國立虎尾科技大學 |

Predicting Political Leanings With Machine Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Predicting Political Leanings With Machine Learning./
作者:	Ghosh, Jay K.
面頁冊數:	1 online resource (110 pages)
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Political science. -
電子資源:	click for full text (PQDT)
ISBN:	9798379696948

Predicting Political Leanings With Machine Learning.
Ghosh, Jay K.

Predicting Political Leanings With Machine Learning. - 1 online resource (110 pages)

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.S.)--University of Colorado at Boulder, 2023.

Includes bibliographical references

This study proposes an alternative approach to predicting political sentiment in the United States by employing machine learning models. Traditional methods of predicting political sentiment have become outdated and require reevaluation. This work utilizes a variety of features, including Google Trends data and Census demographics, to create ensemble models capable of providing more accurate and insightful predictions of political leanings and electoral outcomes. By leveraging the power of machine learning, this research represents a significant innovation over traditional polling and prediction models. The resulting models achieve high accuracy in both training and validation sets, enabling a deeper understanding of political leanings and insights into their dynamics.

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

Mode of access: World Wide Web

ISBN: 9798379696948Subjects--Topical Terms:

558774
Political science.
Subjects--Index Terms:

Census demographicsIndex Terms--Genre/Form:

554714
Electronic books.

Predicting Political Leanings With Machine Learning.
LDR:02102ntm a22003977 4500 001 1142938
005 20240513061029.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379696948
035 $a (MiAaPQ)AAI30425595
035 $a AAI30425595
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Ghosh, Jay K. $3 1467468
245 1 0 $a Predicting Political Leanings With Machine Learning.
264 0 $c 2023
300 $a 1 online resource (110 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: 84-12.
500 $a Advisor: Zietz, Jason.
502 $a Thesis (M.S.)--University of Colorado at Boulder, 2023.
504 $a Includes bibliographical references
520 $a This study proposes an alternative approach to predicting political sentiment in the United States by employing machine learning models. Traditional methods of predicting political sentiment have become outdated and require reevaluation. This work utilizes a variety of features, including Google Trends data and Census demographics, to create ensemble models capable of providing more accurate and insightful predictions of political leanings and electoral outcomes. By leveraging the power of machine learning, this research represents a significant innovation over traditional polling and prediction models. The resulting models achieve high accuracy in both training and validation sets, enabling a deeper understanding of political leanings and insights into their dynamics.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Political science. $3 558774
650 4 $a Computer science. $3 573171
650 4 $a Information science. $3 561178
653 $a Census demographics
653 $a Elections
653 $a Google trends
653 $a Machine learning
653 $a United States
655 7 $a Electronic books. $2 local $3 554714
690 $a 0723
690 $a 0984
690 $a 0615
710 2 $a University of Colorado at Boulder. $b Information Science. $3 1467469
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Masters Abstracts International $g 84-12.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30425595 $z click for full text (PQDT)