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House Price Prediction using Machine Learning Algorithms



House Prices depend on various factors such as location, features of the house, construction and so on. To factor all these aspects and predict an accurate price of the house can be very tricky. The following paper proposes a system that considers every aspect of a house that effects its pricing and gives an accurate prediction of the house. The system accomplishes this utilising Gradient Boosting, Random Forest, and Decision Tree algorithms. A real time dataset with complete information regarding every single house in the area is used to help give an accurate prediction and minimize the error in the system. The system can be optimized without making changes in the core of it. The system is flexible and not solely dependent on one algorithm but on different algorithms. 

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