ML Library

Streamlining Machine Learning Pipelines

The Machine Learning class is a versatile Python tool designed to simplify the creation, optimization, and evaluation of machine learning pipelines. It integrates seamlessly with popular libraries such as scikit-learn, Optuna (for hyperparameter tuning), and SHAP (for model interpretation).

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Example Usage

For a detailed example using MLCore, please refer to the xxxx.ipynb notebook in this repository. This notebook demonstrates how to leverage the module to streamline the development and optimization of machine learning models.

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Dataset

Start your experimentation with a practical dataset like the Mobile Phone Price Prediction Cleaned Dataset from Kaggle. This dataset offers a real-world scenario for applying MLCore to predictive modeling tasks.

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Setting Up Your Azure ML Workspace

This project includes a Makefile to automate common tasks. Use make <command> to execute specific tasks defined in the Makefile.

Step 1: Create a Compute Instance

Begin by creating a compute instance to run your notebooks.

Step 2: Install Poetry and Dependencies

Install poetry and other dependencies by running:

make setup

This command triggers the setup process defined in the Makefile. For a full list of commands and their functionalities, review the Makefile.

Note: To run your notebooks in this virtual environment, add the created environment to your list of available kernels.

Step 3: Push Code to Notebooks

Push your code to the notebooks using SSH or HTTP:

1. Generate an SSH key on the compute instance.
2. Add this SSH key to your Git repository platform.

Once configured, continue with the Getting_Your_Data.ipynb notebook to retrieve data, set up your environment, and execute jobs.

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Features

This project framework includes the following features:

• Simplified pipeline creation
• Integrated hyperparameter optimization
• Model interpretation support
• Extensible and modular design

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Getting Started

Prerequisites

• Operating System: Windows, Linux, or macOS
• Python (version 3.7 or later)
• Familiarity with command line operations

Installation

To clone and set up the project:

1. Clone the repository:

   git clone [repository URL]

2. Change to the repository directory:

   cd [repository name]

3. Install required dependencies via Poetry:

   make setup

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Quickstart

Follow these steps to get up and running quickly:

1. Clone the repository and navigate into the folder.
2. Run make setup to install dependencies.
3. Open the provided Jupyter notebooks and start experimenting.

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Demo

A demo application is included to showcase the usage of ML Library. To run the demo, follow the steps below:

1. Set up your compute instance and install dependencies.
2. Run the demo using the provided script/notebook.
3. Enjoy exploring machine learning pipeline creation with ease.

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Resources

• Documentation
• Related Projects
• Community Discussions

For additional resources and guidance, visit our repository linked above.