Mall Customer Segmentation

Project Overview

This project focuses on segmenting mall customers into distinct groups based on their demographic and behavioral data. Using unsupervised machine learning techniques, specifically K-Means clustering, the goal is to identify hidden patterns and create customer segments. This is a fundamental task in marketing and business strategy, as it allows for targeted campaigns and personalized customer experiences.

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Technical Highlights

• Dataset: Kaggle - Customer Segmentation Tutorial in Python
• Size: 200 entries, 5 columns
• Key Features:
  • Gender, Age, Annual Income (k$), Spending Score (1-100). The CustomerID column is dropped as it is a unique identifier.
• Approach:
  • Data Cleaning: The dataset was clean with no missing values or duplicates.
  • Exploratory Data Analysis: Histograms and boxplots were used to visualize the distribution of numerical features. A heatmap was generated to show the correlation between these features.
  • Label Encoding: Applied to the Gender column to convert it into a numerical format.
  • Unsupervised Learning: The K-Means clustering algorithm was used for segmentation. The optimal number of clusters was determined using the Elbow Method by plotting the inertia for a range of clusters.
  • Segmentation Scenarios: The model was applied to three different feature combinations to explore various customer segments:
    1. Age and Spending Score: Identified 4 distinct clusters.
    2. Annual Income and Spending Score: Identified 5 distinct clusters, which is a classic and highly interpretable segmentation for marketing.
    3. Age, Annual Income, and Spending Score: Identified 6 clusters in a 3D space, showing more nuanced segmentation.
• Key Findings:
  • The segmentation based on Annual Income and Spending Score is particularly insightful for creating marketing personas, revealing groups like "high-income, high-spending" and "low-income, low-spending" customers.
  • The visualizations clearly show the distinct clusters formed by the algorithm.

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Purpose and Applications

• Targeted Marketing: Enables businesses to design specific marketing campaigns for each customer segment, increasing their effectiveness.
• Personalized Experiences: Helps in tailoring product recommendations, offers, and services to different customer groups.
• Business Strategy: Provides a data-driven approach to understand the customer base and inform business strategy.
• Retail Management: Assists in optimizing store layouts, product assortments, and promotions for different customer segments.

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Installation

Clone the repository:

git clone https://github.com/BhaveshBhakta/Mall-Customer-Segmentation-Using-Kmeans.git
cd Mall-Customer-Segmentation-Using-Kmeans

Install the necessary libraries:

pip install pandas numpy seaborn matplotlib scikit-learn

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Collaboration

We welcome contributions to improve the project. You can help by:

• Exploring other clustering algorithms like DBSCAN or Hierarchical Clustering to see if they produce different or more meaningful segments.
• Performing a more in-depth analysis of each cluster to describe the characteristics of the customer segments in detail.
• Investigating the impact of scaling on the clustering results, especially when using features with different units and scales.
• Adding a pipeline for new customer data to be automatically classified into one of the existing segments.