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Likhithasai-18/census-income-prediction

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App README

Deploying ML Pipeline with FastAPI

This project demonstrates a complete Machine Learning pipeline for the Census Income Prediction task. It includes data processing, model training, and deployment using FastAPI and Docker.

Project Structure

Deploying-ML-Pipeline-with-FastAPI/
├── data/                   # Dataset files
├── ml/                     # Machine learning modules (data processing, model definition)
├── model/                  # Saved model artifacts (encoder, model.pkl)
├── Experimentation/        # Jupyter notebooks for EDA
├── main.py                 # FastAPI application
├── train_model.py          # Script to train the model
├── evaluation.py           # Script to evaluate model performance
├── Dockerfile              # Docker configuration
├── requirements.txt        # Python dependencies
└── ...

Features

—Machine Learning: XGBoost Classifier trained on Census Income data.
—API: FastAPI for serving real-time predictions.
—Containerization: Docker support for easy deployment.
—Data Versioning: DVC integration (configured).
—Testing: Unit tests for ML components (if applicable).

Installation

1.Clone the repository:

bash

    git clone https://github.com/Lali182k5/Deploying-ML-Pipeline-with-FastAPI.git
    cd Deploying-ML-Pipeline-with-FastAPI

1.Create a virtual environment:

bash

    python -m venv venv
    # Windows
    .\venv\Scripts\activate
    # Linux/Mac
    source venv/bin/activate

1.Install dependencies:

bash

    pip install -r requirements.txt

Usage

1. Train the Model

To retrain the model using the data in data/census_cleaned_data.csv:

bash

python train_model.py

This will save the trained model and encoder to the model/ directory.

2. Run the API Locally

Start the FastAPI server using Uvicorn:

bash

uvicorn main:app --reload

The API will be available at http://127.0.0.1:8000.

3. Docker Deployment

Build and run the application using Docker:

Build the image:

bash

docker build -t fastapi-ml-app .

Run the container:

bash

docker run -p 8000:8000 fastapi-ml-app

API Endpoints

`GET /`

Returns a welcome message.

`POST /data/`

Predicts whether income is <=50K or >50K.

Example Request Body:

json

{
  "age": 37,
  "workclass": "Private",
  "education": "HS-grad",
  "education-num": 10,
  "marital-status": "Married-civ-spouse",
  "occupation": "Prof-specialty",
  "relationship": "Husband",
  "race": "White",
  "sex": "Male",
  "capital-gain": 0,
  "capital-loss": 0,
  "hours-per-week": 40,
  "native-country": "United-States"
}

Example Response:

json

{
  "result": "<=50K"
}

Documentation

Once the app is running, visit http://localhost:8000/docs for the interactive Swagger UI documentation.