International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022
p-ISSN: 2395-0072
www.irjet.net
REAL ESTATE PRICE PREDICTION Yash Sheth1, Sahil Morudkar2, Palak Nayak3, Abhay Patil4 123UG
student, Dept of Information Technology, Mumbai University, Maharashtra, India Assistant Professor, Dept of Information Technology, Mumbai University, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------4
Abstract -The traditional approach of the sales and
marketing goals no longer help the companies to cope up with the pace of the competitive market, as they are carried out with no insights to customers purchasing patterns. Major transformations can be easily seen in the domain of sales and marketing as a result of Machine Learning advancements. Owing to such advancements, various critical aspects such as consumers’ purchase patterns, target audience, and predicting sales for the recent years to come can be easily determined, thus helping the sales team in formulating plans for a boost in their business. The aim of this paper is to predict the price of Real Estates (Houses) in India using some different Machine Learning Algorithms and to see which one has the most accuracy. The buyers are just not concerned about the size(square feet) of the house and there are various other factors that play a key role to decide the price of a house/property. A comprehensive study of sales prediction is done using Machine Learning models such as Linear Regression, Decision Trees Regression, Gradient Boost & Random Forest Regressor.
Key Words: Random Forest Regressor, Regression, Algorithms, Real Estate, Price Prediction, Data mining, Machine Learning. 1. INTRODUCTION Real Estate Property is not only the basic need of a man but today it also represents the richness and prestige of a person. Investment in the real estate generally seems to be profitable because their property values do not decline rapidly. Changes in real estate price can affect various household investors, bankers, policy makers and many. Sales forecasting has always been a very significant area to concentrate upon. Manual infestation of being able to predict House Prices could lead to drastic errors leading to poor management of the organization and most importantly would be time consuming, which is something not desirable in today's expedited world. A major part of the global economy relies upon the business sectors, which are expected to produce appropriate quantities of products to meet the overall needs. The forecasting process can be used for many purposes, including: predicting the future demand of the products or service and predicting how much of the product will be sold in a given period.
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In our paper we have proposed the machine learning algorithms towards the data collected across various property aggregators across India. The objective here is to predict the price of Houses in India using three different algorithms and then comparing them to see which one gives a more accurate result based on some key features gathered from the raw data we have. Accurately predicting house prices can be a daunting task. Analysis and exploration of the collected data has also been done to gain a complete insight of the data. Analysis of the data would help the business organizations to make a probabilistic decision at each important stage of marketing strategy. 1.1 Problem Statement Housing prices in any city are an important reflection of the economy, and housing price ranges are of great interest for both buyers and sellers. Ask any home buyer to describe their dream house, and they probably won’t begin with the height of the basement ceiling or the proximity to an eastwest railroad. But in reality there is much more that influences price negotiations than the number of bedrooms or a white-picket fence. Prices of real estate properties are sophisticatedly linked with our economy. Despite all of this, we do not have accurate measures of housing prices based on the vast amount of data available. Simulation results show that the FLSR provides a superior prediction function as compared to ANN and FIS in capturing the functional relationship between dependent and independent variables and has the lowest computational complexity. Therefore, the goal of this project is to use machine learning algorithms to predict the selling prices of houses based on many economic factors.
2. LITERATURE SURVEY [1] Nihar Bhagat, Ankit Mohokar, Shreyash published in the International Journal of Computer Applications. This work aim towards the forecasting of house prices using Data Mining.
[2] Sunitha Cheriyan, Shaniba Ibrahim, Saju Mohanan,
Susan Treesa published in the year 2018. This study briefly analyzes the concept of sales data and sales forecast to predict the sales of any store using the previous data.
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