❤️🩺 Heart-Disease-prediction-using-genomic-profile-project

This project focuses on predicting the likelihood of heart disease in patients using machine learning techniques. The model employs the RandomForestClassifier algorithm to analyze a range of health parameters and assess the probability of heart disease.

Overview

This project aims to predict the presence of heart disease in patients using machine learning techniques. The model leverages the RandomForestClassifier algorithm to analyze various health parameters and determine the likelihood of heart disease.

📚 Table of Contents

• Features
• Technologies Used
• Installation
• Usage
• Model Evaluation

🌟 Features

• User-friendly web application built with Streamlit.
• Predicts heart disease based on user-provided health metrics.
• Provides an overview of model accuracy and performance.

💻 Technologies Used

• Python 🐍
• Streamlit 🌊
• Scikit-learn 📊
• Pandas 📚
• NumPy 🔢
• Matplotlib/Seaborn 🎨

🚀 Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/swayamjk10/-heart-disease-prediction-using-genomic-profile-.git

2. Navigate to the project directory:

   cd -heart-disease-prediction-using-genomic-profile-

3. Create a virtual environment (optional but recommended):

   python -m venv venv

4. Activate the virtual environment: On macOS/Linux: source venv/bin/activate

5. Install the required packages:

pip install -r requirements.txt

🖥️ Usage

6. Run the Streamlit app:

streamlit run app7.py

Open a web browser and navigate to http://localhost:8501.

Input the required health parameters in the application and submit to receive a prediction. 🩺

📈 Model Evaluation

The model's performance metrics, including accuracy, precision, recall, and F1 score, can be found in the model evaluation section. Detailed visualizations of feature importance and prediction outcomes are included.