Olist Catalyst

Olist Catalyst is an advanced analytical dashboard designed for sellers on the Olist e-commerce platform. It empowers sellers by transforming raw sales data into actionable insights, enabling them to understand market trends, optimize operations, and make data-driven predictions to boost their business performance.

Core Features

• Interactive Dashboard: A user-friendly interface to visualize key performance indicators (KPIs) such as revenue, orders, and customer activity.
• Business Insights: In-depth analysis of sales, customers, and products, allowing sellers to identify top-performing items and understand purchasing patterns.
• Predictive Analysis: Features sales forecasting to help sellers manage inventory and plan marketing campaigns more effectively.
• RFM Analysis: Customer segmentation based on Recency, Frequency, and Monetary value to enable targeted marketing and improve customer retention.
• Market Basket Analysis: Uncovers associations between products frequently bought together, providing a basis for recommendation systems and product bundling strategies.

Tech Stack

• Backend: Python, FastAPI, Pandas, NumPy, scikit-learn, Prophet
• Frontend: React, Next.js, Chart.js, Tailwind CSS
• Database: PostgreSQL
• Environment: uv for Python package and environment management

Data Source

This project utilizes the publicly available Olist E-commerce Dataset from Kaggle. This comprehensive dataset contains real, anonymized e-commerce data from 2016 to 2018, covering orders, products, customers, and reviews.

You can find the dataset here: Brazilian E-Commerce Public Dataset by Olist

Frontend Template

The user interface and component library for the frontend were built upon the excellent TailAdmin Next.js template.

For more information, visit: https://tailadmin.com

Getting Started with Docker (Recommended)

This project is fully containerized using Docker, which is the recommended way to get started. This method simplifies the setup process to just a few commands.

Prerequisites

1. Docker Desktop: Install it from the official Docker website.
2. Kaggle API Token: The application automatically downloads the required dataset from Kaggle. You need an API token for this.
   • Go to your Kaggle account settings (https://www.kaggle.com/account).
   • Click on Create New API Token. This will download a kaggle.json file.
   • Place this file in your home directory under a .kaggle folder (e.g., ~/.kaggle/kaggle.json on macOS/Linux or C:\Users\<Your-Username>\.kaggle\kaggle.json on Windows).

1. Launch the Application

Clone the repository, navigate into the project directory, and run the following command:

docker-compose up --build

This command will:

• Build the Docker images for the frontend and backend.
• Start containers for the frontend, backend, and a PostgreSQL database.
• The frontend will be available at http://localhost:3000.
• The backend API will be available at http://localhost:8000.

2. Populate the Database

After the containers are up and running, open a new terminal window and run the following command to download the dataset and populate the database:

docker-compose exec backend uv run python backend/populate_db.py

This script will:

• Automatically download the Olist dataset from Kaggle into the data directory.
• Create the necessary table schema.
• Fill the tables with the data from the CSV files.

Once the script finishes, your application will be fully functional with all the necessary data.

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For manual setup without Docker, please refer to older commits of this README.

License

This project is licensed under the MIT License. See the LICENSE file for more details.