An Improved Model for House Price/Land Price Prediction using Deep Learning

Introduction

Data Science to solve real-time problems like house price/land price prediction. Estimation of the housing price/land price is a crucial issue and decision making is a challenging task while considering the various parameters to predict the house price/land price (Phan, T.D., 2018). The various study (Alfiyatin, A.N., et al., 2017; Feng, Y., & Jones, K., 2015; Lu, S., et al., 2017) proposed to solve this problem like a prediction of house price/land price used machine learning algorithm still there is a gap and uncertainty to design in the probabilistic and methodology (Alfiyatin, A.N., et al., 2017). The prediction of house price/land price will help the users to invest in a property without approaching an agent. It also decreases the risk involved in the estimation of property and misassumptions by both buyers and sellers (Feng, Y., &Jones, K., 2015). The regression methodology is the best way of estimating house prices or land prices but the correct way of estimation is not existing, regression is a methodology that observed the target variable and independent variable. The independent variable gives the prediction, there are various types of regression analysis techniques are existing such as linear, logistic, ridge, lasso, polynomial, and Bayesian. But every regression technique can be classified as three ways such as no. of independent variables and dependent variables (Lu, S., et al., 2017). There is a lack of probabilistic methodology in ridge and lasso regression a greater number of regression types are invented. The multilinear regression is the most appropriate to invent the house price/land price prediction but these models contain the risk involved in the estimation of price, and lack of customer satisfaction. The result displayed that the approach of the issue needs to be successful but still there is looking for comfortable application (Lu, S., et al., 2017). The multilinear regression has the ability to operate predictions (Li, Y., et al., 2016) The various datasets are available in the Kaggle data source (Lu, S., et al., 2017), and huge real estate data are existing but exact prediction not developed so far, in India skandhanshi real estate’s Pvt. Ltd. Announced 1,25,068 constructions agreed on the overall India (Thamarai, M., &Malarvizhi, S. P., 2020). Every advertised contains commercial complex, villas, apartments and also includes the plot for selling and buying (Thamarai, M., & Malarvizhi, S. P.,2020). Several researchers suggest data science and its applications must improve the healthcare industry, business, e-commerce, transportations, security and data science and its applications must be useful in every aspect in home automation. Data Science and its applications improve the lifestyle of human life (Geo, G., et al., 2019). In Indian government of Andhra Pradesh construct the houses and donates the people whose poverty below 2 lakhs rupees as per the Indian currency (Thamarai, M., & Malarvizhi, S. P., 2020). House construction with the rooms to provide to maintenance shops, self-financed business, and restaurants for hotels, where charities and individuals can contact establishment of houses for orphans’ societies and function halls, to celebrate parties and socialgatherings, and many other social activities in the present scenario (Thamarai, M., & Malarvizhi, S. P.,2020). Several organizations start the business to construct the houses, independent house construction is not that easy task for the poor and middle-class peoples (Thamarai, M., &Malarvizhi, S. P., 2020). The current framework of the ML&DL algorithms to estimate the house price needs the internet-based application that provides all specific features that provide the estimation and house price prediction (Piao, Y., et al., 2019). The estimation of house price prediction of the current mechanism of most essential product and works as perthe demands and needs to benefit of society. Mallikarjuna, B., & Reddy, D.A.K., (2019) worked out health care application works on smartphones they developed application runs on Android operating system used IoT devices connected the cloud and access the healthcare data, these types of applications are most needed for society. The house price/ land price prediction framework would establish a shared communication platform, Mallikarjuna, B., et al.,(2013) worked out face detection mechanism by using genetic algorithm, but present most advanced technology startup the real-time applications, Mallikarjuna, B., et al., (2020a) solved the groundwater prediction by using-based binary predictions, groundwater can be estimation and prediction can passible with the SVM.