lisstasy/ecommerce_satisfaction_prediction_deployment

This project is made around the data of 100k orders of a Brazilian e-commerce store "Olist". It includes EDA, customer satisfaction prediction, NLP of reviews, clustering and RFM analysis along with the project deployment in Streamlit.

25
/ 100
Experimental

This tool helps e-commerce managers analyze their customer order data to understand satisfaction levels and segment their customer base. You input your historical order information, and it provides insights into customer satisfaction, natural language processing of reviews, and customer clusters (like high-value or at-risk customers). It's designed for anyone managing an online store who wants to improve customer experience and targeted marketing.

No commits in the last 6 months.

Use this if you are an e-commerce manager or analyst looking to transform raw order data into actionable insights for customer satisfaction and segmentation.

Not ideal if you need a real-time analytics dashboard or a tool for predicting future sales volumes rather than customer behavior.

e-commerce-analytics customer-satisfaction customer-segmentation online-retail marketing-strategy
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 3 / 25
Maturity 8 / 25
Community 14 / 25

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Stars

3

Forks

3

Language

Python

License

Last pushed

Jan 31, 2024

Commits (30d)

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