Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
Document Type
Event
Start Date
25-4-2024 4:00 PM
Description
Predicting house prices is a challenging task that researchers from various fields (economics, statistics, politics, etc.) have attempted to answer. An accurate house prediction is useful not only to policymakers to improve their policies, but also to help sellers and buyers in the real estate market make well- informed decisions. Commonly, prediction models are trained on the whole dataset. However, as Azimlu et al [1] suggested, such models might not perform very well on dispersed data. They propose a new approach which first divides the whole dataset into smaller clusters, and then each cluster would be trained with an appropriate machine learning algorithm. It is approved to provide a more accurate prediction.
Included in
GMR-47 A Two-Stage Prediction Model For House Prices
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
Predicting house prices is a challenging task that researchers from various fields (economics, statistics, politics, etc.) have attempted to answer. An accurate house prediction is useful not only to policymakers to improve their policies, but also to help sellers and buyers in the real estate market make well- informed decisions. Commonly, prediction models are trained on the whole dataset. However, as Azimlu et al [1] suggested, such models might not perform very well on dispersed data. They propose a new approach which first divides the whole dataset into smaller clusters, and then each cluster would be trained with an appropriate machine learning algorithm. It is approved to provide a more accurate prediction.