通过机器学习技术分析Airbnb(空气床和早餐)的房源

Xiang Li, Jingxi Liao, Tianchuan Gao
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引用次数: 1

摘要

机器学习是一个广泛的领域,包含多个学科领域,包括数学、计算机科学和数据科学。有些概念,比如深度神经网络,可能很复杂,很难用几个词来解释。本章侧重于基本的方法,如监督学习的分类,聚类和降维,这些方法可以很容易地解释和解释初学者可以接受的方式。本章以伦敦的Airbnb (Air Bed and Breakfast)房源数据为源数据,研究每种机器学习技术的效果。通过使用K-means聚类、主成分分析(PCA)、随机森林等方法从特征中帮助构建分类模型,能够预测分类结果,并提供一些性能度量来测试模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques
Machine learning is a broad field that contains multiple fields of discipline including mathematics, computer science, and data science. Some of the concepts, like deep neural networks, can be complicated and difficult to explain in several words. This chapter focuses on essential methods like classification from supervised learning, clustering, and dimensionality reduction that can be easily interpreted and explained in an acceptable way for beginners. In this chapter, data for Airbnb (Air Bed and Breakfast) listings in London are used as the source data to study the effect of each machine learning technique. By using the K-means clustering, principal component analysis (PCA), random forest, and other methods to help build classification models from the features, it is able to predict the classification results and provide some performance measurements to test the model.
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