Paper Details

Machine learning has played a major role over the years in image acquisition, spam editing, general speech command, product recommendation and medical analysis. The current machine learning algorithm helps us to improve safety alerts, ensure public safety and improve medical devices. The machine learning program also provides better customer service and safer car systems. In the current paper we talk about predicting future house prices generated by a machine learning algorithm. With the selection of predictive methods we examine and investigate the various predictive methods. We use regression as our model because of its flexible and flexible approach to model selection. Our result shows that our case approach needs to be successful, and has the potential to process forecasts that can be compared to other home-cost cost models. In addition, on the other hand the indicators of housing value, the development of a real estate cost forecast that is often the development of real estate policy plans for sale. This study uses machine learning algorithms as a research method that develops housing value, estimation models. We create a housing cost forecasting model By looking at the models of the machine learning algorithm. At that point we recommend a real estate forecasting model to support a real estate agent or real estate agent for better information based on real estate calculations. Those tests show that the lasso regression algorithm, in terms of accuracy, reliably surpasses other models in the use of housing cost forecasting.

Neural Networking, Machine Learning, Prediction, house price.