A jupyter notebook comparing different models to find the best model for heart disease prediction
Python libraries used:
- Numpy
- Pandas
- Seaborn
- Matplotlib
- Scikit-learn
- Keras
Machine Learning algorithms used:
Logistic Regression (Scikit-learn)
Naive Bayes (Scikit-learn)
Support Vector Machine (Linear) (Scikit-learn)
K-Nearest Neighbours (Scikit-learn)
Decision Tree (Scikit-learn)
Random Forest (Scikit-learn)
XGBoost (Scikit-learn)
Artificial Neural Network with 1 Hidden layer (Keras)
Highest accuracy achieved: 90% (Random Forest)
Dataset used: https://www.kaggle.com/ronitf/heart-disease-uci