week-3

Week 3: Customer Segmentation and Product Recommendation Engine

Table of Contents

1. Introduction
2. Why Customer Segmentation and Product Recommendation?
3. Workflow Overview
4. Detailed Task Breakdown
   • 4.1. Implement Unsupervised Learning Techniques for Customer Segmentation
   • 4.2. Create a Content-Based Recommendation System
   • 4.3. Optional: Use Optuna for Hyperparameter Optimization
   • 4.4. Optional: Develop a Real-Time Recommendation API
5. Deliverables
6. Submission Guidelines
7. Resources

1. Introduction

Welcome to Week 3 of the AI/ML Development Track. This week, you'll work on customer segmentation using unsupervised learning techniques and develop a content-based recommendation system for products. This will involve clustering methods and similarity measures to recommend products effectively.

2. Why Customer Segmentation and Product Recommendation?

Customer segmentation helps in identifying distinct groups within a customer base, allowing for targeted marketing and personalized experiences. Product recommendation engines enhance user experience by suggesting relevant products, increasing engagement and sales.

3. Workflow Overview

1. Find a customer transaction dataset
2. Implement unsupervised learning techniques for customer segmentation:
   • K-means clustering using Scikit-learn
   • DBSCAN for density-based clustering
3. [Optional] Create a content-based recommendation system:
   • TF-IDF vectorization for product descriptions (Scikit-learn)
   • Cosine similarity for item-item similarity

4. Detailed Task Breakdown

4.1. Find a customer transaction dataset

• Here are some good customer transaction datasets to use for clustering:
  • Ecommerce Data
  • Customer Personality Analysis

4.2. Implement Unsupervised Learning Techniques for Customer Segmentation

• Here are some good Kaggle notebook demos for the above 2 datasets to start with:

  • Fabien Daniel - Customer Segmentation
  • Karnika Kapoor - Customer Segmentation and Clustering

• K-means Clustering

  • Use Scikit-learn to implement K-means clustering.
  • Determine the optimal number of clusters using the elbow method or silhouette score.
  • Guide to K-means Clustering
  • K-means Clustering Documentation

• DBSCAN

  • Apply DBSCAN for density-based clustering.
  • Adjust parameters like epsilon and minimum samples to achieve meaningful clusters.
  • Guide to DBSCAN Clustering (At the bottom)
  • DBSCAN Documentation

4.3. Optional: Create a Content-Based Recommendation System

• Here are some good customer transaction datasets to use for clustering:
• Customer Recommendation (Step 12) (This is a basic recommendation system based on clusters)
• Recommendations using Association Rules

Here are some ideas you could use to make even more personalized suggestions to users based on their previous purchases:

• TF-IDF Vectorization

  • Use TF-IDF to convert product descriptions into numerical vectors.
  • TF-IDF Vectorizer Documentation

• Cosine Similarity

  • Calculate cosine similarity between products to find similar items.
  • Cosine Similarity Documentation

5. Deliverables

• Jupyter notebook with the implementation of customer segmentation and product recommendation pipeline.
• A concise Markdown report discussing your approach, challenges, and results.
• [Optional] Python script for the real-time recommendation API.

6. Submission Guidelines

• Submit your Jupyter notebook as a .ipynb file.
• Submit your report as a .md file.
• [Optional] Submit your API script as a .py file.