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  Machine Learning Approach to House Price Prediction with Ensemble Model  
  Authors : V Manju; Stephie Sara Philipose; Dr.Radhakrishnan B
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House is a place of residence, accommodation or shelter, determining the price of house is challenging, as it is reliant on a number of features. In this paper we propose a model to predict the house price considering the environmental and housing characteristics. The proposed approach is designed to estimate the price of house and land using a neural network ensemble model. The neural network ensemble model is an architecture designed to process the data parallel with two different mechanisms namely recurrent, to estimate time series data and cascading to estimate the difference or deviation in the data with respect to time. The proposed system takes data like house data, location data, house and location demand etc. The system finds or extracts time related, time and location based price distribution from the input data, during the training process, and test data or evaluation data is evaluated against the trained model. The proposed system offers better accuracy which is more than the current systems.


Published In : IJCSN Journal Volume 8, Issue 3

Date of Publication : June 2019

Pages : 258-261

Figures :04

Tables : 01


V Manju : received her B Tech (CSE) degree from university of Kerala in 2017. She is currently pursuing her Masters in Computer Science & Engineering from KTU. She has published a survey paper on data mining.

Stephy Philipose : is working as an Assistant Professor in computer Science and Engineering Department. Her research interest focuses on Networking and Data security. She has published several papers on image processing.

Dr. Radhakrishnan B : is working as the Head of CSE department. He has more than 14 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining, image mining.


NJOP, Residual Neural Network, Recurrent Neural Network, Meta-learner, Ensemble learning

A base predictor would have its pros and cons, and it might not be able to work on all datasets with the universal superiority. By applying the ensemble techniques, we can strengthen the advantages of the underlying base predictors or models while suppressing the shortcomings of these base models. The ensemble model demonstrates its effectiveness in dealing with datasets with noise and over-fitting problems.


[1] Muhammad Fahmi Mukhlishin, Ragil Saputra, Adi Wibowo, Predicting House Sale Price Using Fuzzy Logic, Artificial Neural Network and K-Nearest Neighbor. [2] Hakan Kus_an *, Osman Aytekin, _Ilker Özdemir, The use of fuzzy logic in predicting house selling price. [3] Gang-Zhi Fan, Seow Eng Ong and Hian Chye Koh, Determinants of House Price: A Decision Tree Approach. [4] Neelam Shinde, Kiran Gawande, Valuation Of House Prices Using Predictive Techniques [5] Bowen Yang, Buyang Cao, Ensemble Learning Based Housing Price Pre-diction Model.