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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Published Paper Details:
Paper Title
House Price Prediction Using Machine Learning & Deep Neural Networks
Authors
Mr. Siraparapu Lakshmana Suneel, Prof. K. Venkata Rao
Keywords
Machine Learning, Artificial Neural Networks, Regression Techniques, Multiple Linear Regression, LASSO.
Abstract
House price prediction is a critical area of study in real estate analytics, aiming to accurately estimate property values based on various features. In this study, we assess the performance of Multiple Linear Regression, Lasso Regression, and a Neural Network model for predicting house prices. Multiple Linear Regression provides a basic approach by modelling the linear relationships between house features and prices, while Lasso Regression adds regularization to enhance prediction accuracy and prevent overfitting. However, our findings demonstrate that the Neural Network model, which captures complex non-linear patterns, significantly outperforms the regression models in terms of accuracy. This highlights the potential of Neural Networks to deliver superior predictive performance in real estate price estimation, offering substantial improvements over traditional regression methods.
IJCRT's Publication Details
Unique Identification Number - IJCRT2409284
Paper ID - 269077
Page Number(s) - c526-c534
Pubished in - Volume 12 | Issue 9 | September 2024
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Mr. Siraparapu Lakshmana Suneel, Prof. K. Venkata Rao, "House Price Prediction Using Machine Learning & Deep Neural Networks", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 9, pp.c526-c534, September 2024, Available at :http://www.ijcrt.org/papers/IJCRT2409284.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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