E-commerce Customer Segmentation

This project is focused on segmenting e-commerce customers using unsupervised machine learning models, specifically clustering algorithms. I've applied these models to customer data to identify meaningful groups based on customer behavior and preferences. As part of the project, I've also performed exploratory data analysis using SQL to gain deeper insights into the dataset. The project helped me develop the ability to build, evaluate, and maintain unsupervised learning models.

Key Skills Acquired:

Unsupervised Learning: Understanding and applying clustering algorithms (e.g., K-Means, DBSCAN, Hierarchical Clustering) to segment customers based on their behavior.
SQL for Data Analysis: Using SQL to perform exploratory data analysis (EDA) and extract meaningful insights from large datasets, which is essential for working with big data platforms like Google BigQuery or AWS Athena.
Model Evaluation: Evaluating the performance of clustering models through internal validation metrics such as silhouette score and external evaluation techniques like cross-validation or cluster stability tests.
Feature Engineering: Preparing and transforming data to improve clustering performance, including scaling, normalizing, and selecting key features that best represent customer behavior.
Customer Profiling: Analyzing clusters to create actionable customer profiles and personas based on purchasing habits, demographics, and engagement.
Model Maintenance: Understanding how to deploy and monitor unsupervised models over time to ensure their relevance and effectiveness as new data comes in.

Technologies Used:

Python: Data manipulation, clustering model implementation, and evaluation.
SQL: Writing queries for exploratory data analysis and data extraction from large databases.
Scikit-learn: Implementing and evaluating clustering algorithms.
Pandas & NumPy: Data wrangling and manipulation for feature engineering.
Matplotlib / Seaborn: Visualizing customer segments and model performance.