K-Means Clustering Project

In this project, we’ll apply K-Means clustering to a real-world dataset: customer segmentation. The goal is to group customers based on their spending patterns.

The dataset contains the following columns:

• Customer ID: Unique identifier for each customer.
• Age: Age of the customer.
• Annual Income: Annual income in thousands of dollars.
• Spending Score: A score assigned based on customer behavior and spending patterns (1–100).

We’ll start by importing the necessary libraries and loading the dataset:

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

# Load dataset
data = pd.read_csv('Mall_Customers.csv')
print(data.head())

We’ll select relevant features for clustering and normalize the data if necessary. For this project, we’ll use Annual Income and Spending Score:

# Select relevant features
X = data[['Annual Income (k$)', 'Spending Score (1-100)']].values
print(X[:5])

We use the elbow method to find the optimal number of clusters by plotting the within-cluster sum of squares (WCSS) for different values of K:

wcss = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)
    kmeans.fit(X)
    wcss.append(kmeans.inertia_)

plt.plot(range(1, 11), wcss)
plt.title('Elbow Method')
plt.xlabel('Number of Clusters')
plt.ylabel('WCSS')
plt.show()

Based on the elbow method, we choose the optimal number of clusters (e.g., K=5) and fit the K-Means model:

kmeans = KMeans(n_clusters=5, init='k-means++', random_state=42)
kmeans.fit(X)

# Add cluster labels to the dataset
data['Cluster'] = kmeans.labels_
print(data.head())

We can plot the clusters in a 2D space to observe their separation:

plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_, cmap='viridis')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=200, c='red', marker='X')
plt.title('Customer Segmentation')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.show()

Each cluster represents a group of customers with similar spending patterns. For example:

• Cluster 1: High income, high spending.
• Cluster 2: Low income, low spending.
• Cluster 3: Moderate income, high spending.

While clustering is powerful, real-world applications face challenges like:

• High-dimensional datasets.
• Noisy or incomplete data.
• Interpreting cluster results meaningfully.

In this project, we:

• Explored a real-world dataset.
• Applied K-Means clustering and visualized the results.
• Interpreted the clusters to understand customer behavior.

Next, we will learn about dimensionality reduction techniques like PCA and t-SNE.