House Price Prediction Using Hybrid Deep Learning Techniques

The impact of machine learning on the world has been immense and is only growing. Machine learning is also being used to improve health care, detect fraud, predict weather, and even develop autonomously vehicles. Furthermore, house prices have been steadily increasing over the past few years. This has been due to a number of factors, including a strong economy, low interest rates, and a limited supply of housing. As the demand for housing continues to outpace the availability of new homes, the prices of existing homes have increased significantly. This has caused many people to struggle to afford a home; there has been an increase in the cost of living in recent years. The goal of this paper is to use machine learning as a powerful tool for predicting the future value of a house. It can be used to predict the price of a house given certain features such as size, location, and amenities. We have used machine learning algorithms such as support vector machines (SVM) models, regression models, random forest, and bagging and boosting models to predict house prices. Hyperparameter tuning is also being used to optimize the model performance. As a result, we have compared and analyzed a number of prediction methods in order to select the most suitable one. House prediction using machine learning can be used to estimate the future market value of a house, identify potential investment opportunities, and assist in making informed decisions about buying and selling properties. In Sect. 1, we have given introduction about the real estate industry and how machine learning can be helpful for predicting house prices. In Sect. 2, we have reviewed several papers to gather information to compare result of different models. Sections 3, 4 and 5 are about methodology and implementation of various algorithms to get the desired results. In Sect. 6, we have compared various models and found the desired algorithm for this research paper.