Last updated: February 1, 2023
For a given customer's historical data, we are tasked to predict the review score for the next order or purchase. We will be using the Brazilian E-Commerce Public Dataset by Olist. This dataset has information on 100,000 orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allow viewing charges from various dimensions: from order status, price, payment, freight performance to customer location, product attributes, and finally, reviews written by customers. The objective here is to predict the customer satisfaction score for a given order based on features like order status, price, payment, etc. In order to achieve this in a real-world scenario, we will be using ZenML to build a production-ready pipeline to predict the customer satisfaction score for the next order or purchase.
This project structure including the stack and components can be used on occasions you need to construct ML pipelines for tabular data ML problems.
Stack and Components
This project uses the following Stack Components:
- Orchestrator - Local Orchestrator.
- Artifact Store - Local Artifact Store.
- Experiment Tracker - MLflow.
- Model Deployer - MLflow.
The codes to reproduce this project are open-source on GitHub. View the code here.
The detailed write-up of this project is in the following blog.