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ISSN 2063-5346
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A NOVEL APPROACH FOR PREDICTING HOUSE PRICE USING K-NEAREST NEIGHBORS ALGORITHM COMPARING ACCURACY PREDICTION WITH LINEAR REGRESSION

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Z.Sri Sai Swetha, R. Dhanalakshmi
» doi: 10.31838/ecb/2023.12.sa1.430

Abstract

Aim: This work is a general research of artificial intelligence K-Nearest Neighbors algorithm (KNN) and Novel linear regression (LR) for the house price prediction systems to chip away the exactness and the accuracy of house price prediction. Materials and Methods: From Machine Learning, Novel K-Nearest Neighbors algorithm (N=10) and Novel linear regression(N=10) techniques are mimicked by varying the KNN parameter and Novel linear regression to propel the ph. The sample size was calculated using the G power of 80% for two groups and there are 20 samples used in this work. Results: Based on the obtained results KNN has significantly better classification accuracy (67.92%) compared to the Novel Linear Regression (53.46).Statistical significance difference between long short term memory and Novel Artificial neural network was found to be 0.278 (p>0.05) which infers that both groups are insignificant. Conclusion: The K-Nearest Neighbors algorithm produces better results in prediction on house price monitoring to improve the price prediction accuracy than the Novel Linear Regression.

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