Customer Segmentation – IT and Computer Engineering Guide
1. Project Overview
Objective: Perform customer segmentation using the K-Means
clustering algorithm on shopping data.
Scope: Understand customer behavior by grouping similar customers based on their
purchasing habits.
2. Prerequisites
Knowledge: Basics of Python programming, clustering
algorithms, and data visualization.
Tools: Python, Scikit-learn, Pandas, Matplotlib, and Seaborn.
Dataset: Shopping data containing features like customer ID, age, income, and
spending score.
3. Project Workflow
- Dataset Preparation: Obtain or create a dataset with relevant customer information.
- Data Preprocessing: Handle missing values, normalize features, and select relevant attributes.
- Exploratory Data Analysis (EDA): Understand data distribution and relationships between features.
- Clustering Model: Apply K-Means clustering to segment customers into distinct groups.
- Model Evaluation: Use metrics like inertia and silhouette score to determine optimal clusters.
- Visualization: Plot clusters to visualize customer segments and their characteristics.
- Interpretation: Analyze and label clusters based on their behavioral traits.
4. Technical Implementation
Step 1: Import Libraries
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
Step 2: Load and Preprocess the Dataset
# Load dataset
data = pd.read_csv('shopping_data.csv')
# Normalize relevant features
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data[['Age', 'Annual Income (k$)', 'Spending
Score (1-100)']])
Step 3: Determine Optimal Number of Clusters
# Use the Elbow Method
inertia = []
for k in range(1, 11):
kmeans = KMeans(n_clusters=k,
random_state=42)
kmeans.fit(data_scaled)
inertia.append(kmeans.inertia_)
# Plot the elbow curve
plt.figure(figsize=(8, 5))
plt.plot(range(1, 11), inertia, marker='o')
plt.xlabel('Number of Clusters')
plt.ylabel('Inertia')
plt.title('Elbow Method for Optimal Clusters')
plt.show()
Step 4: Apply K-Means Clustering
# Apply K-Means with the optimal number of clusters (e.g., 5)
kmeans = KMeans(n_clusters=5, random_state=42)
data['Cluster'] = kmeans.fit_predict(data_scaled)
Step 5: Visualize Clusters
# Visualize the clusters
sns.scatterplot(x='Annual Income (k$)', y='Spending Score (1-100)',
hue='Cluster', data=data, palette='viridis')
plt.title('Customer Segmentation')
plt.show()
5. Results and Insights
Analyze and interpret the characteristics of each cluster to derive actionable insights for marketing or product targeting.
6. Challenges and Mitigation
Feature Selection: Ensure the features used are relevant to
the segmentation goal.
Optimal Clusters: Use silhouette scores or other methods to validate cluster
quality.
7. Future Enhancements
Incorporate additional features like online behavior or
purchase history.
Apply advanced clustering techniques like DBSCAN or hierarchical clustering.
8. Conclusion
The Customer Segmentation project demonstrates the application of K-Means clustering in understanding and categorizing customer behaviors, aiding in data-driven decision-making.