Intelligent E-commerce Analysis and Prediction System Based on the Olist Dataset

This project is based on the public dataset of Brazil's Olist e-commerce platform. It uses Python, Natural Language Processing (NLP), and machine learning technologies to build an intelligent e-commerce analysis and prediction system, covering four core modules: order analysis, comment sentiment analysis, quality analysis, and sales prediction. It aims to help merchants and platform operators make data-driven decisions, while providing a complete practical case reference for e-commerce data analysis learners.

Project Background

In today's booming e-commerce era, data-driven decision-making has become key for e-commerce platforms to maintain competitiveness. As one of Brazil's largest e-commerce platforms, Olist connects a large number of sellers and consumers. This project builds an analysis system based on its public dataset to facilitate business understanding and trend prediction.

Dataset Overview

The Olist dataset is a well-known public dataset in Brazil's e-commerce field. It contains multi-dimensional real transaction data such as order information, product details, seller information, and customer reviews. Its time span covers multiple years, supporting time series analysis and trend prediction.

The project includes four modules implemented via Jupyter Notebooks:

1. Order Analysis Module (ordini.ipynb)：Analyzes order lifecycle, time/amount distribution, regional hotspots, etc., to optimize inventory and logistics;
2. Comment Sentiment Analysis Module (recensioni.ipynb)：Uses NLP technology for sentiment polarity classification and topic extraction to track customer satisfaction;
3. Quality Analysis Module (qualita.ipynb)：Evaluates product/service quality and analyzes the relationship between quality and return rate;
4. Sales Prediction Module (previsione.ipynb)：Uses random forest model to perform time series prediction based on historical data, supporting multiple time granularities.

The project uses mainstream tools from the Python ecosystem:

• Data processing: Pandas
• Numerical computation: NumPy
• Machine learning: Scikit-learn (random forest, etc.)
• Natural language processing: NLP libraries
• Visualization: Matplotlib, Seaborn
• Interactive environment: Jupyter Notebook

Platform Operators

Monitor business health, identify problems and improvement opportunities, optimize resource allocation and marketing strategies;

Merchants

Understand product market performance, improve products and services based on reviews, formulate data-driven inventory and pricing strategies;

Data Analysts

Serve as an entry-level case for e-commerce data analysis, learn the complete machine learning process, understand the transformation of analysis results into business insights.

This project is a high-quality learning resource for e-commerce data analysis:

• Demonstrates the systematic process from raw data to business insights;
• Has clear structure, reasonable modules, and standardized code, which can be used as a reference template;
• Provides learners with real business scenario processing experience, complete modeling process, and practical application cases of NLP and time series prediction.

Data-driven decision-making is reshaping the operation mode of e-commerce. This project extracts valuable information from massive e-commerce data through systematic analysis methods. Whether for business practice or technical learning, it is an open-source project worth paying attention to.