🫀 Heart disease classification

figure out if you have heart disease or not

💠 Introduction

This notebook explores the utilization of diverse Python-based machine learning and data science libraries to develop a predictive machine learning model with the objective of determining whether an individual has heart disease or not, based on their following medical attributes.

Some features are (in total 14):

• age
• sex
• chest pain type (4 values)
• resting blood pressure
• serum cholestoral in mg/dl
• etc.

Target variable will be:

• 0 : No heart disease
• 1 : Heart disease

💠 Tested models

Our analysis included six distinct machine learning models ( mainly from scikit-learn ): LogisticRegression(), KNeighborsClassifier(), RandomForestClassifier(), XGBClassifier(), GaussianNB, and CatBoostClassifier(). And the best performance model, Logistic Regression has been chosen to be tuned with RandomizedSearchCV().

💠 Data

The original data came from the Cleavland data from the UCI Machine Learning Repository.