This project aims to predict the likelihood of a person having heart disease based on medical attributes using various machine learning algorithms. It involves data preprocessing, exploratory data analysis (EDA), feature selection, model training, and performance evaluation.
I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not.
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)
Artificial Neural Network with 1 Hidden layer (Keras)
Accuracy achieved: 90.16% (Random Forest)
Dataset used: https://drive.google.com/file/d/1QTKx0ZsfJ7_c3EiaDIZEMIEV93T_qrLs/view?usp=drive_link
Blog Uploaded: https://medium.com/p/5a1602820cf5/edit