Housing Price Prediction Model using Machine Learning Algorithm- Lasso Regression
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.
CCS Concepts -
Information systems → Data Management systems →Graph based database models
Keywords - Lasso Regression, Gradient Boosting, Prediction Model