kul-arun/heart-disease-prediction

Generate a Machine Learning model which is capable of predicting whether a person has heart disease or not, based on the medical attributes of the person.

Heart Disease Prediction

The aim of this project is to create a Machine Learning model which is capable of predicting whether a person has heart disease or not, based on the medical attributes of the person. Thus, we have a binary classification task at hand. We shall use the Cleveland heart disease dataset which is taken from the UC Irvine Machine Learning Repository. The various medical attributes present in the dataset are explained in the jupyter notebook. In this project, we perform the following tasks:

• Data Preprocessing
• Exploratory Data Analysis (EDA)
• Testing the classification accuracy of several baseline models
• Tuning the hyperparameters of the chosen model
• Evaluating the performance of the tuned model using several metrics
• Inspecting the important features in the dataset

Installation

• Python

Install Python 3.9 or higher. Create a virtual environment with:

python3 -m venv <virtual-environment-name>

Example: To create a virtual environment called ML-project, use

python3 -m venv ML-project

Activate the virtual environment with:

source <path-to-virtual-environment>/bin/activate

The virtual environment can be deactivated with:

deactivate

• Jupyter Notebook

Install Jupyter Notebook with:

pip install notebook

To run the notebook:

jupyter notebook

• Additional Requirements

Install the necessary packages listed in requirements.txt via:

pip install -r requirements.txt