Lee Yih Ven

Predictive Modeling & Classification

Using data to predict risks before they happen.

Who's Likely to Quit - Employee Turnover

Using predictive analytics to identify employees at risk of resigning for proactive retention strategies.

Predicting Depression Risk in Students

Early intervention strategies using predictive modeling to identify students at risk of depression.

Flagging High-Risk Borrowers

Demonstrate how underwriters predict loan default risks using machine learning classification techniques.

Identifying At-Risk Students Early

Predictive modeling to help teachers identify students who need additional academic support.

Saving the Silent Spenders

Identifying at-risk high-value customers using predictive analytics to prevent customer churn.

Predicting Member Retention - Fitness Center

Machine learning models to predict fitness center member retention and reduce churn rates.

Customer Lifetime Value Prediction

Advanced CLV modeling using regression and machine learning techniques for customer value optimization.

Identifying Employee Resignation Risk

HR analytics using predictive models to identify employees at risk of leaving the organization.

Predicting CLV - Car Service Center

Automotive service industry customer value prediction using transaction history and service patterns.

Predicting Customer Response to Discounts

Marketing analytics using classification models to predict customer discount response behavior.

Predicting Voucher Performance - Hardware Shop

Retail promotion effectiveness prediction using historical voucher data and customer segments.

Predicting Student Dropout Risk

Educational analytics using logistic regression to identify students at risk of dropping out.

Predicting High-Revenue Product Categories

E-commerce analytics using ensemble methods to forecast product category performance.

Predicting High-Spending Customers

Customer segmentation and value prediction using RFM analysis and machine learning models.

Predicting ROAS - Fitness Tracker

Marketing ROI prediction for wearable technology using attribution modeling and regression analysis.