Supercharging your data science projects with GitHub Tools

This repository contains the code for the Supercharging your data science projects with GitHub Tools course on AI+ Training.

This project has been created by customizing an AI generated workspace template. To get a similar template for your own projects, you can use the @workspace agent on GitHub Copilot Chat, together with the /new command.

Learn more about this and other features covered in the webinar by reading the accompanying blog post.

The Python code in the 'my_notebook' Jupyter notebook is extracted from the Microsoft Learn Tutorial Exercise - Train and evaluate a regression model.

Tools

To get the tools used in this webinar, follow these steps:

1. Install VS Code on your machine
2. Sign up for a GitHub Copilot free trial
3. Install GitHub Copilot, GitHub Copilot Chat and GitHub Copilot Codespaces extensions on VS Code.

To execute the Jupyter Notebook on the Cloud, leveraging GitHub Codespaces, click on the button below:

This will open your pre-configured environment on the browser.

Project description

Note: The project description below has been generated by GitHub Copilot.

This project provides a workspace for a Jupyter Python notebook with a GitHub Codespaces configuration. It includes the installation of pandas, numpy, and scikit-learn libraries.

Setup

1. Open this project in GitHub Codespaces. This will automatically create a development container with all the necessary dependencies installed.

2. Once the Codespace is ready, open the terminal and navigate to the notebooks directory.

3. Run jupyter notebook to start the Jupyter notebook server.

4. Open my_notebook.ipynb to start working on the notebook.

Usage

You can use this notebook to write and run Python code. The pandas, numpy, and scikit-learn libraries are already installed, so you can import them directly into your code.

For example:

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier

You can also add markdown cells to document your code and explain your analysis.

Remember to save your changes before closing the notebook.

Dependencies

This project uses the following Python packages:

• pandas
• numpy
• scikit-learn

These packages are listed in the requirements.txt file and are automatically installed when the Codespace is created.

If you want to add more packages, you can add them to the requirements.txt file and rebuild the Codespace.