All work
2023 – 2025
Tokyo Airbnb Price Prediction Model
Master’s research evaluating predictive accuracy on Tokyo hospitality pricing trends using hyperparameter-tuned algorithms.
Python
XGBoost
ARIMA
LSTM
Pandas
Scikit-learn
Machine Learning & Data Science
3
Predictive model families
Client
Master’s Research · STIKOM Bali
My role
Lead Researcher & Developer
Timeline
2023 – 2025
Year
2023 – 2025
The problem
Evaluate predictive accuracy on Tokyo hospitality pricing trends using hyperparameter-tuned algorithms.
The solution
Compared tuned XGBoost, ARIMA, and LSTM models using Python, Pandas, and Scikit-learn to assess predictive stability in hospitality pricing.
The result
Published academic research paper demonstrating superior predictive stability of tuned gradient boosting against traditional time-series methods.
Predictive model families
3
Hospitality pricing dataset
Tokyo
Published academic research
Paper
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