| dcterms.abstract | Machine Learning have played an important role in some past
years in image recognition, speech recognition, medical
diagnosis, analyzing big set of data. With the help of machine
learning algorithm, we have enhanced the security measures,
customer services, automatic automobiles systems. Here we
have explored, how predictive models can be very useful for
predicting the sales price of the house on the basis of various
factors. We have analyzed the housing dataset and some of the
learning models. In the previous research based on linear
regression. It has been found that the accuracy was not
certain. In this model, we have used lasso regression to
predict the prices as it has features like Framework able to
adapt and stochastic for selection of models. The results were
impressive as those were able to make a comparison with
other existing house price prediction models. This model
proves to be an improvement of the estates policies. The
research use machine learning methodologies to explore new
scenarios of house price prediction.
In this model, there were few models used like XGBoost, Lasso
regression. These were used because of their order precision
execution. XGBoost also shows that which variable have
important effects on sale price. In that view, we suggest a
house price prediction model that a real estate agent and
buyer can use to get the best deal on basis of different factors
and features of the house. This research exhibits a predicting
model using lasso regression because of its accuracy and
overcoming issue of correlated inputs. | |
