sample-ds-project¶

A Sample Project for Data Science experiments This repository contains a sample Data Science application built with FastAPI, designed to streamline model training and prediction processes via RESTful APIs. The application leverages Poetry for dependency management, ensuring a robust and scalable development environment.

Features¶

FastAPI Endpoints:¶

• /train-model: API endpoint to initiate model training with provided data and configurations.
• /predict: API endpoint for generating predictions using the trained model.

Poetry for Dependency Management:¶

• Simplifies package installation and management.
• Ensures compatibility and reproducibility of the project environment.

Scalable Architecture:¶

• Modular design with clear separation of concerns.
• Easy integration of new features or pipelines.

Prerequisites¶

• Python >= 3.12
• Poetry installed (pip install poetry)

Installation¶

1. Clone the repository:

   git clone https://github.com/DeepakPant93/sample-ds-project.git
   cd sample-ds-project
   

2. Install dependencies using Poetry:

   poetry install
   

3. Activate the virtual environment:

   poetry shell
   

4. Run the FastAPI server:

   uvicorn sample_ds_project.main:app --reload
   

API Endpoints¶

/train-model¶

• Method: POST
• Description: Triggers the model training process using provided training data and configuration.
• Input: JSON object containing training data and optional hyperparameters.
• Output: Success or error message indicating the status of training.

/predict¶

• Method: POST
• Description: Generates predictions for new data using the trained model.
• Input: JSON object containing features for prediction.
• Output: Prediction results, including probabilities or class labels.

Project Structure¶

sample_ds_project/
├── config/          # Configuration files and settings
├── components/      # Reusable components for the application
├── constants/       # Static constants and enumerations
├── entity/          # Definitions of data models and schemas
├── exception/       # Custom exception classes for error handling
├── logging/         # Logging setup for the application
├── pipeline/        # Data science pipeline modules (ingestion, validation, training, etc.)
├── routes/          # API route definitions
├── utils/           # Utility functions (e.g., file handling, data encoding)
└── main.py          # Entry point for the FastAPI application

Enjoy building with this Data Science FastAPI application! 🚀