House Price Prediction |
Author(s): |
| Om Kharche , G.H.Raisoni College of Engineering, Nagpur; Tanushree Tiwari, G.H.Raisoni College of Engineering, Nagpur; Gunwant Sonkusare, G.H.Raisoni College of Engineering, Nagpur |
Keywords: |
| In Investigating The Accuracy Of House Price Predictions, Statistical Models And Machine Learning Algorithms Were Analyzed. Deeper Insights Were Generated By Applying A Range Of Evaluation Metrics Including Those Metrics. For The Purposes of This Research Study, a Publicly Accessible Kaggle Dataset Was Employed For Algorithmic Validation |
Abstract |
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The importance of predicting house prices precisely cannot be understated in helping homebuyers and real estate agents make well-informed choices. This research paper delves into diverse approaches used to gauge house pricing which consist of statistical models, machine learning algorithms as well as deep learning techniques. Utilizing a Kaggle-borne dataset exhibiting numerous housing costs, our investigation evaluates individual method performance relying on different metrics including but not limited. We juxtapose comprehensive findings concerning every technique employed in order to establish which proves most effective at determining the price tag for houses. The study conducted reveals that the accuracy of predicting house prices can be improved through the use of deep learning models, especially neural networks. This demonstrates their superiority over statistical methodologies and machine learning approaches. |
Other Details |
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Paper ID: IJSRDV11I20246 Published in: Volume : 11, Issue : 2 Publication Date: 01/05/2023 Page(s): 326-327 |
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