E-commerce Intelligent Analytics Platform: End-to-End Practice from Data to Decision-Making

This project is an end-to-end e-commerce data analysis platform integrating business intelligence, recommendation systems, customer segmentation, churn prediction, NLP, RAG, and predictive analytics. Based on the Kaggle E-commerce Product Intelligence Dataset (covering 3.5 years of historical data, 10,000 users, 1,000 products, etc.), it extracts business insights using modern AI technologies to provide comprehensive intelligent support for e-commerce decision-making and drive business growth.

Business Background

In a data-driven business environment, extracting insights from massive data is key to the competitiveness of e-commerce platforms.

Dataset Overview

• Source: Kaggle E-commerce Product Intelligence Dataset
• Scale: 3.5 years of historical data, including 10,000 users, 1,000 products, 100,000 interactions, 1,737 transactions, 1,253 reviews, 20 countries, 10 categories
• Core Data Tables:

  Table Name	Description
  Users	Customer demographics and profile information
  Products	Product catalog, categories, pricing, and ratings
  Sessions	User browsing sessions and traffic sources
  Interactions	User-product interaction history
  Purchases	Customer purchase transaction records
  Reviews	Product reviews and ratings

Exploratory Data Analysis

Comprehensive analysis of user behavior, product performance, conversion paths, and other dimensions.

Recommendation Systems

Implemented seven algorithms: user-based collaborative filtering, matrix factorization, content-based filtering, session-based recommendation, sequential recommendation, product similarity recommendation, and popularity recommendation.

Customer Intelligence

• Clustering algorithm for segmentation (4 groups)
• CLV prediction model
• Churn prediction model (key indicator: recency of activity)

NLP Applications

• Review sentiment classification (model accuracy: 100%)
• Semantic product retrieval (TF-IDF + cosine similarity)
• RAG pipeline (Retrieval-Augmented Generation)

Predictive Analytics

Demand/revenue prediction model (Model B: MAE 56.21, RMSE 89.49)

Tech Stack

Python ecosystem: Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, etc.

Revenue and Categories

• Electronics have the highest revenue ($40,300)
• Apparel and accessories have the highest sales volume (392 units)

Traffic and Conversion

• Mobile sessions are the most (11,069 times)
• Organic search has the most conversions (513 transactions)
• Display ads have the highest conversion rate (8.05%)

Customer Segmentation

• A total of 87% of users are in the low-engagement group
• High-value customers have an average consumption of $333.42

Sentiment Analysis

• Positive reviews: 74.3% (931 entries)
• Negative reviews:25.7% (322 entries)

Prediction Results

• Highest monthly revenue: February 2026 ($5,249.97)
• Lowest monthly revenue: September 2025 ($2,725.07)
• Most popular product: 6,777 interactions
• Engagement follows a Pareto distribution

This project demonstrates the end-to-end integration of business analysis, recommendation systems, machine learning, NLP, generative AI, and predictive modeling in e-commerce scenarios. It transforms raw data into actionable insights, providing a comprehensive framework for improving customer experience, enhancing operational efficiency, and optimizing revenue.

Insights for different roles:

• Data science practitioners: An excellent example of an end-to-end project
• E-commerce practitioners: A concrete path for data-driven decision-making
• Tech enthusiasts: Practical application scenarios of various AI technologies

1. Category Strategy: Prioritize promoting high-revenue categories like electronics, and focus on the high-frequency consumption characteristics of apparel and accessories
2. Recommendation Optimization: Improve product discovery efficiency through a multi-algorithm recommendation system
3. Customer Retention: Design exclusive plans for high-value customers, and use CLV analysis to guide investment
4. Channel Investment: Increase investment in high-conversion channels such as organic search and paid search
5. Product Improvement: Use review sentiment analysis to identify quality issues
6. Prediction Application: Integrate prediction models into inventory and operational decisions