bharathp16/UCI_Heart_Disease_for_Binary_Classification

Title: Heart Disease Prediction Dataset (Processed UCI Version)

Subtitle: A cleaned and machine learning-ready version of the UCI Heart Disease dataset for binary classification tasks.

Description:

Overview This dataset is a processed version of the UCI Heart Disease dataset. It contains patient clinical information commonly used for heart disease prediction and binary classification machine learning tasks.

The dataset has been cleaned and prepared for educational purposes, machine learning experiments, classification modeling, feature engineering, and predictive analytics.

Dataset Information Number of Records: 299

Number of Features: 13

Target Variable: target

Classes:

0 = No Heart Disease 1 = Heart Disease Present Missing Values: None

Duplicate Records: None

Features age: Age of the patient sex: Gender of the patient cp: Chest pain type trestbps: Resting blood pressure chol: Serum cholesterol level fbs: Fasting blood sugar restecg: Resting electrocardiographic results thalach: Maximum heart rate achieved exang: Exercise-induced angina oldpeak: ST depression induced by exercise slope: Slope of the peak exercise ST segment ca: Number of major vessels colored by fluoroscopy thal: Thalassemia status target: Presence or absence of heart disease Intended Use This dataset can be used for:

Binary Classification Logistic Regression Decision Trees Random Forests Support Vector Machines XGBoost Feature Selection Model Evaluation Educational Machine Learning Projects Source This dataset is derived from the UCI Heart Disease Dataset. The original dataset has been cleaned and reformatted to make it easier to use for machine learning workflows.

Acknowledgement Credit to the UCI Machine Learning Repository for providing the original heart disease dataset.