House price prediction method with variable selection capability
The invention provides a house price prediction method with variable selection capability, and belongs to the technical field of house price prediction based on a computer model. The problem that an existing house price prediction method lacks variable selection capacity is solved. Comprising the fo...
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| creator | SU MEIHONG HU LIHUA ZHANG JIFU |
| description | The invention provides a house price prediction method with variable selection capability, and belongs to the technical field of house price prediction based on a computer model. The problem that an existing house price prediction method lacks variable selection capacity is solved. Comprising the following steps: house data collection: acquiring house transaction information and house characteristics of each community in a given range to form a data set; house data arrangement and standardization processing: arranging the collected house data; dividing the data set into a feature data set X and a house price data set Y, and performing standardization processing; constructing a regression model based on quantiles: constructing a robust regression model suitable for an abnormal point, and adding a regularization item so as to restrain a regression coefficient; training the model: training the constructed model by using the sorted house data, estimating a regression coefficient, and determining a house character |
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The problem that an existing house price prediction method lacks variable selection capacity is solved. Comprising the following steps: house data collection: acquiring house transaction information and house characteristics of each community in a given range to form a data set; house data arrangement and standardization processing: arranging the collected house data; dividing the data set into a feature data set X and a house price data set Y, and performing standardization processing; constructing a regression model based on quantiles: constructing a robust regression model suitable for an abnormal point, and adding a regularization item so as to restrain a regression coefficient; training the model: training the constructed model by using the sorted house data, estimating a regression coefficient, and determining a house character</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230623&DB=EPODOC&CC=CN&NR=116308441A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25543,76293</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230623&DB=EPODOC&CC=CN&NR=116308441A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SU MEIHONG</creatorcontrib><creatorcontrib>HU LIHUA</creatorcontrib><creatorcontrib>ZHANG JIFU</creatorcontrib><title>House price prediction method with variable selection capability</title><description>The invention provides a house price prediction method with variable selection capability, and belongs to the technical field of house price prediction based on a computer model. 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The problem that an existing house price prediction method lacks variable selection capacity is solved. Comprising the following steps: house data collection: acquiring house transaction information and house characteristics of each community in a given range to form a data set; house data arrangement and standardization processing: arranging the collected house data; dividing the data set into a feature data set X and a house price data set Y, and performing standardization processing; constructing a regression model based on quantiles: constructing a robust regression model suitable for an abnormal point, and adding a regularization item so as to restrain a regression coefficient; training the model: training the constructed model by using the sorted house data, estimating a regression coefficient, and determining a house character</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | House price prediction method with variable selection capability |
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