HOUSING PRICE FORECASTING USING MACHINE LEARNING ALGORITHMS (IN CASE OF REAL STATES IN BANGALORE CITY)

As we know Bangalore is a silicon valley of India and Because of fastest economic growth and rising of real estate's properties year to year exclude the last consecutive two year. Folks are searching a housing price to rent, sell and buy a new house tend to be more sticking with their budgets and house market strategies. most of the existing researches are only showing the house prices while not much concerning about the prediction of housing price trends in specific working area; mostly of in this survey research paper, researcher's key emphasis is on both housing price forecasting and machine learning algorithm technique; besides the paper is identifying best house price forecasting model can be done by analysing different machine algorithm model. this survey also Identify the trends of housing forecasting price of published papers and to distinguish the modern leading supervised learning algorithms. Specifically, the current researcher is trying to create predictive machine learning algorithm models for classification and prediction of housing property prices namely support vector machine, linear regression, Random Forest, one-way ANOVA and Decision Tree algorithm models. Additionally, The house price property dataset has variable with many unknown values, the researcher does use rmse and R-squared is to measure the model performance of housing forecasting price. finally, researcher also choice appropriate algorithm model as a final model to forecast the housing price and recommend best selected machine learning model. This proposed research outcome will help public stakeholders as well as real estate agents because we want to know how to prepare for the future. The key advantage is that it ensures budget flow tracking, control, and management.