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Extract\nThe pipeline requests data from the TfL and Open-Meteo APIs.\n\n### 2. Transform\nThe data is cleaned and structured into a more consistent format for downstream use.\n\n### 3. Validate\nBasic checks are applied to improve reliability and reduce the chance of poor-quality outputs.\n\nExamples include:\n- missing value checks\n- consistency checks\n- handling failed or incomplete API responses\n\n### 4. Load\nThe cleaned data is stored in **DuckDB** for structured querying and dashboard use.\n\n### 5. Present\nA **Streamlit dashboard** displays the processed outputs in a user-friendly way.\n\n### 6. Automate\nThe workflow is scheduled through **GitHub Actions** so it can run on a repeatable basis.\n\n---\n\n## Tech Stack\n\n- **Python**\n- **TfL API**\n- **Open-Meteo API**\n- **DuckDB**\n- **Streamlit**\n- **GitHub Actions**\n- **Pytest** (for testing)\n\n---\n\n## Repository Structure\n\n```text\nuk-transit-weather/\n│\n├── .github/workflows/      # Scheduled GitHub Actions workflow\n├── src/                    # Pipeline and application source code\n├── tests/                  # Test files\n├── .env.example            # Example environment variables\n├── requirements.txt        # Project dependencies\n└── README.md               # Project documentation\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm77rahman%2Fuk-transit-weather","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fm77rahman%2Fuk-transit-weather","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm77rahman%2Fuk-transit-weather/lists"}