Heart Disease Prediction image

Heart Disease Prediction

View Project

Built a machine learning model to predict the likelihood of heart disease using various medical features.

Features

  • Data Collection : Used public heart disease datasets for training the model.
  • Feature Engineering : Performed feature selection and engineering to improve model performance.
  • Model Building : Built and compared different ML models like Logistic Regression, Random Forest, and XGBoost.
  • Evaluation : Evaluated model performance using metrics like accuracy, precision, recall, and F1-score.

Overview

This project involved building a machine learning model that predicts the likelihood of heart disease based on features like age, gender, cholesterol levels, blood pressure, and more...

Tools

  • Scikit-learn : For building and evaluating machine learning models.
  • Pandas : For data manipulation and analysis.
  • Matplotlib, Seaborn : For visualizing the data and results.
  • XGBoost : For building advanced machine learning models.