https://github.com/vedanty3/heart-disease-prediction

This project aims to build a machine learning model using K-Nearest Neighbor, LogisticRegression, RandomForestClassifier to classify whether or not a person has heart disease based upon his medical attributes. (accuracy achieved : 88.52%)
https://github.com/vedanty3/heart-disease-prediction

confusion-matrix correlation-matrices jupyter-notebook knn-classification logistic-regression machine-learning matplotlib numpy pandas python random-forest randomforestclassifier roccurve scikit-learn sklearn zerotomastery

Last synced: about 1 month ago
JSON representation

This project aims to build a machine learning model using K-Nearest Neighbor, LogisticRegression, RandomForestClassifier to classify whether or not a person has heart disease based upon his medical attributes. (accuracy achieved : 88.52%)

• Host: GitHub
• URL: https://github.com/vedanty3/heart-disease-prediction
• Owner: vedanty3
• Created: 2022-01-23T15:19:46.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-02-12T07:30:18.000Z (almost 4 years ago)
• Last Synced: 2024-11-02T18:34:16.846Z (about 1 year ago)
• Topics: confusion-matrix, correlation-matrices, jupyter-notebook, knn-classification, logistic-regression, machine-learning, matplotlib, numpy, pandas, python, random-forest, randomforestclassifier, roccurve, scikit-learn, sklearn, zerotomastery
• Language: Jupyter Notebook
• Homepage:
• Size: 1.72 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
## **_💓 Heart Disease Prediction_**

A machine learning model able to classify whether or not a person has a heart disease.

Heart-Disease-Prediction-using-Machine-Learning

Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate.

The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and 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)_

#### _Accuracy achieved: 86.88% (Random Forest)_

#### _Accuracy achieved: 75.41% (KNearestNeighbors)_

#### _Accuracy achieved: 88.52% (LogisticRegression)_

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