Open AccessCost prediction using gradient descent algorithm
Author(s) -
Chandrashekhar Azad,
Urvin Desai,
P. Abhilash
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1706/1/012038
Subject(s) - computer science , negotiation , renting , sort , class (philosophy) , gradient descent , world wide web , revenue , operations research , advertising , business , database , artificial intelligence , engineering , artificial neural network , civil engineering , political science , law , accounting
In recent days booking through web sites has become very prominent. Almost 70% of bookings or transactions are happening through online. Of all the sales web sites housing websites has become a great platform for sales. Different companies provide different services like selling new house, house rentals, sale of used houses etc. Price matters a lot in any sa les. As houses are one time buy in the life for most of the Indians, they do a lot of research related to price, area and facilities while buying a house. The first thing common middle-class person looks at is the price based on their requirement. Integration of Machine learning algorithm to these web sites or application can be a great advantage. This can reduce the manual effort of sending quotations or negotiations. Presently we see many web sites which give us the details regarding the houses and their prices, but a person must invest more time to search the house which fits his necessities and budget. By integrating a machine learning algorithm into these websites, it helps the user just to sort his necessities and saves his time. In this paper the usage of this algorithm is explained practically and how the functionality can help improve the deliverability of the application is also discussed.