Customer Segmentation using K-Means Clustering

Idea: Analyze a retail dataset (e.g., customer purchase data) to identify distinct customer groups using K-means clustering. This helps in targeted marketing strategies.

1. Import Libraries

You’ll need the following libraries for data manipulation, visualization, and clustering:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

2. Load the Data

You can download a dataset like the Mall Customers Dataset or use any customer data with attributes like age, income, spending score, etc.

Here’s how you load the dataset:

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

3. Preprocess the Data

Remove any missing values or unnecessary columns (if applicable). You can also scale the features for better clustering performance.

# Selecting the features we want to use for clustering
X = data[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]

# Standardizing the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

4. Finding the Optimal Number of Clusters using the Elbow Method

The Elbow Method helps in finding the number of clusters (k) by plotting the within-cluster sum of squares (WCSS) against the number of clusters.

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

# Plotting the Elbow Curve
plt.plot(range(1, 11), wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()

5. Training the K-Means Model

Once you've identified the optimal number of clusters (e.g., 4 or 5 from the elbow method), you can fit the K-Means algorithm to the data:

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

# Predicting the clusters
y_kmeans = kmeans.predict(X_scaled)

# Adding the cluster labels to the original dataset
data['Cluster'] = y_kmeans

6. Visualizing the Clusters

You can use Seaborn or Matplotlib to visualize the clusters. Here’s an example for a 2D plot using any two features:

plt.figure(figsize=(10, 6))
sns.scatterplot(x='Annual Income (k$)', y='Spending Score (1-100)', hue='Cluster', data=data, palette='viridis', s=100)
plt.title('Customer Segmentation')
plt.show()

7. Interpreting the Clusters

You can analyze the resulting clusters by comparing the average characteristics (like age, income, and spending score) of each cluster:

# Grouping data by clusters to understand the characteristics
cluster_profile = data.groupby('Cluster').mean()
print(cluster_profile)

HERE ARE THE RESULTS