CVD-Risk-Predictor 🩺🚀

A group academic project featuring a Cardiovascular Disease (CVD) Risk Prediction App and an Exploratory Data Analysis (EDA) Dashboard. This project utilizes machine learning and data visualization to assess CVD risk based on health metrics.

🔗 Live Links

• 🏥 CVD Risk Prediction App: Click Here
• 📄 Final Project Report: Click Here

📌 Project Overview

Cardiovascular diseases (CVD) are among the leading causes of mortality worldwide. This project aims to:
✔ Predict the likelihood of developing CVD using machine learning models.
✔ Provide data-driven insights via an interactive dashboard.
✔ Offer user-friendly visualization for better understanding of CVD risk factors.

🛠 Tech Stack

• Programming Language: Python 🐍
• Data Analysis & Visualization: Pandas, NumPy, Matplotlib, Seaborn
• Machine Learning: Scikit-learn
• App Development: Streamlit
• Deployment: Streamlit Cloud

📂 Repository Structure

📦 CVD-Risk-Predictor ┣ 📁 Data # Contains dataset for analysis ┣ 📁 EDA_Notebook # Jupyter notebooks for exploratory data analysis ┣ 📁 Models # Machine learning models used for prediction ┣ 📁 Streamlit_Apps # Streamlit app source code for prediction and analysis ┣ 📜 requirements.txt # Dependencies for running the project ┣ 📜 README.md # Project documentation

📊 Exploratory Data Analysis (EDA)

• Understanding CVD risk factors through visualizations
• Correlation analysis between different health indicators
• Data preprocessing and feature engineering

🧠 Machine Learning Model

The app uses logistic regression, random forest, and other classifiers to predict CVD risk. Model performance is evaluated using:

• Accuracy, Precision, Recall, and F1-Score
• ROC Curve & Feature Importance

💡 How to Use

1️⃣ Clone the Repository:

git clone https://github.com/Shubham-S151/CVD-Risk-Predictor.git cd CVD-Risk-Predictor

2️⃣ Install Dependencies:

Copy :

pip install -r requirements.txt

3️⃣ Run the Streamlit App Locally:

Copy

streamlit run Streamlit_Apps/app.py