E-Commerce Customer Segmentation🛒

Overview:

This project focuses on three main components: Database Design and Implementation, Exploratory Data Analysis (EDA), Customer Segmentation using RFM (Recency, Frequency, Monetary) analysis and K-means clustering.

• Database Schema
• SQL Code
• EDA Notebook
• Customer Segmentation Notebook

Tasks Completed:

1. Designing and Implementing a Database:
   • Created an SQL database and populated it using the Brazilian E-Commerce dataset.
   • Implemented triggers to update sequential values within the database.
   • Designed and implemented views and procedures within the database infrastructure, exported the results derived from them to CSV files.
2. Exploratory Data Analysis (EDA):
   • Utilized Matplotlib for creating insightful visualizations.
   • Analyzed online purchasing behaviors and relationships in the Brazilian market.
   • Unraveled the underlying trends and patterns.
3. Customer Segmentation:
   • RFM Analysis:
     • Calculated RFM (Recency, Frequency, Monetary) metrics using SQL queries.
     • Explored the distributions of RFM metrics.
   • K-means Clustering:
     • Determined the optimal number of clusters using the Elbow method.
     • Applied K-means clustering to RFM data.
     • Analyzed segment characteristics.
   • Visualization and conclusion
     • Interpreted results, plotted clusters and assigned meaningful names to them.
     • Summarized findings and drew conclusions from the analysis.