Brazilian e-commerce data analysis project using the Olist dataset to uncover insights about delivery times, customer satisfaction, and revenue patterns.

Full analysis and findings available in: OlistDataPresentation.pdf

Source: Olist Brazilian E-Commerce Public Dataset

The dataset contains information about:

• Orders and their status
• Customers and their locations
• Order items and products
• Payments
• Reviews and ratings
• Sellers

olistdata/
├── data/
│   ├── csv/                    # CSV datasets
│   └── olist.zip              # Original dataset archive
├── olistanalyisis.py          # Main analysis script
├── Untitled.ipynb             # Jupyter notebook analysis
└── venv/                      # Python virtual environment

The analysis includes:

1. Data Loading & Merging: Combines multiple datasets (orders, customers, products, payments, reviews, sellers)
2. Data Cleaning: Handles timestamps, calculates delivery times, removes null values
3. Descriptive Statistics: Average delivery time, review scores
4. Visualizations:
   • Delivery time distribution
   • Review score vs delivery time correlation
   • Top 10 states by revenue
5. Key Insights: Customer satisfaction metrics and delivery performance

• Python 3.12
• pandas
• numpy
• matplotlib
• seaborn

# Clone the repository
git clone https://github.com/ignaciogomenuka/olistdata.git
cd olistdata

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install pandas numpy matplotlib seaborn jupyter

python olistanalyisis.py

jupyter notebook Untitled.ipynb

• Delivery Performance: Analysis of average delivery times across different regions
• Customer Satisfaction: Correlation between delivery time and review scores
• Revenue Distribution: Geographic revenue patterns across Brazilian states
• Review Patterns: Percentage of positive reviews (4-5 stars)

The dataset consists of multiple interconnected tables that provide a comprehensive view of the e-commerce operations:

• olist_orders_dataset.csv - Order information
• olist_customers_dataset.csv - Customer data
• olist_order_items_dataset.csv - Items per order
• olist_products_dataset.csv - Product catalog
• olist_order_payments_dataset.csv - Payment details
• olist_order_reviews_dataset.csv - Customer reviews
• olist_sellers_dataset.csv - Seller information
• olist_geolocation_dataset.csv - Geolocation data
• product_category_name_translation.csv - Category translations

Ignacio Muñoz Gomeñuka

This project uses public data from Olist. Please refer to the original dataset terms of use.