This comprehensive project addresses the issue of customer churn.
In addition to breaking down the business problem, understanding the data, and employing machine learning models for predictions, I've also established key business metric and deployment measures.
I've calculated the precise percentage of the population receiving discounts that could optimize the business objective.
Built a LSTM network using pytorch to predict user ratings on items. Achieved 93% accuracy by adjusting learning rates, epochs, dropout percentages and norm regularization
Learned and evaluated supervised learning models including logistic regression, SVM, and XGBoost on predicting hotel booking cancellation using R. Achieved 85% accuracy and suggested deployment strategies
This dashboard provides a detailed overview of key metrics such as number of flights, cancellation rate, average delays, and a deep dive into each operational aspect.
A fun project learning HTML stucture and using BeautifulSoup to track the price of my desired item on Amazon. System automatically sends email to me when the price is below certain threshold.
When being the product owner of Audi Taiwan LINE official account, I was given an annual award for my success in making the LINE official account the primary portal for communication between the brand and car owners. This article shared my learnings and experinces conducting a user behavior analysis.
Using anonymous credit card data from Taiwan as an example to conduct clustering analysis. Introduce various matrices such as RFM Index, and develop a marketing strategy.