Data Science and Analytics Tools - Customer Segmentation using K-Means Clustering

 Notes:

• What problem does it solve?
  Helps businesses identify distinct customer segments based on purchasing behavior or demographic features, allowing targeted marketing.

• How can businesses or users benefit from customizing the code?
  Businesses can adjust the model for different customer attributes, clustering methods, and target numbers of segments.

• How can businesses or users adopt the solution further, if needed?
  Custom segmentation can be used for marketing campaigns, personalized promotions, and improving customer retention strategies.

Actual Python Code:

import pandas as pd

from sklearn.cluster import KMeans

import matplotlib.pyplot as plt

from sklearn.preprocessing import StandardScaler

# Load customer data (assumed to have columns like 'Age', 'Annual Income', 'Spending Score')

data = pd.read_csv('customer_data.csv')

# Normalize data

scaler = StandardScaler()

scaled_data = scaler.fit_transform(data[['Age', 'Annual Income', 'Spending Score']])

# Apply KMeans clustering

kmeans = KMeans(n_clusters=4, random_state=42)

clusters = kmeans.fit_predict(scaled_data)

# Add cluster labels to the data

data['Cluster'] = clusters

# Visualize the clusters

plt.scatter(data['Annual Income'], data['Spending Score'], c=data['Cluster'], cmap='viridis')

plt.xlabel('Annual Income')

plt.ylabel('Spending Score')

plt.title('Customer Segmentation using K-Means')

plt.show()

# Display cluster centers

print("Cluster Centers:")

print(kmeans.cluster_centers_)