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Setup\n\n### Preparing Your Environment\n\n1. **Setup Python Virtual Environment using UV:**\n\n   ```bash\n   uv venv --python python3.11\n   source .venv/bin/activate\n   ```\n\n2. **Install dependencies:**\n\n   ```bash\n   uv pip install -r requirements.txt\n   ```\n\n---\n\n### Setup MLflow for Experiment Tracking\n\nTo track experiments and model runs:\n\n```bash\ndocker-compose -f deployment/mlflow/docker-compose.yml up -d\ndocker-compose ps\n```\n\n\u003e **Using Podman?** Use this instead:\n\n```bash\npodman-compose -f deployment/mlflow/docker-compose.yaml up -d\npodman-compose ps\n```\n\nAccess the MLflow UI at [http://localhost:5555](http://localhost:5555)\n\n---\n\n## Using JupyterLab (Optional)\n\nIf you prefer an interactive experience, launch JupyterLab with:\n\n```bash\nuv python -m jupyterlab\n# or\npython -m jupyterlab\n```\n\n## Model Workflow\n\n### 🧹 Step 1: Data Processing\n\nClean and preprocess the raw housing dataset:\n\n```bash\npython src/data/run_processing.py   --input data/raw/house_data.csv   --output data/processed/cleaned_house_data.csv\n```\n\n---\n\n### Step 2: Feature Engineering\n\nApply transformations and generate features:\n\n```bash\npython src/features/engineer.py   --input data/processed/cleaned_house_data.csv   --output data/processed/featured_house_data.csv   --preprocessor models/trained/preprocessor.pkl\n```\n\n### Step 3: Modeling \u0026 Experimentation\n\nTrain your model and log everything to MLflow:\n\n```bash\npython src/models/train_model.py   --config configs/model_config.yaml   --data data/processed/featured_house_data.csv   --models-dir models   --mlflow-tracking-uri http://localhost:5555\n```\n\n\n## Building FastAPI and Streamlit App\n\n### Building FastAPI\n\nTo build and run the FastAPI that the streamlit app will use, run:\n\n```bash\npodman build -t house-price-predict-fastapi:latest .  # Build the image\npodman run -idtp house-price-predict-fastapi         # Run the container\n```\n\nYou could test the API with Postman, or using curl using:\n\n```bash\ncurl -X POST \"http://localhost:8000/predict\" \\\n-H \"Content-Type: application/json\" \\\n-d '{\n  \"sqft\": 1500,\n  \"bedrooms\": 3,\n  \"bathrooms\": 2,\n  \"location\": \"suburban\",\n  \"year_built\": 2000,\n  \"condition\": \"fair\"\n}'\n```\n**NOTE**: Be sure to replace `http://localhost:8000/predict` with actual endpoint based on where its running.\n\n### Run Streamlit Application with FastAPI\n\nTo run the streamlit app with FastAPI, run:\n\n```bash\npodman-compose build  # Build the image\npodman-compose up -d  # Run the streamlit app with Fastapi\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodex-0915%2Fhouse-price-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodex-0915%2Fhouse-price-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodex-0915%2Fhouse-price-predictor/lists"}