Summary of Predicting Rental Price Of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models, by Tingting Chen and Shijing Si
Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models
by Tingting Chen, Shijing Si
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper uses five traditional machine learning methods (multiple linear regression, ridge regression, lasso regression, decision tree, and random forest) and a Large Language Model (LLM) approach using ChatGPT to predict rental prices of lane houses in Shanghai. The study applies these methods to a public data sample of 2,609 lane house rental transactions in 2021 and compares their results. Random forest achieves the best performance among traditional methods, while the LLM approach shows promising results that surpass traditional methods in terms of R-Squared value. The paper evaluates the models using three performance metrics: mean squared error, mean absolute error, and R-Squared. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to help people who are looking for apartments in Shanghai by finding a way to predict how much they will cost. To do this, it uses six different ways of doing machine learning (some old-fashioned ones like multiple linear regression, and some new-fangled ones using a computer program called ChatGPT). The researchers took data from 2,609 apartment rentals in Shanghai last year and tried all these methods to see which one worked best. They found that some older methods were good at predicting prices, but the new method with ChatGPT was even better! This is important because it helps us understand how we can use computers to make decisions about big things like housing. |
Keywords
» Artificial intelligence » Decision tree » Large language model » Linear regression » Machine learning » Random forest » Regression
