Abstract
This comprehensive study explores advanced applications in Artificial neural networks by integrating memristor-based technology into sophisticated multi-variable regression models for house price prediction. Simultaneously, a novel deep learning model employs public facilities, satellite maps, and attention mechanisms, surpassing traditional methods. In the real estate domain, Google Trends emerges as a tool for predictive analysis, enhancing decision-making. Lasso regression proves crucial in machine learning for adaptable housing price prediction models. The utilization of Linear Regression aids predictions based on bedrooms and amenities, enabling informed customer decisions. Concluding the exploration, a research effort combines regression analysis and particle swarm optimization to predict house prices, achieving a minimal error of IDR 14,186 and providing valuable insights for precise predictions in real estate across diverse locations.
Keywords
House Price Prediction, Machine Learning Algorithm, Real Estate, Heterogeneous Data, Big Data Utilization, Deep Learning, Regression.
Citations
IRE Journals:
Vaishnavi Patil , Akash Vishwakarma , Rohan Ghorpade , Varad Pawale , Prof. Dipali Mane
"Estate Eyes, Predicting Your Dream Home's Value: A Comprehensive Survey" Iconic Research And Engineering Journals Volume 7 Issue 8 2024 Page 119-124
IEEE:
Vaishnavi Patil , Akash Vishwakarma , Rohan Ghorpade , Varad Pawale , Prof. Dipali Mane
"Estate Eyes, Predicting Your Dream Home's Value: A Comprehensive Survey" Iconic Research And Engineering Journals, 7(8)
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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