Machine Learning
Heart Disease Classification
Predicting heart disease using clinical parameters with 88%+ accuracy using Logistic Regression and Random Forest.
Scikit-Learn Pandas Classification Medical AI
Project Overview
This project aims to predict whether a patient has heart disease based on various medical attributes. We utilize the Cleveland Heart Disease dataset from the UCI Machine Learning Repository.
Problem Statement
Given clinical parameters about a patient, can we predict whether or not they have heart disease?
Evaluation Metric
Our goal was to reach 88% accuracy during the proof of concept.
Interactive Notebook
Explore the full data analysis, feature engineering, and model training process below.
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