Real Estate Cost Estimation Through Data Mining Techniques

Real estate is one of the most fast-paced and emerging industries today. Nowadays everyone wants to be the owner of their house rather than live on rent. Therefore, people are very cautious in searching for the most suitable house. Different people have a different budgets and so varies their desire. This paper draws attention to the house rate predictions based on different objectives like financial status and expectations of non-house holders. It consists of two prediction sets, one with all the available features required for buying the house and the other with a few selected features. It involves varying machine learning regression techniques like linear regression, polynomial regression, decision tree, and random forest. Here, all the above techniques are compared, and it is found that polynomial regression with all the features gives the best results.

[1]  Manjusha Pandey,et al.  Segmentation of Nuclei in Microscopy Images Across Varied Experimental Systems , 2020, FICTA.

[2]  Timothy J. Fik,et al.  Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach , 2003 .

[3]  Gurudeo Anand Tularam,et al.  PERFORMANCE OF MULTIPLE LINEAR REGRESSION AND NONLINEAR NEURAL NETWORKS AND FUZZY LOGIC TECHNIQUES IN MODELLING HOUSE PRICES , 2012 .

[4]  Serena Ng,et al.  Tests for Skewness, Kurtosis, and Normality for Time Series Data , 2005 .

[5]  Lilian Berton,et al.  Housing Prices Prediction with a Deep Learning and Random Forest Ensemble , 2019, Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2019).

[6]  Kelvin K. W. Yau,et al.  Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .

[7]  Manjusha Pandey,et al.  A comprehensive survey and analysis of generative models in machine learning , 2020, Comput. Sci. Rev..

[8]  W. Ambrosius,et al.  Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses , 2014, PloS one.

[9]  Wayan Firdaus Mahmudy,et al.  Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study : Malang, East Java, Indonesia , 2017 .

[10]  Malik Abdul Aziz,et al.  Boarding House Renting Price Prediction Using Deep Neural Network Regression on Mobile Apps , 2021, ArXiv.

[11]  Jeremy N. V. Miles,et al.  R Squared, Adjusted R Squared† , 2005 .

[12]  Guy Lebanon,et al.  Linear Regression , 2010 .

[13]  Paddy Crop Disease Prediction- A Transfer Learning Technique , 2020, International Journal of Recent Technology and Engineering.

[14]  Abdulhamit Subasi,et al.  Congestive heart failure detection using random forest classifier , 2016, Comput. Methods Programs Biomed..

[15]  Jacob Benesty,et al.  On the Importance of the Pearson Correlation Coefficient in Noise Reduction , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  Zengxiang Li,et al.  A hybrid regression technique for house prices prediction , 2017, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[17]  Patrick A. Thompson,et al.  Evaluation of the M-competition forecasts via log mean squared error ratio , 1991 .

[18]  Ritesh Sharma.  ECG Classification using Deep Convolutional Neural Networks and Data Analysis , 2020 .

[19]  B. Bharathi,et al.  House Price Prediction Using Machine Learning Algorithms , 2018 .

[20]  Nihar Bhagat,et al.  House Price Forecasting using Data Mining , 2016 .

[21]  Sylvia J. T. Jansen,et al.  Developing a House Price Index for The Netherlands: A Practical Application of Weighted Repeat Sales , 2008 .

[22]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[23]  Gugulothu Narsimha,et al.  Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques , 2013 .

[24]  Abhijit Sarma,et al.  House Price Prediction Using Machine Learning and Neural Networks , 2018, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).

[25]  Siddharth Swarup Rautaray,et al.  Comparative Analysis of Heart Disease Classification Algorithms Using Big Data Analytical Tool , 2020 .

[26]  Mahendra Kumar Gourisaria,et al.  Heart Disease Detection using Core Machine Learning and Deep Learning Techniques: A Comparative Study , 2020 .

[27]  Gerhard Nahler,et al.  Pearson Correlation Coefficient , 2020, Definitions.

[28]  Siddharth Swarup Rautaray,et al.  Prediction of Heart Disease by Mining Frequent Items and Classification Techniques , 2019, 2019 International Conference on Intelligent Computing and Control Systems (ICCS).

[29]  A. Acock.  Working With Missing Values , 2005 .

[30]  Anna Veronika Dorogush,et al.  CatBoost: gradient boosting with categorical features support , 2018, ArXiv.