Heart Disease Prediction

This project utilizes machine learning techniques to predict the likelihood of heart disease based on various health metrics and patient data. By analyzing patterns in historical data, the model aims to assist in early diagnosis and aid healthcare professionals in decision-making.

Features

Exploratory Data Analysis (EDA): Gain insights into the dataset, including the distribution of features and correlations. Data Preprocessing: Handle missing data, encode categorical features, and scale numerical variables. Model Training and Evaluation: Train multiple machine learning models and evaluate their performance using appropriate metrics. Feature Importance Analysis: Identify key health indicators contributing to heart disease risk. Technologies Used

Programming Language: Python 3 Libraries and Frameworks: Pandas, NumPy: Data analysis and preprocessing Matplotlib, Seaborn: Data visualization Scikit-learn: Machine learning model implementation and evaluation Dataset

The dataset includes patient health records with the following attributes:

Age Sex Chest pain type Resting blood pressure Cholesterol level Fasting blood sugar Resting ECG results Maximum heart rate achieved Exercise-induced angina Oldpeak (ST depression induced by exercise) Slope of the peak exercise ST segment Number of major vessels colored by fluoroscopy Thalassemia (thal) The target variable indicates the presence or absence of heart disease.

How to Use

Clone the Repository git clone https://github.com/pranavprasad7/Heart-Disease-Prediction.git cd Heart-Disease-Prediction Install Dependencies Ensure you have Python 3 installed, then install the required libraries: pip install -r requirements.txt Run the Project Execute the main script to preprocess data, train models, and evaluate results: python main.py Analyze Outputs Visualize model performance and feature importance using the provided graphs and metrics.

Project Structure

data/ - Contains the dataset(s) used for the project notebooks/ - Jupyter notebooks for EDA and initial experimentation src/ - Core Python scripts for preprocessing, modeling, and evaluation requirements.txt - List of dependencies README.md - Project overview and instructions

Models Implemented

Logistic Regression Decision Trees Random Forest Support Vector Machines (SVM) K-Nearest Neighbors (KNN)