Machine Learning API Project

This repository contains a machine learning API project built with Flask.

Overview

This project provides an API for making predictions using a trained machine learning model. The model is trained on the Iris dataset and can predict the species of iris flowers based on their sepal and petal measurements.

Installation

1. Clone the repository:

   git clone https://github.com/lainceline/machine-learn.git
   cd machine-learn

2. Set up a virtual environment and activate it:

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

3. Install the required dependencies:

   pip install -r requirements.txt

Training the Model

To train the machine learning model, run the following command:

python model.py

This script trains a RandomForestClassifier on the Iris dataset and saves the trained model to a file named model.joblib.

Running the Flask API

To start the Flask API, run the following command:

python app.py

The API will be accessible at http://127.0.0.1:5000/.

API Endpoints

• GET /: Welcome message
• POST /predict: Make a prediction

Example Request

To make a prediction, send a POST request to http://127.0.0.1:5000/predict with the following JSON data:

[
  {"sepal length (cm)": 5.1, "sepal width (cm)": 3.5, "petal length (cm)": 1.4, "petal width (cm)": 0.2}
]

Example Response

The API will return a JSON response with the predicted species:

[0, 1, 2]

Running Tests

1. Ensure you are in the virtual environment.

2. Run the tests using unittest:

   python -m unittest discover -s tests

Directory Structure

• model.py: Script for training and saving the machine learning model.
• app.py: Flask application providing the API.
• requirements.txt: Python dependencies.
• tests/: Directory containing unit tests.

License

This project is licensed under the MIT License.

Contributing

If you'd like to contribute to this project, please follow these guidelines:

• Fork the repository
• Create a new branch (git checkout -b feature-branch)
• Make your changes
• Commit your changes (git commit -m 'Add new feature')
• Push to the branch (git push origin feature-branch)
• Create a new Pull Request

Contact

For any questions or inquiries, please contact sarahmyers180@gmail.com.

Happy coding!