AutoML Model Comparison

This project explores Automated Machine Learning (AutoML) using three different libraries:

• PyCaret
• Lazy Predict
• H2O AutoML

📌 Project Overview

The goal of this project is to compare multiple machine learning models automatically and evaluate their performance using different AutoML tools.

🚀 Tools & Libraries Used

• PyCaret: Simplifies the machine learning workflow and provides automated model selection and tuning.
• Lazy Predict: Quickly trains multiple models without extensive preprocessing.
• H2O AutoML: An advanced framework that performs automatic model selection and ensemble learning.

🔍 Results Summary

Each AutoML library was used to train and compare various models. The evaluation metrics include:

• Accuracy / R² Score
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• Area Under Curve (AUC)
• Training Time

For detailed results, check the outputs in the project.

📂 Project Structure

🛠 Installation & Usage

To run this project, install the required libraries:

pip install pycaret lazypredict h2o