❤️ Heart Disease Prediction

This project predicts the risk of heart disease using a machine learning model and provides an interactive web interface to input patient data and get predictions.

📊 Key Features Used (Inputs):

Age

Sex

Chest Pain Type

Resting Blood Pressure

Cholesterol

Fasting Blood Sugar

Resting ECG results

Max Heart Rate

Exercise Induced Angina

ST Depression, Slope, Vessels, Thal

(Full list matches dataset columns in data/ folder)

⚙️ How It Works

Data Preparation: Load patient health data from CSV

Model Training: Logistic Regression is trained on historical heart disease data

Prediction: Users input their data through the web interface

Result: Model predicts heart disease risk with confidence level

💻 Tech Stack

Dataset

Python, FastAPI – Machine Learning & Backend logic

Logistic regression model(trained in Jupyter notebooks and later converted to .pkl)

HTML / CSS / JavaScript – Frontend interface

Jupyter Notebooks – Exploratory Data Analysis & Model training

🚀 Live Demo

🔗 Live Demo

📝 Setup Instructions

1. Clone the repository: git clone https://github.com/ParidhiMis/heart_disease_classification.git

2. Install dependencies: pip install -r requirements.txt

3. Run the app: python main.py

4. Open in browser

💡 Purpose:

I built this project to combine healthcare data with machine learning. It helps learn the full ML workflow, from data preprocessing → model training → web interface.