Prediction of House Pricing using Machine Learning with Python

YEAR : 2022


People are careful when they are trying to buy a new house with their budgets and market strategies. The objective of the project is to forecast the coherent house prices for non-house holders based on their financial provisions and their aspirations. By analyzing the foregoing merchandise, fare ranges and also forewarns developments, speculated prices will be estimated. The project involves predictions using different Regression techniques like LASSO regression, Gradient boosting Regression. House price prediction on a data set has been done by using all the above mentioned techniques to find out the best among them. The motive of this project is to help the seller to estimate the selling cost of a house perfectly and to help people to predict the exact time slap to accumulate a house. Some of the related factors that impact the cost were also taken into considerations such as physical conditions, concept and location etc.


System Requirements

Operating System : Windows 7,8,10 (64 bit)
Software : Python
Tools : Anaconda (Jupyter notebook and anaconda prompt)


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