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About this project

Learning data analysis and machine learning with the popular 'Iris' dataset

In this project, I use the 'Iris' dataset to learn some machine learning and data analytics with Python.

Table of contents

  • Installation
  • Accessing project files
  • Usage
  • References

Installation

  1. VS Code: To follow along and learn as I did, you should download VS code onto your device if not already downloaded. This will allow you to run my Jupyter notebook code cell by code cell after reading each markdown cell.

    • How to install VS Code

  2. Python 3.12.7: Make sure you have Python installed on your computer also. You will need it installed on your machine to use the Jupyter Notebook on VS Code. I would reccomend downloading Anaconda, as it is a distribution of the Python language with pre-installed additional tools and packages, giving you greater scope for this and other projects you might create or learn from in the future.

Accessing project files

Once you have the necessary software installed, download this repository to your device and open the tasks.ipynb file in VS Code.

You can download the repository as a .zip folder and extract it, or if you have SSH set up on your machine, you can clone it directly using the command line.

image

Usage

Once you have the tasks.ipynb file open in VS Code, ensure all outputs are cleared as pictured below, and then follow along by playing each code cell individually and in order. I explain what each code cell does in the markdown cells that precede it. I've also included plenty of comments in the code cells to explain what each block of code does. I've included references to where I learnt how to do this in both the markdown cells and the code cells.