Machine Learning API

📌 Overview

This repository contains an API that serves a Machine Learning model for predictions. The API is built using FastAPI and serves a pre-trained model stored in a pickle file. It receives input in JSON format, processes it, and returns predictions.

🚀 Features

• Trains a Machine Learning model and serializes it using joblib
• Provides an API endpoint to make predictions
• Accepts JSON input for inference
• Includes a sample .json file for testing
• Supports API request handling via a separate script

📂 Project Structure

📦 ML_API_Repository
 ┣ 📜 model.pkl                # Trained Machine Learning model
 ┣ 📜 api.py                    # FastAPI implementation
 ┣ 📜 request.py                # Script to send test requests to API
 ┣ 📜 sample_input.json         # Example input file
 ┣ 📜 requirements.txt          # List of dependencies
 ┗ 📜 README.md                 # This documentation file

🛠 Setup & Installation

1. Clone the Repository

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

2. Create a Virtual Environment (Recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

📡 Running the API

1. Start the API Server

   uvicorn api:app --reload

   The API will run on localhost:8000

2. Test the API (Use request.py or curl)

   python request.py

   OR

   curl -X POST "http://localhost:8000/predict" -H "Content-Type: application/json" -d @sample_input.json

📤 Making a Prediction Request

• Example JSON input (sample_input.json):

  {
    "feature1": value1,
    "feature2": value2,
    "feature3": value3
  }

• Example Response:

  {
    "prediction": "predicted_value"
  }

📜 Commit & Share

1. Commit the necessary files:

   git add api.py model.pkl request.py sample_input.json README.md
   git commit -m "Initial commit - ML API setup"
   git push origin main

2. Share the Repository:
   • Ensure the repository is accessible to the instructor (Icedgarr)
   • Submit the repository link as required

🔗 References

• FastAPI Documentation
• Uvicorn ASGI Server
• Joblib for Model Serialization

📌 Notes

• Ensure that the .pkl file is stored in the same directory as api.py
• The API should be structured to handle errors gracefully
• Modify request.py to test with different inputs

🚀 Happy coding!