Problem statement: 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.
pip install -r requirements.txt
zenml init
zenml up
visit the ULR Given in the bash
zenml integration install mlflow -y
zenml experiment-tracker register mlflow_tracker --flavor=mlflow
zenml model-deployer register mlflow_tracker --flavor=mlflow
zenml stack register local_with_mlflow -m default -a default -o default -d mlflow
attributeerror: module 'sklearn.metrics' has no attribute 'scorers'
pip install --upgrade mlflow
streamlit run streamlit_app.py