House Price Prediction using Machine Learning Algorithm

Sharma, Shailendra; Arora, Deepti; Shankar, Gori; Sharma, Priyanka; Motwani, Vihaan

doi:10.1109/iccmc56507.2023.10084197

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…In this example, a forecast for the parameter "number of services" is made based on other parameters, such as "paracetamol sales", "positive feedback about the service", "negative feedback about the service", "positive feedback on paracetamol", and "negative feed-back on paracetamol", using the HistGradientBoostingRegressor model (Sharma, S. et al, 2023). After training this model on the data, a forecast for each of the parameters "paracetamol sales", "positive feedback about the service", "negative feedback about the service", "positive feedback on paracetamol", and "negative feedback on paracetamol" is made using the XGBoost meth-od for 10 days in the future.…”

Section: Resultsmentioning

confidence: 99%

Sustainable Information System for Enhancing Virtual Company Resilience Through Machine Learning in Smart City Socio-Economic Scenarios

Lipianina-Honcharenko,

Komar,

Melnyk

et al. 2024

Economics

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This paper introduces an innovative framework for the management of virtual companies in smart urban environments, with an emphasis on socio-economic resilience facilitated by Sustainable Information Systems. The system aims to equip virtual enterprises in smart cities with tools for robust operations amid socio-economic challenges. Its effectiveness is evidenced by improvements in investment risk assessment, business process simulation, and HR project management, enhancing efficiency and foresight. A key feature is predictive analytics for crisis demand forecasting, enabling swift market adjustments and strategic inventory management. It also helps identify alternative clients and suppliers, ensuring business continuity. Integrating machine learning and augmented reality, the system supports automation and strategic decision-making, significantly benefiting the e-commerce sector by addressing fluctuating demand, supply chain issues, and market adaptations during crises. The Sustainable Information System for Virtual Company Management in Smart Cities offers crucial support for e-businesses facing these socio-economic challenges, facilitating their navigation through turbulent times. Its meticulously designed architecture and functionalities make it a powerful instrument for assisting virtual companies in crisis conditions, fostering their sustainable growth within the socio-economic framework of smart urban settings. Comparative studies with existing models underscore this system’s superior efficiency and holistic approach, highlighting its contribution to enhancing the operational efficiency of virtual companies by 95%, reducing the time needed for critical activities like investment risk analysis and business process simulation, and bolstering the socio-economic resilience of smart cities against crises

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Section: Resultsmentioning

confidence: 99%

Sustainable Information System for Enhancing Virtual Company Resilience Through Machine Learning in Smart City Socio-Economic Scenarios

Lipianina-Honcharenko,

Komar,

Melnyk

et al. 2024

Economics

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show abstract

“…These parameters have different influences on performance, and there are certain mutual checks and balances among the parameters. Drawing inspiration from the application of machine learning in house price prediction, 29,30 we employ the linear regression algorithm to strategically weight the selection of T, W, and G. They can be regarded as three features, and Z 0 is the label value. A three-layer neural network is simply built, and the structure is shown in Fig.…”

Section: Analyze and Simulationmentioning

confidence: 99%

Structure simulation and design optimization of high-performance lithium niobate thin film electro-optic modulator

Chen,

Liao,

Yang

et al. 2023

Opt. Eng.

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“…In [20] authors developed house price prediction models using four machine learning techniques-random forest regressor, Histogram gradient boosting regressor, gradient boosting regressor, and linear regression. They have used only one performance measure to evaluate the performance of their proposed models for house price prediction.…”

Section: Related Workmentioning

confidence: 99%

Recommendation of Regression Models for Real Estate Price Prediction using Multi-Criteria Decision Making

Kumar

2023

JCOMSS

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Accurate prediction of real estate prices is an essential task for establishing real estate policies. Even though various regression models for real estate price prediction have been developed so far, selecting the most suitable regression model is a challenging task since the performance of different regression models varies for different accuracy measures. This paper aims to recommend the most suitable regression model for real estate price prediction, considering various performance measures altogether using multi-criteria decision making (MCDM). The evaluation of regression models involves a number of competing accuracy measures; hence, choosing the best regression model for predicting real estate price is modeled as the MCDM problem in the proposed approach. An experimental study is designed using 22 regression models, three MCDM methods, six performance measures, and three real estate price datasets to validate the proposed approach. Experimental outcomes show that Gradient Boosting, Random Forest, and Ridge Regression are recommended as the best regression models based on MCDM ranking. The results of the experimental study show that the proposed MCDM-based strategy can be utilized effectively in real estate industries to choose the best regression model for predicting real estate prices by optimizing several competing accuracy measures.

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House Price Prediction using Machine Learning Algorithm

Cited by 7 publications

References 15 publications

Sustainable Information System for Enhancing Virtual Company Resilience Through Machine Learning in Smart City Socio-Economic Scenarios

Sustainable Information System for Enhancing Virtual Company Resilience Through Machine Learning in Smart City Socio-Economic Scenarios

Structure simulation and design optimization of high-performance lithium niobate thin film electro-optic modulator

Recommendation of Regression Models for Real Estate Price Prediction using Multi-Criteria Decision Making

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