Summary of An Optimal House Price Prediction Algorithm: Xgboost, by Hemlata Sharma et al.
An Optimal House Price Prediction Algorithm: XGBoost
by Hemlata Sharma, Hitesh Harsora, Bayode Ogunleye
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP); Methodology (stat.ME)
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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 The paper proposes a machine learning approach to predict house prices, considering both physical attributes of a property and its surrounding neighborhood. The authors use various regression algorithms to develop a predictive model, comparing the performance of support vector regressor, random forest regressor, XGBoost, multilayer perceptron, and multiple linear regression on the Ames City housing dataset in Iowa, USA. The study identifies key factors influencing housing costs and finds that XGBoost is the most accurate model for house price prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers tried to figure out how to accurately predict house prices. They know that a house’s value isn’t just about its physical features, but also about where it’s located. To help real estate developers build homes people can afford, they used special computer programs called machine learning algorithms. These algorithms looked at data from houses in Ames City, Iowa, and tried to guess how much each house was worth. They compared different types of algorithms to see which one worked best. Surprisingly, the XGBoost algorithm did the best job predicting house prices. |
Keywords
* Artificial intelligence * Linear regression * Machine learning * Random forest * Regression * Xgboost
