This project analyzes the Olist E-Commerce Dataset, which contains nearly 100,000 orders from March 2016 to August 2018. Olist is a Brazilian platform that connects small businesses to major online marketplaces. The goal of this analysis is to gain insights into e-commerce trends in Brazil — from customer behavior and logistics performance to payment methods and seller distribution.
Understand customer purchasing behavior and order frequency.
Analyze delivery performance and identify logistical bottlenecks.
Explore payment methods, installment trends, and revenue distribution.
Detect regional differences across Brazilian states.
Combine SQL for data extraction and Python for deeper analysis and visualization.
SQL (SQLite) — for data cleaning, joining, and aggregation.
Python (Pandas, Matplotlib, Seaborn) — for visualization and statistical analysis.
Jupyter Notebook — for interactive exploration and presentation.
The dataset was originally published by Olist on Kaggle.
It includes multiple tables:
orders — order lifecycle timestamps
customers — customer locations and IDs
order_items — product, seller, and price details
products — product categories and dimensions
sellers — seller IDs and locations
geolocation — coordinates of customers and sellers
payments — payment methods and installments
reviews — customer satisfaction feedback
Average delivery time vs expected delivery time across states.
Comparison of internal handling times between sellers.
Distribution of order values and installment counts.
Top performing states in total sales volume.
Correlation between review scores and delivery delays.
📁 olist-ecommerce-analysis_sql-python │ ├── data/ # Raw and cleaned datasets ├── sql/ # SQL queries used for analysis ├── notebooks/ # Jupyter Notebooks (Python analysis) ├── visuals/ # Charts and visualizations ├── README.md # Project documentation └── requirements.txt # Dependencies
Clone the repository:
git clone https://github.com/alopezmoreira1989/olist-ecommerce-analysis_sql-python.git cd olist-ecommerce-analysis_sql-python
(Optional) Create and activate a virtual environment:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
Install dependencies:
pip install -r requirements.txt
Explore SQL queries or open Jupyter Notebooks to reproduce the analysis.
Automate SQL-to-Python data pipeline.
Build interactive dashboards with Plotly or Streamlit.
Extend the analysis to predict delivery delays using ML models.
This project is released under the MIT License. Feel free to use, modify, and share it with attribution.