Health Track Pro is a machine learning application designed to analyze health data collected from a diverse group of users, including students and individuals of various age groups. The application utilizes Flask, a Python web framework, to provide a user-friendly interface for inputting health-related information and receiving personalized health reports.
- Data Collection: Health Track Pro collects data from 1000 users, comprising students, individuals from surrounding communities, and other relevant resources.
- Input Parameters: The application prompts users to input the following parameters:
- Age
- Gender
- Smoking status (Yes/No)
- Alcohol consumption (Yes/No)
- Past medical history
- Symptoms
- Blood pressure (BP)
- Heart rate
- Body temperature
- Weight
- Hours of exercise
- Hours of sleep
- Lifestyle (Active/Moderate/etc.)
- Output Reports: Based on the input data, Health Track Pro generates personalized health reports, indicating the severity of health conditions (if any) and providing recommendations for improvement.
- Machine Learning Model: The application employs a Random Forest classifier to analyze the collected data and generate accurate predictions regarding users' health statuses.
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Clone the repository from GitHub:
git clone https://github.com/your_username/Health-Track-Pro.git
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Navigate to the project directory:
cd health-track-pro
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Install dependencies:
pip install -r requirements.txt
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Run the Flask application:
python app.py
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Open a web browser and navigate to
http://localhost:5000
to access the Health Track Pro interface. -
Input the required health parameters and submit the form.
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View the generated health report, including recommendations for diet, lifestyle changes, etc.
The Random Forest classifier was chosen for its ability to handle diverse datasets and provide accurate predictions. It achieves high accuracy in analyzing health data and generating personalized health reports for users.
Contributions to Health Track Pro are welcome! If you'd like to contribute, please fork the repository and submit a pull request with your proposed changes.