epythonlab/EthiomedDataWarehouse

A comprehensive data warehouse solution for Ethiopian medical business data scraped from Telegram channels, including data scraping, object detection with YOLO, and ETL/ELT processes. # Ethiopian Medical DataWarehouse using YoloV5 A comprehensive data warehouse solution for Ethiopian medical business data scraped from Telegram channels, including data scraping, object detection with YOLO, and ETL/ELT processes. ## Screenshot that shows the FastAPI Call ## Project Directory Structure The repository is organized into the following directories: - `.github/workflows/`: Contains configurations for GitHub Actions, enabling continuous integration and automated testing. - `.vscode/`: Configuration files for the Visual Studio Code editor, optimizing the development environment. - `app`: Contains the implementation of the machine learning model API, allowing interaction with the model through RESTful endpoints. - `notebooks/`: Jupyter notebooks used for tasks such as data exploration, feature engineering, and preliminary modeling. - `scripts/`: Python scripts for data preprocessing, feature extraction, and the implementation of the credit scoring model. - `tests/`: Unit tests to ensure the correctness and robustness of the implemented model and data processing logic. ## Installation Instructions To run the project locally, follow these steps: 1. **Clone the Repository:** ```bash git clone github.com cd EthiomedDataWarehouse ``` 2. **Set up the Virtual Environment:** Create a virtual environment to manage the project's dependencies: **For Linux/MacOS:** ```bash python3 -m venv .venv source .venv/bin/activate ``` **For Windows:** ```bash python -m venv .venv .venv\Scripts\activate ``` 3. **Install Dependencies:** Install the required Python packages by running: ```bash pip install -r requirements.txt ``` ## Tasks ### Task 1: Scraping Data from Telegram Channels - Navigate to the `scripts/` directory and run `telegram_scraper`. - Ensure that the required libraries are installed and store the API ID and hash in the `.env` file. - Next, run `data_cleaner.py` to auto-clean the data. - Once clean …