Skip to content
View LuuMyHoa's full-sized avatar
1 follower · 23 following

Block or report LuuMyHoa

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
LuuMyHoa/README.md

Welcome 👋

Pinned Loading

  1. Analyzing-E-Commerce-Traffic-Performance-and-Purchase-Funnel-in-Merchandise-Store-SQL Analyzing-E-Commerce-Traffic-Performance-and-Purchase-Funnel-in-Merchandise-Store-SQL Public

    This project uses BigQuery SQL to analyze Google Analytics e-commerce data in order to evaluate website traffic performance, customer behavior, and the purchase funnel.

  2. Sustainable-Revenue-Growth-and-Market-Strategy-in-Global-Superstore-Power-BI Sustainable-Revenue-Growth-and-Market-Strategy-in-Global-Superstore-Power-BI Public

    An interactive dashboard delivering actionable insights into current performance and supporting strategic decisions on sustainable revenue growth, market expansion, and product strategy.

  3. Identifying-Manufacturing-Inefficiencies-to-Improve-Operational-Efficiency-Power-BI Identifying-Manufacturing-Inefficiencies-to-Improve-Operational-Efficiency-Power-BI Public

    An interactive dashboard for monitoring manufacturing performance and uncovering key operational issues such as production delays, process inefficiencies, and scrap.

  4. E-Wallet-Payment-Performance-and-Transaction-Behavior-Analysis-Python E-Wallet-Payment-Performance-and-Transaction-Behavior-Analysis-Python Public

    EDA and data wrangling on e-wallet data to analyze payment performance, uncover refund patterns, and understand transaction behavior using Python.

    Jupyter Notebook

  5. RFM-Customer-Segmentation-for-Holiday-Campaigns-in-Global-Retail-Python RFM-Customer-Segmentation-for-Holiday-Campaigns-in-Global-Retail-Python Public

    Using RFM analysis in Python to cluster customers for a global retail store. This project helps create targeted marketing campaigns for Christmas and New Year.

    Jupyter Notebook

  6. Predicting-Customer-Churn-and-Identifying-Retention-Opportunities-in-E-Commerce-Machine-Learning Predicting-Customer-Churn-and-Identifying-Retention-Opportunities-in-E-Commerce-Machine-Learning Public

    Conducted EDA, applied supervised models (Logistic Regression, Random Forest) to predict at-risk customers, and used KMeans clustering to segment churned users and uncover key patterns.