Brazilian E-Commerce Analytics
Predictive Analytics and Business Intelligence for Brazilian E-commerce (Olist Dataset)
Project Overview
End-to-end data science pipeline showcasing real-world business impact
This project is designed as a full-stack data science pipeline, showcasing my ability to take a real-world business dataset from raw information to actionable insights and deployed solutions.
The dataset comes from the Olist Brazilian E-commerce platform, which is one of the most complete open datasets for online retail. It contains multiple interconnected tables covering customers, orders, sellers, products, payments, logistics, and customer reviews — essentially simulating the data infrastructure of a real online marketplace.
Project Scope
Comprehensive end-to-end data science lifecycle implementation
Data Engineering & Preparation
- Cleaning and integrating multiple relational tables
- Handling missing values, outliers, and data inconsistencies
- Designing SQL pipelines for reproducibility
Exploratory Data Analysis & Visualization
- Rich Tableau and Power BI dashboards for key metrics
- Revenue, delivery delays, seller performance tracking
- Geospatial visualizations across Brazil
Machine Learning & Advanced Analytics
- Delivery delay prediction models
- Customer churn and lifetime value (CLV) modeling
- Demand forecasting for products and categories
- Clustering & segmentation for marketing
- NLP sentiment analysis on customer reviews
Deployment & Full-Stack Integration
- Packaging models into APIs for business applications
- Unified dashboards and predictions integration
- Cloud deployment (AWS/GCP/Heroku)
Why This Project Matters
Real-world application mirroring industry requirements
Business Relevance
Answers questions executives care about: revenue, customer retention, and delivery performance
Complex Data
Multiple messy tables requiring strong data wrangling and integration skills
Technical Breadth
Combines BI tools, machine learning, and NLP in a cohesive solution
Deployment Mindset
Not just models, but usable solutions ready for business implementation
Technical Stack
Technologies and tools powering this comprehensive project
Languages
ML & Analytics
Visualization
Deployment
Project Impact
Demonstrating full-stack data science capabilities
When completed, this project will serve as a showcase of full-stack data science skills, proving my ability to work across data engineering, analytics, machine learning, and business intelligence — all tied back to real-world business impact.
Business Intelligence
Executive-level dashboards and KPI tracking
Predictive Analytics
ML models for operational optimization
Customer Insights
Segmentation and lifetime value modeling