House Price Prediction Using Random Forest Regression Considering the Nearest In-Demand Location

Machine learning has become more important over the past few years in a variety of fields, including image detection, spam recognition, speech commands, product suggestions, and medical diagnosis. Today’s sophisticated machine learning algorithms are assisting us in enhancing security warnings, enhancing public safety, and advancing medical research. Moreover, machine learning technologies offer safer automotive systems and improved customer service. In this study, we concentrate on applying machine learning techniques to forecast future home prices. We examined and investigated different prediction systems in order to choose the optimal one. Therefore, we selected random forest regression as our model because of its versatility and probabilistic model selection process. Our findings demonstrate the effectiveness of our method for solving the issue and its capacity to generate forecasts that are on par with those from other models for predicting housing costs. The establishment of home value indexes, which in turn promotes real estate policies and plans, is another contribution made by this study. To develop a housing cost prediction model, we used machine learning techniques as a research strategy. To assess the performance of different machine learning algorithm models, including gradient boosting boost, lasso regression, and neural random forest, we tested and compared them. According to our research, the random forest regression method predicts housing costs more accurately than other models on a constant basis. Overall, our study advises making more educated decisions about a home’s valuation with the use of the lasso regression method for sellers or real estate brokers. We can create dependable and accurate housing cost prediction models with the aid of machine learning, which will ultimately help the real estate market as a whole.