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每个人的生活中都有一个点,那就是该人希望购买或出售房屋。 首先考虑一个人需要买房的情况。 该人将以合理的价格寻找他/她想要的房子。 该人将具有一些决定他/她想要在房子里拥有什么功能。 该人将能够决定他/她所寻找的房屋类型是否物有所值。类似地,考虑一个人需要出售房屋的情况。 通过使用房屋价格预测系统,卖方将能够决定他/她可以在房屋中添加的所有功能,以便可以更高的价格出售房屋。 因此,从以上两种情况中我们可以确认房价预测对买卖双方都有用。本文将有助于基于各种参数来预测房价。 用户将能够输入他们想要购买的房屋类型,并在机器学习的帮助下,房屋价格预测器将显示所需房屋的估计价格。
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House Price Prediction Using Multiple Linear
Regression
Anirudh Kaushal, Achyut Shankar
Department of Computer Science and Engineering, Amity School of Engineering and
Technology, Amity University, Noida, Uttar Pradesh, India
anirudhkushal30@gmail.com, ashankar2711@gmail.com
Abstract. There comes a point in everyone’s life when the person wishes to buy
or sell a house. First consider a scenario where a person needs to buy a house. The
person will look for his/her desired house for a sensible price tag. The person will
have some features decided what he/she wants to have in the house. The person
will be able to decide whether the type of house he/she is looking for is worth of
the price or not.
Similarly, consider a scenario where a person needs to sell a house. By making
use of the house price prediction system, the seller would be able to decide what
all features he/she could add in the house so that the house can be sold for a higher
price. Hence, from both the above scenarios we can confirm that house price
prediction is useful both for the buyer and seller.
This paper will help to predict the house prices based on various parameters. The
users will be able to input the type of house they desire to buy and with the help
of machine learning the house price predictor will display the estimated price of
the desired house.
Keywords: House Price Prediction, Machine Learning, Multiple Linear Regression
1
INTRODUCTION
Usually when people want to buy a house, they look for a house which has a reasonable
cost, and which has all the desired features they want in the house. The house price
prediction will help them to decide whether the house they desire to buy is worth of the
price or not. Similar is the case with people who want to sell the house. By making use
of the house price prediction system, the seller would be able to decide what all features
he/she could add in the house so that the house can be sold for a higher price.
This paper’s objective is predicting house prices on the basis of various parameters.
This will allow the buyer to get an idea of what amount of money he/she has to spend in
order to buy the desired house. It will also allow the seller to get information regarding
what is the house’s real worth and how he/she can maximize the profit gained by selling
the house.
There are many platforms which help the buyers and sellers to predict the price of
the property they are desire and the property they are looking for. Some of them are
MagicBricks and 99acres. They allow the user to enter the locality of the house any-
where in India along with all the other features thus making the house price prediction
system more effective.
Electronic copy available at: https://ssrn.com/abstract=3833734
2 LITERATURE REVIEW
Over the past few years, there have been a lot of studies conducted regarding the anal-
ysis and prediction of house prices. Wilson [7] developed an artificial neural network
which helped in predicting the future trends of house prices in England. Mark and John
[3] developed a regression model which was useful in analyzing house price trends of an
area. Tinghao [5] predicted the real estate prices using auto regressive integrated moving
average model. Zhangming [8] predicted house prices by using back propaga- tion
neural network model. Sampath Kumar and Santhi [4] used multiple linear regres- sion
technique to predict house price of an area, and they also predicted what would be the
increase in price of the land after a period of one year. Kilpatrick [1] stated how and why
the time series regression models are useful for the prediction of house prices. Wang and
Tian [6] made use of the neural networks in order to find out the house price trends. Li
Li and Kai-Hsuan Chuet [2] also used neural networks to predict house prices in Taipei.
Instead of using normal parameters, they used economic parameters in order to make
their house price prediction model.
3 METHODOLOGY AND IMPLEMENTATION
The block diagram given below is the summary of the methodology followed in the
paper.
Fig. 1. Block Diagram
3.1 Data Gathering and Analysis
My procedure can be divided into several stages. The first stage is the data gathering
stage in which I have collected the dataset from the internet. This will be used to train
the machine learning model. The dataset collected in this stage is raw and unstructured
data. There are 546 rows and 12 columns in the dataset. According to the dataset, the
prices are given in Indian rupees and the plot size has been given in square feet. The
price column in the dataset is the dependent variable and the rest of the columns are
independent variables (also called features).
Electronic copy available at: https://ssrn.com/abstract=3833734
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