Читать книгу Advanced Analytics and Deep Learning Models - Группа авторов - Страница 33

Palak Furia* and Anand Khandare†

Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, India

Abstract

For a long time since the very beginning, a continuous paradigm of selling and buying houses/land has continued to exist. The wealth of a man is often determined by the kind of house he/she buys, but this process had multiple people intermediate. However, with the increase in technology, this barter system has also changed a lot. With PropTech being the new upcoming thing to disrupt in the real estate market, using technology to complete the operations has made buying property very simple. It is seen as part of a digital transformation in the real estate industry, focuses on both the technological and psychological changes of the people involved, and could lead to new functions such as transparency, unprecedented data, statistical data, machine learning, blockchain, and sensors that are part of PropTech.

In India, there are number of websites, which collect the data for properties that are to sell, but there are cases where on different sites price vary for the same apartment, and as a result, there is a lot of obscurity [1, 2]. This project uses machine learning to predict house prices. One heuristic data commonly used in the analysis of housing price deficits is the Bangalore city suburban housing data. Recent analysis has found that prices in that database are highly dependent on size and location. To date, basic algorithms such as linear regression can eliminate errors using both internal and local features. The previous function of forecasting housing prices are basis of retrospective analysis and machine learning [6, 7]. A linear regression model and a decision tree model, using vague assumptions. In addition, a multi-dimensional object model with two training items is used to evaluate house prices where something that predicts the “internal” cost of a house is used, and the non-objective component can count neighbors’ preferences. The aim is to solve the problems of relapse where the target variable is the value and the independent variable region. We have used hot code coding in each of our institutions. The business application of this algorithm is that classified websites can directly use this algorithm to predict the values of new properties that are listed by taking variable input and predicting the correct and appropriate value.

Keywords: Machine learning, clustering algorithm, linear regression, LASSO regression, decision tree, support vector machine, random forest regressor