How nguyenanhducit2/api_projdt works
<explanation>
1. Project Type and Purpose
- This is a data‑science/ML estimator tool that consists of two broad phases:
a. Offline data pipeline (scraping, cleaning, EDA, model training)
b. A production inference service (Flask API that loads a serialized model and returns salary estimates)
- Its primary goal is to let clients send job‑feature payloads and receive a data‑science salary estimate.
2. File Structure Analysis
- Top‑level scripts and notebooks under the root handle data ingestion (crawl_data.ipynb, crawl_Integrated_data.csv), cleaning (data preprocessing..py, data_cleaned.csv), EDA (data_eda.ipynb, eda_data.csv), and model building (model_building.py, resulting in model_file.p).
- The FlaskAPI directory contains the production service:
• app.py – entry point for the Flask application
• api.py – defines the HTTP endpoint(s) for inference
• data_input.py – request payload parsing & feature‐vector assembly
• models/model_file.p – serialized RandomForest model
• requests.py – example client that calls the API
• requirements.txt – Python dependencies
3. README Insights
- Data is scraped from Glassdoor using Python & Selenium (1000+ records)
- Feature engineering extracts numeric indicators for skills (Python, R, Excel, AWS, Spark)
- Model training compares RandomForest, LinearRegression, Ridge; RandomForest wins
- Productionization uses Flask to expose a POST endpoint for salary predictions
4. Main Components for Diagram
a. Data Ingestion & Scraping
- Selenium‐based scraper script → raw CSV
b. Data Cleaning & Feature Engineering
- Preprocessing scripts/notebooks (Pandas transformations) → cleaned CSV & feature matrix
c. EDA & Model Training
- EDA notebooks for visualization
- model_building.py → trains models → serializes best model (model_file.p)
d. Model Storage
- model_file.p in FlaskAPI/models
e. Production API Service
- Flask app loads model at startup
- /predict endpoint (api.py) uses data_input.py to validate & vectorize incoming JSON
- Returns JSON response with estimated salary
f. External Client
- requests.py demonstrates HTTP POST to the Flask endpoint
5. Component Relationships & Data Flow
- Arrow from Scraper → Raw Data Store (CSV)
- Arrow from Raw Data Store → Cleaning/Feature Engineering
- Arrow from Cleaning → Feature Store → Model Training
- Arrow from Model Training → Model Store (pickle file)
- At runtime: External Client → Flask API → Model Store → Prediction → Flask API → External Client
6. Architectural Patterns & Design Principles
- Pipeline pattern for offline data processing stages
- Separation of concerns:
• Data pipeline scripts separated from production code
• API uses a lightweight service encapsulating only inference logic
- Model serialization for reproducible deployments
- Configurable requirements.txt for environment setup
7. Key Technologies & Frameworks to Annotate
- Python 3.x
- Selenium (scraping)
- Pandas / NumPy (cleaning & feature engineering)
- scikit‑learn (model training, serialization)
- Flask (API)
- pickle (model persistence)
- HTTP/JSON (client–server communication)
8. Tailoring the Diagram for this ML + API Project
- Visually separate the “Offline Pipeline” (left side) from the “Production Inference Service” (right side).
- Group all Jupyter notebooks and training scripts in a “Data Science Workspace” container.
- Group Flask files in a “Deployment” or “API Service” container.
9. Diagramming Guidelines
- Use rectangles to represent modules/components (e.g., Scraper, Preprocessor, Model Trainer, Flask App).
- Use cylinders for data stores (e.g., Raw CSV, Cleaned CSV, Feature Store, Model Store).
- Use arrows with labels to show data flow and control flow (e.g., “train_model()”, “POST /predict”).
- Color‑code by layer:
• Blue for data pipeline
• Green for model training
• Orange for service/API
• Grey for external clients
- Clearly label each component, filesystem artifacts (e.g., model_file.p), and endpoints.
- Show the HTTP request/response loop with the client.
10. Emphasize Detail
- Include all offline stages (scraping → cleaning → EDA → training).
- Capture the serialized model as a distinct artifact.
- Show how the FlaskAPI loads and uses it.
- Don’t omit configuration/dependencies (requirements.txt) and example client (requests.py).
By following these steps and focusing on the specific scripts and directories in your project tree, you’ll produce a precise, easy‑to‑read system design diagram that maps every major component—from the initial Glassdoor scrape to the final salary estimate returned via your Flask endpoint.
</explanation>