scholarly journals House Price Prediction using Machine Learning

2021 ◽  
Vol 9 (VIII) ◽  
pp. 687-691
Author(s):  
Millee Samukcham
Keyword(s):  
Machine Learning ◽  
Public Safety ◽  
House Price ◽  
Price Prediction ◽  
To Come ◽  
Market Strategies

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.

scholarly journals A Comparative Study on House Price Prediction

2020 ◽  
Vol 6 (12) ◽  
pp. 103-107
Author(s):  
Akash Dagar and Shreya Kapoor
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
House Price ◽  
Machine Learning Algorithms ◽  
Random Forest Regression ◽  
Image Detection ◽  
Price Prediction ◽  
Normal Speech

Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Due to increase in urbanization, there is an increase in demand for renting houses and purchasing houses. Therefore, to determine a more effective way to calculate house price accurately is the need of the hour. So, an effort has been made to determine the most accurate way of predicting house price by using machine learning algorithms: Multivariable Linear Regression, Decision Tree Regression and Random Forest Regression and it is determined that Multivariable Linear Regression has showed most accuracy and less error.

scholarly journals House Price Prediction using Machine Learning Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 3645-3650
Author(s):  
G. Gayathri Priya
Keyword(s):  
Machine Learning ◽  
Robust Regression ◽  
Polynomial Regression ◽  
Housing Prices ◽  
House Price ◽  
Real Estate Market ◽  
Stochastic Gradient Descent ◽  
Selling Price ◽  
Data Set ◽  
Price Prediction

The real estate market is one of the most price-driven, but it is still affected by volatility. This is one of the main uses of machine learning ideas to improve and predict costs with high precision. As housing prices are fluctuating, People are cautious when trying to buy a new house based on their budget and marketing strategy. The purpose of the paper is to forecast consistent home prices for non-owners based on their financial dispositions and aspirations. The paper involves predictions using various Regression techniques like linear regression, random forest regression, polynomial regression, robust regression, lasso regression, elastic net regression, stochastic gradient descent, svm regression, artificial neural network. On a data set, house price prediction has been done by combining all of the above-mentioned strategies to determine which is the most effective. The purpose of the paper is to assist the seller in accurately estimating the selling price of a house. Physical circumstances, and location, among other things, were all taken into account while determining the cost.

scholarly journals Housing-Price Prediction in Colombia using Machine Learning

2021 ◽  
Author(s):  
MIGUEL ANGEL CORREA MANRIQUE ◽  
Omar Becerra Sierra ◽  
Daniel Otero Gomez ◽  
Henry Laniado ◽  
Rafael Mateus C ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evaluation Studies ◽  
Housing Prices ◽  
House Price ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Price Prediction ◽  
Statistical Indicator ◽  
Real Estate Company

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 prediction in Colombia. Moreover, to measure the accuracy of the proposed models, the Root Mean Squared Logarithmic Error (RMSLE) statistical indicator was used. The best cross validation results obtained were 0.25354±0.00699 for the LightGBM, 0.25296 ±0.00511 for the Bagging Regressor, and 0.25312±0.00559 for the ExtraTree Regressor with Bagging Regressor, and it was not found a statistical difference between their performances.

scholarly journals House Price Prediction Using Linear Regression

2021 ◽  
Vol 9 (10) ◽  
pp. 1811-1815
Author(s):  
Samkit Saraf
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Arima Model ◽  
House Price ◽  
Support Vector ◽  
Network Support ◽  
Price Prediction ◽  
Index Terms

Index Terms: Regression model, House price prediction, machine learning, housing market, Arima model, artificial neural network, support vector machine, random forest, dataset.

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