Predicting Customer Satisfaction Score with Olist Dataset

Overview

This project aims to predict the customer satisfaction score for the next order or purchase based on historical data using the Brazilian E-Commerce Public Dataset provided by Olist. By leveraging machine learning techniques, we strive to build a robust predictive model that can anticipate customer satisfaction levels and enhance overall service quality.

Problem Statement

For a given customer's historical data, we are tasked with predicting the review score for the next order or purchase. The dataset comprises information on 100,000 orders spanning from 2016 to 2018, gathered from various marketplaces in Brazil. It encompasses diverse dimensions, including order status, price, payment, freight performance, customer location, product attributes, and customer reviews.

Dataset

The Brazilian E-Commerce Public Dataset Olist serves as the foundation for our analysis and model development. This comprehensive dataset offers insights into the intricacies of e-commerce operations, enabling us to extract valuable patterns and trends to inform our predictive model.

Tech Stack

• ML Framework: XGBoost
• Web Framework: Flask
• Experimentation Tracking: MLflow, DVC (Data Version Control)
• Testing Framework: pytest

Python Requirements

Let's jump into the Python packages you need. Within the Python environment of your choice, run:

git clone https://github.com/bot69dude/Custumor_satisfation_mlops.git
pip install -r requirements.txt

Set up the MLflow tracking URI using the provided credentials:

export MLFLOW_TRACKING_URI=https://dagshub.com/bot69dude/Retail_price_optimization.mlflow
export MLFLOW_TRACKING_USERNAME=bot69dude
export MLFLOW_TRACKING_PASSWORD=559b04e28f7af9242d3e209229040403de58f073

Run the following commands:

dvc repro
python app.py

Application Testing:

pytest

Experimentation Tracking

Experimentation tracking for this project is available on Dagshub. You can view detailed experiment logs, metrics, and visualizations to understand the model's performance.

Acknowledgements

We would like to express our gratitude to Olist for providing the invaluable dataset for this project.