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使用机器学习进行房价预测-研究论文
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更新于2023-04-29
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房地产市场是最受关注的定价之一,并且一直在波动。 将机器学习的思想应用于如何以高精度提高和预见成本是主要领域之一。 本文的目标是预测房地产的市场价值。 该系统有助于根据地理变量找到房产的起始价格。 通过打破过去的市场模式和价值范围,以及未来的进步,未来的成本将被预测。 该检查意味着使用决策树回归器预测孟买市的房价。 它将帮助客户将资源置于遗产中,而无需转向经纪人。 这项研究的结果证明决策树回归器的准确率为 89%。
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HOUSE PRICE FORECASTING USING
MACHINE LEARNING
ALISHA KUVALEKAR
DEPT. OF INFORMATION TECHNOLOGY
DATTA MEGHE COLLEGE OF ENGINEERING
AIROLI, INDIA
alishakuvalekar7@gmail.com
SIDHIKA MAHADIK
DEPT. OF INFORMATION TECHNOLOGY
DATTA MEGHE COLLEGE OF ENGINEERING
AIROLI, INDIA
sidhikamahadik@gmail.com
SHIVANI MANCHEWAR
DEPT. OF INFORMATION TECHNOLOGY
DATTA MEGHE COLLEGE OF ENGINEERING
AIROLI, INDIA
shivani.manchewar0998@gmail.com
SHILA JAWALE (GUIDE)
DEPT. OF INFORMATION TECHNOLOGY
DATTA MEGHE COLLEGE OF ENGINEERING
AIROLI, INDIA
shilaph@gmail.com
Abstract
—The real estate market is a standout amongst
the most focused regarding pricing and keeps fluctuating. It
is one of the prime fields to apply the ideas of machine
learning on how to enhance and foresee the costs with high
accuracy. The objective of the paper is the prediction of the
market value of a real estate property. This system helps
find a starting price for a property based on the
geographical variables. By breaking down past market
patterns and value ranges, and coming advancements
future costs will be anticipated. This examination means to
predict house prices in Mumbai city with Decision tree
regressor. It will help clients to put resources into a bequest
without moving towards a broker. The result of this
research proved that the Decision tree regressor gives an
accuracy of 89%.
Keywords—Decision tree regressor, machine
learning.
I. INTRODUCTION
Every single organization in today’s real estate
business is operating fruitfully to achieve a competitive
edge over alternative competitors. There is a need to
simplify the process for a normal human being while
providing the best results. This paper proposes a system
that predicts house prices using a regression machine
learning algorithm. In case you're going to sell a house,
you have to recognize what sticker price to put on it.
What's more, a PC calculation can give you a precise
gauge!. This regression model is built not only for
predicting the price of the house which is ready for sale
but also for houses that are under construction.
Regression is a machine learning apparatus that
encourages you to make expectations by taking in – from
the current measurable information – the connections
between your target parameter and a lot of different
independent parameters. As per this definition, a house's
cost relies upon parameters, for example, the number of
rooms, living region, area, and so forth. On the off
chance that we apply counterfeit figuring out how to
these parameters, we can compute house valuations in a
given land region.
The target feature in this proposed model is the price
of the real estate property and the independent features
are: no. of bedrooms, no.of bathrooms, carpet area,
built-up area, the floor, age of the property, zip code,
latitude and longitude of the property. Other than those of
the mentioned features, which are generally required for
predicting the house prices, we have included two other
features - air quality and crime rate. These features
provide a valuable contribution towards predicting
property prices since the higher values of these features
will lead to a reduction in house prices.
The whole implementation is done using the python
programming language. For the construction of the
predictive model, a Decision tree regressor is used from
the “Scikit-learn” machine learning library. Grid Search
CV helps to find the best max-depth value for
Electronic copy available at: https://ssrn.com/abstract=3565512
weixin_38722874
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