Real Estate Company Beater Price Prediction Using Machine Learning

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creativework.keywordsRandom Forest, DecisionTree, Regressor,Linear Regression, Gradient Boosting Regressor, Multi layer perceptron, price prediction, Python, Sklearn, Pandas, NumPy,Machine learning, Advanced ML Algorithm and Model.
dc.contributor.advisorShazzad Hosain
dc.contributor.authorMd. Nur-E-Azam
dc.contributor.authorMd. Reaz Islam
dc.contributor.authorHridoy Saha
dc.contributor.id1512268042
dc.contributor.id1520663042
dc.contributor.id1611520042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-08-27
dc.date.accessioned2025-08-27T05:06:00Z
dc.date.available2025-08-27T05:06:00Z
dc.date.issued2021
dc.description.abstractThe below document presents the implementation of price prediction project for the real estate markets and housing. Many algorithms are used here to effectively increase the accuracy percentage, various researchers have done this project and implemented the algorithms like hedonic regression, artificial neural networks which is considered as the best models in the price prediction. These are considered as the base models and by the help of advanced data analysis tools algorithms like a random forest, gradient boosted trees, multi layer perceptron and ensemble machine learning models are used and prediction accuracy is attained in a higher rate. The results and evaluation of these models using the machine learning and advanced data analysis tools like pandas will have the more influence in the price prediction.
dc.description.degreeUndergraduate
dc.identifier.cd600000638
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1409
dc.language.isoen
dc.publisherNorth South University
dc.rights© NSU Library
dc.titleReal Estate Company Beater Price Prediction Using Machine Learning
dc.typeThesis
oaire.citation.endPage30
oaire.citation.startPage1
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