RSK World - E-commerce Customer Dataset | Customer Segmentation | Marketing Analytics | Customer Behavior Analysis | RSK World

Customer Segmentation 40 Features Marketing Analytics SQL Queries Python Customer Analytics

This project features a comprehensive E-commerce Customer dataset designed for professional customer segmentation, marketing analytics, and customer behavior analysis. The dataset includes 100 customer records with 40 features including purchase history, browsing patterns, customer lifetime value (CLV), loyalty tiers, payment methods, geographic regions, and customer segmentation labels. Includes powerful Python scripts: analyze_customers.py for data analysis, customer_segmentation.py for clustering analysis, visualize_data.py for visualization, and generate_enhanced_dataset.py for dataset generation. Also includes 50 SQL queries for data analysis. The package includes interactive demo website, comprehensive README.md, and MIT License. Perfect for data scientists, researchers, students, and developers working on customer analytics, recommendation systems, marketing analytics, and customer segmentation projects.

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Dataset Overview

Complete e-commerce customer dataset with purchase history, browsing patterns, customer segmentation labels, and 40 features for customer analytics.

Customer records with comprehensive features
Purchase history (frequency, order values, totals)
Customer segmentation labels (High/Medium/Low Value)
100 customer records with 40 features
Customer Lifetime Value (CLV) included
Loyalty tiers and payment methods
Pre-labeled customer segments
Ready for clustering and segmentation models
Advanced features (27 additional features)
Multiple analysis scripts included
Perfect for customer analytics & marketing

Dataset Structure & Files

Well-organized project structure with customer data, Python scripts for analysis and segmentation, SQL queries, and visualization tools.

ecommerce_customers.csv - Main dataset file
analyze_customers.py - Comprehensive data analysis script
customer_segmentation.py - Clustering analysis script
visualize_data.py - Data visualization script
generate_enhanced_dataset.py - Dataset generation script
test_dataset.py - Dataset testing script
queries.sql - 50 SQL queries for analysis
index.html - Interactive demo website
README.md - Comprehensive documentation
Consistent naming convention
Easy to load with pandas
Scikit-learn ready format for clustering

Customer Segmentation & Analysis

Complete analysis pipeline with support for customer segmentation, clustering models, and marketing analytics.

K-Means clustering for segmentation
Customer segmentation analysis
Customer Lifetime Value (CLV) calculation
Loyalty tier analysis
Scikit-learn compatibility
Advanced feature analysis
Payment method preferences
Geographic region analysis
Marketing metrics analysis
Customer behavior patterns
Performance metrics
Segmentation examples
Customer analytics utilities

Multiple File Formats

Dataset available in CSV format for maximum compatibility with different data science tools and ML frameworks.

CSV format (comma-separated values)
Excel compatible
Pandas DataFrame ready
NumPy array compatible
Scikit-learn compatible
XGBoost/LightGBM ready
Standard data science formats
Easy to import and process
Compatible with all ML libraries
Jupyter Notebook ready
Python pandas ready

Analysis & Visualization

Comprehensive analysis tools with visualization capabilities and interactive customer data explorer.

Interactive Customer Data Explorer
Customer segment distribution charts
Purchase behavior visualization
Dataset statistics
Feature importance analysis
Customer segment filtering
Performance benchmarking
Segmentation evaluation metrics
Customer analytics visualization
Dataset analysis tools
Interactive Demo Website

Compatible Frameworks

Works with all major data science and analytics frameworks out of the box.

Scikit-learn ML library
K-Means clustering
Customer segmentation algorithms
NumPy numerical computing
pandas data manipulation
matplotlib visualization
seaborn statistical visualization
SQL database support
Jupyter Notebook support
Customer analytics models
Marketing analytics tools

What You Get

Complete package with all files needed for professional e-commerce customer analytics projects.

Customer records with segmentation labels
Python analysis scripts
analyze_customers.py - Comprehensive data analysis
customer_segmentation.py - Clustering analysis
visualize_data.py - Data visualization
generate_enhanced_dataset.py - Dataset generation
test_dataset.py - Dataset testing
queries.sql - 50 SQL queries
ecommerce_customers.csv - Main dataset (40 features)
Interactive demo website
Complete documentation (README)
MIT License

Interactive Demo Website

Beautiful demo website with customer data explorer, customer analytics dashboard, and comprehensive guide.

Modern animated design
Interactive Customer Data Explorer
Customer Analytics Dashboard
Segmentation Performance Metrics
Filter by customer segments
Customer feature visualization
Segment distribution charts
Dataset statistics display
Step-by-step usage guide
Dark theme with gradients
Fully responsive layout

Python Scripts Included

Professional Python scripts for customer analysis, segmentation, and data visualization.

analyze_customers.py - Analyze customer dataset
customer_segmentation.py - K-Means clustering analysis
visualize_data.py - Comprehensive data visualization
generate_enhanced_dataset.py - Generate enhanced dataset
test_dataset.py - Dataset validation and testing
SQL queries integration
Customer analytics utilities
Dataset verification
Batch processing support
Segmentation utilities
Marketing analytics tools
Complete code examples

Dataset Features

Comprehensive e-commerce customer dataset with customer records and 40 features for analytics.

Customer records - Purchase history, browsing patterns, demographics
Purchase features - Frequency, order values, totals, CLV
Segmentation labels - High/Medium/Low Value customers
100 customer records - Balanced dataset
40 features - 27 additional enhanced features
High-quality customer data
Accurate segmentation labels
Ready for clustering and segmentation
Customer analytics utilities
Easy to extend dataset
Organized project structure

Credits & Acknowledgments

This dataset is provided for educational and research purposes. Core technologies and libraries are credited below.

Python 3.8+ - Programming language (PSF License)
Scikit-learn - Machine learning library (BSD License)
XGBoost - Gradient boosting framework (Apache 2.0)
NumPy - Numerical computing (BSD License)
pandas - Data manipulation (BSD License)
matplotlib - Data Visualization (PSF License)
RSK World - Dataset creator and provider
GitHub Repository - Source code and releases
Author: Molla Samser | Designer: Rima Khatun
MIT License - Free for learning & research

Support & Contact

For commercial use, custom datasets, or integration help, please contact us.

Email: help@rskworld.in
Phone: +91 93305 39277
Website: RSKWORLD.in
Location: Nutanhat, Mongolkote, West Bengal, India
Author: Molla Samser
Designer & Tester: Rima Khatun
GitHub: Coming Soon
E-commerce Customer Dataset Documentation
Technical Support Available
Custom Dataset Requests Welcome

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E-commerce Customer Dataset

Dataset Overview

Dataset Structure & Files

Customer Segmentation & Analysis

Multiple File Formats

Analysis & Visualization

Compatible Frameworks

What You Get

Interactive Demo Website

Python Scripts Included

Dataset Features

Credits & Acknowledgments

Support & Contact

Featured Content

Additional Sponsored Content

Download Free Source Code

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