/Heart-Disease-Prediction

Heart Disease Prediction - Using Sklearn, Seaborn & Graphviz Libraries of Python & UCI Heart Disease Dataset Apr 2020

Primary LanguageJupyter Notebook

Heart-Disease-Prediction-Using-Multiple-Classifiers

Heart Disease Prediction

Apr 2020

Project Summary :

  1. Dataset : UCI Heart Disease Dataset

  2. Implementation :

-> First task was to analyze and visualize data of UCI Heart Disease Dataset using the Seaborn and Matplotlib libraries of Python.

-> And then it was followed by the Splitting of the dataset for training and testing

-> Training the multiple classifiers using Sklearn Library of the Python

-> Prediction of Heart Disease using Logistic Regression, Decision Tree and Random Forest Algorithms

-> Finding the respective Accuracy, Recall, Precision scores of all the three models

-> Final task was to compare and find the best Algorithm for the dataset, which turn on to be the Random Forest model with an accuracy of 84.31 % and the accuracies of Logistic Regression and Decision Tree were 83.17 %, 73.12 %.