Estimation and Prediction of Hospitalization and Medical Care Costs

Description
This project aims to analyze and understand the factors influencing medical care costs and provide valuable insights using data visualization techniques. By leveraging a comprehensive insurance dataset, we explore the relationships between age, sex, BMI, smoking status, and region with medical charges. Through interactive visualizations and statistical analysis, we uncover patterns, trends, and significant factors impacting healthcare expenses.

Features
-> Interactive dashboards and visualizations
-> Comparative analysis of medical charges based on age, sex, and BMI
-> Exploration of the relationship between smoking and healthcare costs
-> Regional analysis of medical charges and BMI distribution
-> Odds ratio analysis to determine significant factors affecting charges
-> User-friendly interface for easy data exploration and insights

Installation
1. Clone the repository: git clone https://github.com/your-repo.git
2. Install the required packages: pip install -r requirements.txt
3. Set up the database and import the dataset.
4. Run the Flask application: python app.py
5. Open your web browser and visit http://127.0.0.1 to access the Medistats website.

Usage
-> Navigate to the Medistats website on your web browser.
-> Explore the different dashboards and visualizations available.
-> Interact with the charts to gain insights into medical care costs based on various factors.
-> Use the search and filtering options to customize your analysis.
-> Refer to the user guide for more detailed instructions on using the website.

Contributing
Contributions are welcome! If you'd like to contribute to this project, please follow these guidelines:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with descriptive messages.
4. Push your changes to your forked repository.
5. Create a pull request detailing your changes.

License
This project is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgements
-> Kaggle for providing the "Insurance Cost Prediction" dataset
-> Bootstrap for the Medicio template
-> Tableau for data visualization and analytics
-> Flask for web application development
-> MySQL for the database management