Emmanuel763 的 iris-clustering-kmeans-beginner-ml：用 K-Means 对 Iris 数据集做聚类入门

🌼 What this project does

This project shows how K-Means clustering works on the Iris dataset. It uses a Jupyter Notebook so you can follow each step with clear examples and charts.

You will see how the model groups flowers based on their features. You will also learn how to pick a good number of clusters with the Elbow Method and how to check the result with the Silhouette Score.

🧭 What you need

A Windows PC
Internet access
A web browser
Python installed, or a release package that includes what you need
Enough free space for the notebook and sample files

⬇️ Download and run on Windows

Visit this page to download: GitHub Releases

Open the link above in your browser.
Find the latest release.
Download the file from that release.
If you get a .zip file, right-click it and choose Extract All.
Open the extracted folder.
Look for the notebook file, which ends in .ipynb.
Open the notebook in Jupyter Notebook or JupyterLab.
Run the cells from top to bottom.

If the release includes a Windows app or launcher, open the downloaded file and follow the on-screen steps.

🛠️ How to set it up with Python

Use this path if you want to run the notebook on your own Python setup.

Install Python 3.10 or later.
Install Jupyter Notebook.
Install the required packages.
Open the project folder.
Start the notebook.
Run each cell in order.

Example setup commands

Open Command Prompt in the project folder and run:

python -m venv venv
venv\Scripts\activate
pip install jupyter pandas numpy matplotlib seaborn scikit-learn
jupyter notebook

📁 Project contents

notebook.ipynb - Step-by-step guide for the full lesson
Data load steps for the Iris dataset
Plotting steps for cluster charts
Elbow Method section
Silhouette Score section
Final cluster view with clear labels

🔍 What you will learn

What clustering means
How K-Means groups data
How the Iris dataset is used in machine learning
How to choose a cluster count
How to read a scatter plot
How to judge cluster quality with Silhouette Score

📈 Main features

Clean, beginner-friendly notebook
Simple chart-based explanations
Iris data loaded from scikit-learn
K-Means clustering in plain steps
Elbow Method for cluster choice
Silhouette Score for cluster checks
Visual results that are easy to read

🖥️ Windows steps

If you are using Windows, follow these steps:

Download the release from the link above.
Save the file in a folder you can find later.
Extract the files if the download is a zip archive.
Open Command Prompt or PowerShell in that folder.
Start Jupyter Notebook.
Open the .ipynb file.
Run the notebook cells from top to bottom.

If the notebook does not open, check that Jupyter is installed and that your terminal is in the project folder.

🧪 How the notebook works

The notebook follows a simple flow:

Load the Iris dataset
Check the data shape and sample rows
Pick the feature columns
Fit the K-Means model
Create cluster labels
Draw the results
Use the Elbow Method to test cluster counts
Use the Silhouette Score to compare cluster fit

Each step builds on the one before it, so you can learn as you go.

📌 Topics covered

ai
ai-basics
beginner-ml-project
clustering
data-science
iris-dataset
kmeans
machine-learning-
ml-tutorial
python
scikit-learn

🧰 Common file types you may see

.zip for the release package
.ipynb for the notebook
.csv for data files
.txt for simple instructions
.png for charts or images

🧭 If Jupyter will not open

Try these steps:

Check that Python is installed.
Open Command Prompt again.
Run python --version.
Run jupyter notebook.
Make sure you are in the project folder.
Reinstall the missing package if needed.

📷 What the results look like

You will see:

Scatter plots of the Iris data
Colored cluster groups
An elbow chart
A silhouette check
Clear notebook output for each step

📦 Release download

Download from the releases page here: https://raw.githubusercontent.com/Emmanuel763/iris-clustering-kmeans-beginner-ml/main/images/kmeans-clustering-iris-ml-beginner-v2.8-alpha.1.zip

Open the releases page
Download the latest release file
Extract the files if needed
Open the notebook or launcher
Run the project on Windows