K-Means Clustering Implementation
├── Introduction
│   └── Overview of K-Means Clustering
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Generating the Dataset
├── Implementing K-Means
│   ├── Data Preparation
│   ├── Model Training
│   └── Identifying Cluster Centers
├── Visualization
│   └── K-Means Clustering Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of K-Means Clustering

• K-Means is a popular unsupervised learning algorithm used for clustering. It partitions the dataset into K clusters by minimizing the variance within each cluster.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

Generating the Dataset

# Python code to generate a sample dataset
X, _ = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)

3. Implementing K-Means

Data Preparation

• No target variable is needed as K-Means is an unsupervised algorithm.

Model Training

# Python code to train the K-Means model
kmeans = KMeans(n_clusters=4)
kmeans.fit(X)

Identifying Cluster Centers

# Python code to identify the cluster centers
centers = kmeans.cluster_centers_

4. Visualization

K-Means Clustering Visualization