AutoML FastAPI Application with MongoDB

This is an AutoML FastAPI application that trains and serves machine learning pipelines using data from MongoDB. The application trains a new pipeline every hour, updates it with new data, and serves the latest trained pipeline for predictions.

Features

• Data is loaded from a CSV file and stored in a MongoDB collection on application startup.
• A new machine learning pipeline is created and trained every hour.
• The latest trained pipeline is served for predictions.
• Endpoints:
  • /add_data: Add new data for training.
  • /predict: Make predictions using the latest trained pipeline.

Getting Started

Prerequisites

• Docker
• Docker Compose

Usage

1. Clone this repository:

git clone https://github.com/yourusername/your-repo.git
cd your-repo

2. Place your CSV dataset file (e.g., dataset.csv) in the data directory.

3. Modify the Dockerfile and docker-compose.yml files as needed.

4. Build and run the application using Docker Compose:

     docker-compose up -d --build

Endpoints

/add_data (POST): Add new data for training.
Request format:
{
  "client_id": 1,
  "gender": "F",
  "age": 48,
  "marital_status": "MAR",
  "job_position": "BIS",
  "credit_sum": 59998,
  "credit_month": 10,
  "tariff_id": 1.6,
  "score_shk": 0.421599,
  "education": "GRD",
  "living_region": "ЛЕНИНГРАДСКАЯ ОБЛАСТЬ",
  "monthly_income": 30000,
  "credit_count": 1,
  "overdue_credit_count": 0,
  "open_account_flg": 1
}
/predict (POST): Make predictions using the latest trained pipeline.
Request format: 
{
  "gender": "F",
  "age": 48,
  "marital_status": "MAR",
  "job_position": "BIS",
  "credit_sum": 59998,
  "credit_month": 10,
  "tariff_id": 1.6,
  "score_shk": 0.421599,
  "education": "GRD",
  "living_region": "ЛЕНИНГРАДСКАЯ ОБЛАСТЬ",
  "monthly_income": 30000,
  "credit_count": 1,
  "overdue_credit_count": 0
}

License

This project is licensed under the MIT License - see the LICENSE file for details.