Machine-Learning-project--Heart-Disease

The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and predictions are made with different algorithms KNN, Decision Tree, Random Forest, SVM,Logistic Regression, etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras'

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:

1. Logistic Regression (Scikit-learn)
2. Naive Bayes (Scikit-learn)
3. Support Vector Machine (Linear) (Scikit-learn)
4. K-Nearest Neighbours (Scikit-learn)
5. Decision Tree (Scikit-learn)
6. Random Forest (Scikit-learn)
7. XGBoost (Scikit-learn)
8. Artificial Neural Network with 1 Hidden layer (Keras)

Accuracy achieved: 95% (Random Forest)

Dataset used: https://www.kaggle.com/ronitf/heart-disease-uci