This project involves building and evaluating several machine learning models for predicting heart disease based on a dataset containing patient health information. The main objective is to preprocess the data, perform feature engineering, and train various classification models to identify the best-performing one.
The dataset used is heart.csv, which includes features such as age, cholesterol levels, and heart rate metrics, and is aimed at predicting the presence of heart disease in patients.
- Age to Max Heart Rate Ratio:
age_max_heart_rate_ratio= age / max heart rate - Age Range: Categorized into bins [30-39, 40-49, 50-59, 60-69, 70-79]
- Cholesterol to Max Heart Rate Ratio:
cholesterol_hdl_ratio= cholesterol / max heart rate - Heart Rate Reserve:
heart_rate_reserve= max heart rate - resting blood pressure
Ensure you have the following packages installed:
pip install pandas numpy scikit-learn xgboost matplotlib joblib streamlit