Big Data Analyzing Techniques in Mathematical House Price Prediction Model
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Product Description
Aim:
As the house price prediction is vital for both the Ill-being of the public and the economic development, many experts in different research fields have explored and predicted it with the machine-learning strategies. Because the price is susceptible to multiple factors, it is very challenging to obtain the accurate number.
Abstract:
The Chinese house market has been flourishing in the past three decades as an increasingly bigger population moves into cities. To keep the rise of house price within a proper range and acceptable to the public, the government has adopted various approaches to bring the price under control. After intermittent fluctuations, house purchasing has become a hot topic for both the media and the public. It is of great significance to study the change of house price considering all the related factors. Some scientists prefer machine learning. Machine learning is a subject which involves many subjects such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. In this paper, we will discuss the details of the machine learning algorithms and their strengths and weaknesses. In the future, the growing complexity of all the factors that influence house price will cause more and more trouble for scientists pursuing more precise results.
Proposed System:
The machine-learning strategies. The three main approaches which are popular in this field to predict prices are AdaBoost, Linear Regression and KNN. Because the price is susceptible to multiple factors, it is very challenging to obtain the accurate number.
Advantage:
Collect the related information, and transform them into data, so that scientists can use suitable models and algorithms to analyze and evaluate the results in an increasingly accurate way. It has become a hot and vital issue to develop more advanced models and algorithms, which scientists have been working on to overcome for decades.
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