This repository contains a Jupyter Notebook that demonstrates how to perform K-Means Clustering on a dataset using Python and scikit-learn library.
K-Means Clustering is a popular unsupervised machine learning algorithm used for grouping data points into clusters. In K-Means, we first select K number of centroids (K is the number of clusters we want to form). Then we calculate the distance between each data point and the centroids. Based on the distance, we assign each data point to its nearest centroid. After this, we update the centroids by taking the mean of all the data points assigned to each centroid. This process is repeated until the centroids no longer move or until a specified number of iterations is reached.
This Jupyter Notebook contains step-by-step instructions on how to perform K-Means Clustering on a dataset using Python and scikit-learn library. The notebook uses a sample dataset provided by scikit-learn library, but you can easily replace it with your own dataset.
To use this notebook, you will need to have Jupyter Notebook installed on your machine. You will also need to install the scikit-learn library. You can do this by running the following command in your terminal:
- pip install scikit-learn
Once you have installed the required libraries, you can open the notebook and follow the instructions to perform K-Means Clustering on your dataset.
K-Means Clustering is a powerful machine learning algorithm that can be used to group data points into clusters. This Jupyter Notebook provides a step-by-step guide on how to perform K-Means Clustering on a dataset using Python and scikit-learn library. With this notebook, you can easily apply K-Means Clustering to your own datasets and gain insights from your data.