國立虎尾科技大學 |

Airbnb Valuation : = A Machine Learning Approach.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Airbnb Valuation :/
其他題名:	A Machine Learning Approach.
作者:	Wyatt, Katherine.
面頁冊數:	1 online resource (189 pages)
附註:	Source: Masters Abstracts International, Volume: 85-07.
Contained By:	Masters Abstracts International85-07.
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9798381220087

Airbnb Valuation : = A Machine Learning Approach.
Wyatt, Katherine.

Airbnb Valuation :A Machine Learning Approach. - 1 online resource (189 pages)

Source: Masters Abstracts International, Volume: 85-07.

Thesis (M.S.)--University of Arkansas, 2023.

Includes bibliographical references

This thesis uses a geospatially-enhanced, machine learning approach to investigate variations in rental success on the peer-to-peer property sharing website Airbnb.com. Geographic factors, listing attributes and amenities, customer response metrics, and host attributes are included in decision tree modeling to predict the short-term probability of receiving a review. The most important variables in increasing model accuracy are assessed and variations in the importance of these variables investigated using Shapley values.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381220087Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

Game theoryIndex Terms--Genre/Form:

554714
Electronic books.

Airbnb Valuation : = A Machine Learning Approach.
LDR:01819ntm a22003857 4500 001 1143002
005 20240513061043.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381220087
035 $a (MiAaPQ)AAI30686680
035 $a AAI30686680
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Wyatt, Katherine. $3 1467559
245 1 0 $a Airbnb Valuation : $b A Machine Learning Approach.
264 0 $c 2023
300 $a 1 online resource (189 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-07.
500 $a Advisor: Limp, Fred.
502 $a Thesis (M.S.)--University of Arkansas, 2023.
504 $a Includes bibliographical references
520 $a This thesis uses a geospatially-enhanced, machine learning approach to investigate variations in rental success on the peer-to-peer property sharing website Airbnb.com. Geographic factors, listing attributes and amenities, customer response metrics, and host attributes are included in decision tree modeling to predict the short-term probability of receiving a review. The most important variables in increasing model accuracy are assessed and variations in the importance of these variables investigated using Shapley values.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Information science. $3 561178
650 4 $a Geography. $3 654331
653 $a Game theory
653 $a Geospatial data science
653 $a Machine learning
653 $a Spatiotemporal analysis
655 7 $a Electronic books. $2 local $3 554714
690 $a 0366
690 $a 0723
690 $a 0800
710 2 $a University of Arkansas. $b Geoscience. $3 1467560
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Masters Abstracts International $g 85-07.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30686680 $z click for full text (PQDT)