Regression Using Pycaret

Overview

This Jupyter Notebook provides a comprehensive tutorial on using PyCaret for regression tasks. The notebook can be opened and run directly in Google Colab, making it easy to follow along without any local setup.

Table of Contents

• Introduction
• Setup
• Running on Google Colab
• Contents

Introduction

In this notebook, you will learn how to use PyCaret, a low-code machine learning library in Python, to build and evaluate regression models efficiently. The tutorial covers various techniques such as hyperparameter tuning, ensembling, stacking, and blending.

Setup

To run this notebook, you don’t need to install anything locally. You can open and execute the notebook directly in Google Colab, which provides a cloud-based environment with all the necessary dependencies pre-installed.

Running on Google Colab

Follow these steps to run the notebook on Google Colab:

1. Open the Notebook on GitHub:

   • Navigate to the notebook file (Regression_using_Pycaret.ipynb) on the GitHub repository.

2. Open with Colab:

   • Once you are viewing the notebook file on GitHub, you will see a Open in Colab button if available, or you can manually open it in Colab by replacing github.com in the URL with colab.research.google.com/github/. For example:

     https://colab.research.google.com/github/your-username/your-repo-name/blob/main/Regression_using_Pycaret.ipynb
     

3. Run the Notebook:

   • After the notebook opens in Colab, you can run each cell sequentially by clicking on the play button next to each code cell.

4. Save Your Work (Optional):

   • If you want to save your work, you can either save a copy to your Google Drive by selecting File > Save a copy in Drive, or you can download the notebook by selecting File > Download .ipynb.

Contents

• Data Loading and Exploration: How to load and explore your dataset.
• Setup and Model Comparison: Initializing PyCaret and comparing different regression models.
• Hyperparameter Tuning: Fine-tuning the model for better performance.
• Ensembling and Stacking: Advanced techniques to improve predictions.
• Model Evaluation: Assessing the model's performance.
• Model Deployment: How to deploy your model for real-world use.