https://github.com/bhattbhavesh91/k_means_iris_dataset

KMeans Clustering for IRIS Dataset Classification
https://github.com/bhattbhavesh91/k_means_iris_dataset

classifications clustering iris-dataset k-means-clustering k-means-implementation-in-python kmeans-clustering python

Last synced: 2 months ago
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KMeans Clustering for IRIS Dataset Classification

Host: GitHub
URL: https://github.com/bhattbhavesh91/k_means_iris_dataset
Owner: bhattbhavesh91
License: gpl-3.0
Created: 2017-08-03T06:14:43.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2021-05-29T12:55:28.000Z (about 4 years ago)
Last Synced: 2025-03-29T06:11:18.941Z (3 months ago)
Topics: classifications, clustering, iris-dataset, k-means-clustering, k-means-implementation-in-python, kmeans-clustering, python
Language: Jupyter Notebook
Homepage:
Size: 164 KB
Stars: 23
Watchers: 4
Forks: 26
Open Issues: 1
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE

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README

## K Means clustering for IRIS Dataset Classification

**If you like my work, you can support me by buying me a coffee by clicking the link below**

K Means clustering is an unsupervised machine learning algorithm. An example of a supervised learning algorithm can be seen when looking at Neural Networks where the learning process involved both the inputs (x) and the outputs (y). During the learning process the error between the predicted outcome and actual outcome (y) is used to train the system. In an unsupervised method such as K Means clustering the outcome (y) variable is not used in the training process.

In this example we look at using the IRIS dataset and cover:

Importing the sample IRIS dataset
Converting the dataset to a Pandas Dataframe
Visualising the classifications using scatter plots
Simple performance metrics

# Finding K in K-means Clustering Automatically
## To view the video
* [Click here](https://youtu.be/IEBsrUQ4eMc)
* Click on the image below

[![Finding K in K-means Clustering Automatically](http://img.youtube.com/vi/IEBsrUQ4eMc/0.jpg)](http://www.youtube.com/watch?v=IEBsrUQ4eMc)

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