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Unsupervised Machine Learning: A Real-Time Project Use Case

Introduction to Unsupervised Machine Learning

Unsupervised machine learning is a type of algorithm that learns patterns from unlabeled data. Unlike supervised learning, where the model is trained on labeled data, unsupervised learning algorithms are used to draw inferences from datasets consisting of input data without labeled responses. Common tasks include clustering, association, and dimensionality reduction.

Project Use Case: Customer Segmentation for a Retail Company

Objective: Segment customers into distinct groups based on their purchasing behavior to tailor marketing strategies.

Dataset: Assume you have a dataset with customer transaction details, including features like age, annual income, spending score, etc.

Here’s a sample of the customer_data.csv file :

CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100)
1,Male,19,15,39
2,Male,21,15,81
3,Female,20,16,6
4,Female,23,16,77
5,Female,31,17,40
6,Female,22,17,76
7,Female,35,18,6
8,Female,23,18,94
9,Male,64,19,3
10,Female,30,19,72
11,Male,67,19,14
12,Female,35,19,99
13,Female,58,20,15
14,Female,24,20,77
15,Female,37,20,13
16,Female,22,20,79
17,Male,35,21,35
18,Male,20,21,66
19,Male,52,23,29
20,Female,35,23,98
21,Male,35,24,35
22,Male,25,24,73
23,Female,46,25,5
24,Male,31,25,73
25,Female,54,28,14
26,Male,29,28,82
27,Female,45,29,32
28,Female,35,29,61
29,Female,40,30,31
30,Female,23,30,87

This dataset includes five columns:

  • CustomerID: Unique identifier for each customer.
  • Gender: Gender of the customer (Male/Female).
  • Age: Age of the customer.
  • Annual Income (k$): Annual income of the customer in thousands of dollars.
  • Spending Score (1-100): A score assigned by the mall based on customer spending habits, where 1 is the lowest and 100 is the highest.

Steps:

  1. Data Collection: Gather customer transaction data from the company’s database.
  2. Data Preprocessing: Clean the data by handling missing values, normalizing features, and removing outliers.
  3. Modeling: Apply clustering algorithms like K-Means to segment customers into different groups.
  4. Evaluation: Analyze the clusters to interpret the results and understand the characteristics of each segment.
  5. Application: Use the results to tailor marketing campaigns and product recommendations.

Step 1: Data Preprocessing

import pandas as pd
from sklearn.preprocessing import StandardScaler

# Load the dataset
data = pd.read_csv('customer_data.csv')

# Select relevant features
features = data[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]

# Standardize the features
scaler = StandardScaler()
scaled_features = scaler.fit_transform(features)

Step 2: Applying K-Means Clustering

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

# Determine the optimal number of clusters using the elbow method
inertia = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters=i, random_state=42)
    kmeans.fit(scaled_features)
    inertia.append(kmeans.inertia_)

# Plot the elbow graph
plt.plot(range(1, 11), inertia)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
plt.show()

# Fit the model with the optimal number of clusters (e.g., 4)
kmeans = KMeans(n_clusters=4, random_state=42)
clusters = kmeans.fit_predict(scaled_features)
data['Cluster'] = clusters

Step 3: Analyzing the Clusters

import seaborn as sns

# Visualize the clusters
sns.pairplot(data, hue='Cluster', palette='viridis')
plt.show()

# Analyze each cluster
for i in range(4):
    print(f"Cluster {i}:")
    print(data[data['Cluster'] == i].describe())

Step 4: Applying the Results

After identifying the clusters, you can:

  • Cluster 1: Target high-income, high-spending customers with premium products.
  • Cluster 2: Engage moderate spenders with loyalty programs.
  • Cluster 3: Offer discounts to low-income, low-spending customers to increase their engagement.
  • Cluster 4: Analyze to see if there are unique traits or behaviors that could be capitalized on.

Conclusion

Unsupervised learning, particularly clustering, is highly valuable in situations where you don’t have labeled data but need to discover hidden patterns. In this example, customer segmentation allows for more personalized marketing, which can lead to increased sales and customer satisfaction.

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