine Learning, Data Science, and Python, make sure to Like 👍, Share 🔁, and Subscribe to the channel DataLearnM for more tutorials.