AutoML Framework

This project is a desktop-based Automated Machine Learning (AutoML) framework built with Python. It provides a user-friendly graphical interface for users to load datasets, receive intelligent recommendations for machine learning pipelines, and execute the training and evaluation process in parallel, leveraging the Dask library.

Purpose

The main purpose of this framework is to simplify and accelerate the process of building and evaluating machine learning models. It aims to automate the repetitive and time-consuming tasks of data preprocessing, model selection, and hyperparameter tuning, making it easier for users to discover the optimal pipeline for their specific dataset. The inclusion of meta-learning allows the framework to learn from past experiments and improve its recommendations over time.

Result

The application provides the following results:

• A comprehensive analysis of the input dataset.
• A set of recommended machine learning pipelines tailored to the dataset.
• A parallelized training process that tests multiple pipelines.
• A comparison view to analyze the performance metrics (e.g., accuracy, F1-score) of the trained models.
• The ability to save and load entire experiment sessions, including datasets and results.

Getting Started

Follow these instructions to set up and run the project on your local machine.

Prerequisites

• Python 3.8 or higher
• Git

Installation

1. Clone the repository:

   git clone https://github.com/990aa/automl_framework.git
   cd automl_framework

2. Create and activate a virtual environment:

   • On Windows:

     python -m venv .venv
     .venv\Scripts\activate

   • On macOS/Linux:

     python3 -m venv .venv
     source .venv/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

Running the Application

Once the setup is complete, you can run the application with the following command:

python main_app.py

This will launch the AutoML Framework's graphical user interface.