Advanced Analytics and Deep Learning Models. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
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Жанр произведения: Программы
Год издания: 0
isbn: 9781119792413
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       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.

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

      We are in want of a right prediction at the real estate and the housing marketplace discipline. We see a mechanism that runs all through the residence shopping and promoting; buying a house may be a lifetime purpose for maximum of the people. There are lot of individuals making big errors when buying the houses; the majority are shopping for homes from the people they recognise with the aid of seeing the classified ads and everywhere in the grooves coming across the India. One of the not unusual hassles is shopping for the residences, which are too high priced and no longer really worth it [3]. From claiming valuation structures, additional techniques mirror those natures of asset and those conditions that are provided for [8, 9]. The assets would possibly properly, at the manner, alternate in open market underneath many situations and instances; people are unaware about the contemporary conditions and they start losing their cash [10]. The exchange in cost of residences would affect both the common people together with the financial system of country; to avoid such situations, there is a want of rate prediction. Many techniques are to use within the price prediction.

      Authors (Selim, 2009) [12] compared a few studies of artificial neural network deflection using 60% of residential price calculations, and a lot of comparisons have been made by estimating the performance of all their comparisons with different education sizes and choosing statistical lengths.

      Authors (Wu and Brynjolfsson, 2009) [15] from MIT made an estimate of the way Google searches for global loan and income. The author is well aware about the near encounters between them in the fee of houses and the love for much priced houses. Data taken from net seek manner search queries the use of Google procedures and with the assistance of actual countrywide harmony-information gather each present of states.

      The author provides a brief overview of how a random wooded algorithm is use for retrofitting and phase, power boost, and bag loading used as methods. It generates a lot of distinctions, and the difference between lifting and bagging as stated by Liaw et al. (2002) is the successive trees, calculating the weights of the objects and most will take predictions. Throughout the year 2001, Nghiep and Al (2001) proposed a randomized start-up that included fundraising and provided more randomly the entire random planning and postponement process, which is mentioned here in retrospect.

      Eric Slone et al. (2014) improved the relationship among the various home factors and the number of residential queries analyzed using a simple linear regression and multiple linear regressions using a standard square method. Home square images have been used as descriptive variables in simple queues, and multi-line retouches include an increase in the measurement of the parcel of land, number of bedrooms, year of construction, and more descriptive.

      2.3.1 Methodology

      2.3.2 Work Flow

Schematic illustration of flow of work.

      Figure 2.1 Flow of work.

      2.3.3 The Dataset

Column name Description
Area type The kind of area the flat/plot is in.

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