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Machine Learning Model for House Price Prediction

EasyChair Preprint no. 7089

5 pagesDate: November 26, 2021


Real estate is one of the transparent industry in our ecosystem. Housing prices keeps on changing based on various factors. In the current system, house prices are calculated without the necessary prediction about the future price increase and market trends. Predicting the house prices based on the real factors is one of the main mission of the project. Project mainly involves functioning of a website which accepts customer specifications combining the applications of linear regression algorithm and the results obtained are not the solo determination of one technique, it entails the weighted mean of various techniques which yields minimum number of errors and maximum accuracy than the algorithm which is applied individually. Sale prices for the homes in Banglore are predicted by applying various machine learning techniques. The dataset consists of the information about number of floors, square feet, location, and rooms.Representation of the data is done with the help of Python libray, Regression techniques such as Support vector regression and multiple linear regression are used here to build a predictive model comparing them on the various metrics such as R-Squared Value , Mean Squared Error(MSE),Mean Absolute Error(MAE) , Root Mean Squared Error(RMSE) .Here, the main goal is to build a model that evaluates the house prices based on various factors.

Keyphrases: Houseprice, MultipleLinearRegression, Support Vector Regression

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {V Impana},
  title = {Machine Learning Model for House Price Prediction},
  howpublished = {EasyChair Preprint no. 7089},

  year = {EasyChair, 2021}}
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