Feegow Data Analysis

A project to extract data from the Feegow API, perform marketing-focused analysis, and deliver insights to non-technical stakeholders.

Overview

The main objective of this project is to leverage the data available through the Feegow API to generate valuable marketing insights for medical clinics. This involves:

1. Data Extraction: Systematically pulling relevant data points from the Feegow platform.
2. Data Analysis: Running scripts to process the data and identify trends, patterns, and key performance indicators (KPIs) relevant to marketing efforts.
3. Insight Delivery: Presenting the findings in a simple and accessible format, such as automated email reports or a simple, self-hosted website.

Current Status

🚧 This project is in the early stages of development. 🚧

The current focus is on building the foundational components for interacting with the Feegow API and establishing a solid structure for the data analysis scripts. The project is not yet ready for production use.

Planned Features

• Robust API Client: A stable and reusable client to handle authentication and requests to the Feegow API.
• Data Extraction Scripts: Scripts to fetch specific datasets, such as patient demographics, appointments, and sources.
• Marketing Analysis Modules:
  • Patient acquisition source analysis.
  • Patient retention and churn rate.
  • Appointment scheduling patterns.
• Automated Reporting: A mechanism to generate and send reports (e.g., via email).
• Simple Web Dashboard: A basic web interface to visualize the key metrics.

Getting Started

These instructions will guide you to get a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

• Python 3.8+
• A Feegow API Access Token. You can find more information in the official documentation.

Installation

1. Clone the repository:

   git clone https://github.com/caazzi/feegow_data_analysis.git
   cd feegow_data_analysis

2. Create a virtual environment and install dependencies: It's recommended to use a virtual environment.

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   pip install -r requirements.txt

   (Note: A requirements.txt file will be added as dependencies are finalized.)

3. Set up environment variables: Create a .env file in the root directory of the project by copying the example file:

   cp .env.example .env

   Now, edit the .env file and add your Feegow API token:

   FEEGOW_API_TOKEN="your_api_token_here"
   

Usage

Once the scripts are developed, you will be able to run them from the command line. For example:

python src/run_analysis.py --report patient_acquisition

(Note: This is a placeholder for future usage instructions.)

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

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