Machine learning has played a bigger role than in previous years’ image detection, spam changes, normal conversation orders, product recommendations, medical diagnoses. Current machine learning algorithms help to improve security alarms, public safety and medical improvement and other advantages using machine learning penetration in to several global implementations and their improvement. The machine learning system provides better opportunities customer service and safe car system. Currently A paper on which we talk about future housing price forecasts machine learning algorithms. As for Choose from our comparative and various study hypotheses forecasting methods. We use refined-lasso regression as our model due to the method of adaptation and verification on the model selection. Research shows that we need a problem-solving approach able to succeed and develop assumptions It will be possible to compare the cost of the house with other models. On the other hand, the housing price index and progress forecast of housing costs that tend to make real progress real estate policy schemes. This study uses machine learning The algorithm is used as a research method to develop housing prices predictive models. We create a model to calculate the cost of housing an example of a machine learning algorithm, e.g. gradient-boosting framework, refined-lasso regression and the machine learning based system Execute orders accurately. We recommend building an apartment at that time a cost estimate model to support a home seller or real estate an agent for better information based on house valuations. These tests show that there is a lasso regression algorithm The appearance of accuracy and reliability of other models Implement preliminary housing cost estimates

gradient-boosting framework refined-lasso regression machine learning advanced predicting techniques.