This model for price estimation of houses helps in finding the deviation in price for houses. Prices of house are strongly related with various parameter such as crime rate, location, employment rate and market reach. For estimating we required to collect many other information related to real state for estimating the prices. Over the year there are lot of paper published about the use of traditional machine learning to estimate house price, but they rarely concern about the performance of individual model, but most of them are not focused on performance of each model and ignores the less popular yet complex models. So as a result, this research paper focuses on all the traditional and latest machine learning algorithms along with considering various required parameter to estimate house prices in more effective way. This research paper will provide sufficient study and references for various models to prove their efficiency in estimating house prices based on statistical operations and provide an optimistic method to achieve price estimating model.

House price prediction, Linear regression, Inferential statistic, Machine learning, Ridge regression

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