Determining House Price Using Regression

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Economy of the country is greatly driven by the prices of houses in that country. Both buyers and sellers depend on the pricing strategies. Ask an emptor to explain the factors they think are considered for pricing the house at that price and that they probably start with railways and end with various attributes. Over here it proves that more factors will be applied on the pricing strategies of the house. The aim of the project is to predict the house prices with various regression models. Nowadays Machine Learning is a booming technology. Data is the heart of Machine Learning. AI and Machine Learning holds the key position in the technological market. All industries are moving towards automation. So we have considered ML as a main predicting subject in our project and worked using it. These days everything fluctuates. Starting with crypto and various business models varies day by day which includes real estate as well so in this project house prediction depends on real estate data and ML techniques. Many people want to buy a good house within the budget. But the disadvantage is that the present system doesn't calculate the house predictions so well and end up in loss of money. So, the goal of our project is to reduce money loss and buy good house. Many factors are there to be considered in order to predict the house price which includes budget factors and fewer house modifications according to the buyer. So, we are considering all of those factors and predicted using various machine learning techniques like SVR, KNN, SGB regression, CatBoost regression, Random forest regression

Amity Journal of Computational Sciences (AJCS)ISSN: 2456-6616 (Online) 18 www.amity.edu/ajcs , 2020

Machine learning participate a significant role in every single area of technology as per the today's scenario. Even I can Say every phase of our lives is surrounded by the implementation of new era technologies such as Hospitality management, Railway, Transportation, Health care, Industry And so on. Machine learning has been employed for many sectors since past decades like image processing, pattern recognition, medical diagnosis, and predictive analysis, product recommendation. House prices changes every year, so it is mandatory for a structure to foresee house prices in the future. House price prediction can help in fixing and thereby predicting house prices and customer can evaluate it. Our intension is to predict house prices using several machine learning techniques. House price of particular location does depends on various factors like lotsize, bedrooms, bathrooms, location, drawing room, material used in house , interiors, parking area and mainly on square feet per area. Our intension behind proposing this paper is to employ different machine learning techniques for predicting the price based on these metrics. The algorithm used in this analysis is Data refining, OLS regression, Classification, Clustering, correlation matrix.

IJRASET, 2021

With the increase in industrialisation, people are also a lot more careful today when they make an attempt to shop for a brand new house with their budgets and market strategies. Until date, existing websites gift solely the house costs given by the homeowners and details of the house largely infrastructure. Some websites even offer comparison between completely different homes with the same infrastructure. But, some individuals aren't awake to what quantity a house with an exact infrastructure is meant to value and are not ready to find how much is sweet enough to be ready to find frauds. individuals additionally want alternative factors however infrastructure to come to a decision whether or not or to not obtain a house Machine learning algorithmic program helps us in enhancing security alerts, guaranteeing public safety and improve medical enhancements.

House price forecasting is an important topic of real estate. The literature attempts to drive useful knowledge from historical data of property markets. Machine learning techniques are applied to analyze historical property transaction to discover useful models for house buyers and sellers. Revealed is the high discrepancy between house prices in the most expensive and most affordable suburbs. Moreover, experiments demonstrate that the combination of stepwise and support vector machine that is based on mean squared error measurement is a competitive approach. The goal of the study is through analyzing a real historical transactional dataset to derive valuable insight into the housing market. It seeks useful models to predict the value of a house given a set of its characteristics. Effective model could allow home buyers or real estate agents to make better decisions.

2021

Our ecosystem's least transparent industry is real estate.Housing prices fluctuate on a daily basis and are sometimes hyped rather than based on valuation. Our research project's main focus is on predicting housing prices using real-world factors. Our goal is to make evaluations on each and every basic parameter that is properly considered when determining the price. We used multiple linear regression to estimate house prices based on square footage and the number of bedrooms in this paper. The relationship between the mean value of one variable and the values of other variables is measured by regression.Regression analysis is a collection of statistical for calculating theassociation between multivariate in statistical modelling.Multiple variables Explain the association between one uninterrupted Basedon variable (y) and two or more individualistic variables. linear regression (x1, x2,x3...etc). Three modules were used to implement this: The data entry module is used to pro...

International Journal for Research in Applied Science and Engineering Technology, 2022

Economy of the country is greatly driven by the prices of houses in that country. Both buyers and sellers depend on the pricing strategies. Ask an emptor to explain the factors they think are considered for pricing the house at that price and that they probably start with railways and end with various attributes. Over here it proves that more factors will be applied on the pricing strategies of the house. The aim of the project is to predict the house prices with various regression models. Nowadays Machine Learning is a booming technology. Data is the heart of Machine Learning. AI and Machine Learning holds the key position in the technological market. All industries are moving towards automation. So we have considered ML as a main predicting subject in our project and worked using it. These days everything fluctuates. Starting with crypto and various business models varies day by day which includes real estate as well so in this project house prediction depends on real estate data and ML techniques. Many people want to buy a good house within the budget. But the disadvantage is that the present system doesn't calculate the house predictions so well and end up in loss of money. So, the goal of our project is to reduce money loss and buy good house. Many factors are there to be considered in order to predict the house price which includes budget factors and fewer house modifications according to the buyer. So, we are considering all of those factors and predicted using various machine learning techniques like SVR, KNN, SGB regression, CatBoost regression, Random forest regression

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

The housing sector is the second-largest employment provider after the agriculture sector in India and is estimated to grow at 30% over the next decade. Housing is one of the major sectors of real estate and is well complemented by the growth of urban and semi-urban accommodations. Ambiguity among the prices of houses makes it difficult for the buyer to select their dream house. The interest of both buyers and sellers should be satisfied so that they do not overestimate or underestimate the price. Our system provides a decisive housing price prediction model to benefit a buyer and seller or a real estate agent to make a better-informed decision system on multiple features. To achieve this, various features are selected as input from the feature set and various approaches can be taken such as Regression Models or ANN.

IRJET, 2022

Buying a house is one of the biggest financial goal of everyone. Owning a house is not only a basic need but it also represents prestige. However, buying a house is one of the most crucial decision of a person's life as there are so many factors to be consider before buying a property. House prices keeps changing based on location, area, population, house condition and structure, availability of parking, backyard, size of house etc. From past few years a lot of data has been generated regarding Real Estate. Machine learning prediction techniques can be very useful to predict an accurate pricing of the houses. The study focuses on developing an accurate prediction model for house price prediction. Machine learning is sub-branch of artificial intelligence that deals with statistical methods, algorithms. Using machine learning we can build a model which can make prediction based on past data. In this paper we will review different machine learning algorithms which can be used for house pricing prediction.

House price forecasting is an important topic of real estate. The literature attempts to drive useful knowledge from historical data of property markets. Machine learning techniques are applied to analyze historical property transaction to discover useful models for house buyers and sellers. Revealed is the high discrepancy between house prices in the most expensive and most affordable suburbs. Moreover, experiments demonstrate that the combination of stepwise and support vector machine that is based on mean squared error measurement is a competitive approach. The goal of the study is through analyzing a real historical transactional dataset to derive valuable insight into the housing market. It seeks useful models to predict the value of a house given a set of its characteristics. Effective model could allow home buyers or real estate agents to make better decisions.

2021

It is a common practice to price a house without proper evaluation studies being performed for assurance. That is why the purpose of this study provide an explanatory model by establishing parameters for accuracy in interpretation and projection of housing prices. In addition, it is intentioned to establish proper data preprocessing practices in order to increase the accuracy of machine learning algorithms. Indeed, according to our literature review, there are few articles and reports on the use of Machine Learning tools for the prediction of property prices in Colombia. The dataset in which the research is built upon was provided by an existing real estate company. It contains near 940,000 items (housing advertisements) posted on the platform from the year 2018 to 2020. The database was enriched using statistical imputation techniques. Housing prices prediction was performed using Decision Tree Regressors and LightGBM methods, thus deriving in better alternatives for house price pr...