Real Estate Property Price Estimator Using Machine Learning

Property price estimator is a mobile application by creating a model using machine learning to forecast the sale price of homes in a Bangalore. This model will be built using data on a variety of house features, such as location, size, age, and condition, as well as the sale prices of the houses. In order to commence, we have gathered and prepared the required data so that it is available for analysis. Managing missing or inaccurate numbers and making sure the data is consistent and appropriately reflects the properties and their sale prices may be involved in this. Next, we will perform exploratory data analysis (EDA) to better understand the relationships between the various house features and the sale prices. This involves visualizing the data and identifying patterns and trends that may be useful for building the prediction model. After having a good understanding of the data, next comes building and training the prediction model. This involves selecting and optimizing an appropriate machine learning algorithm, such as linear regression or a decision tree, as well as selecting and engineering relevant features for the model. The model will then be put through its paces using suitable metrics such as R squared and any necessary corrections will be made to increase its accuracy. Users can inquiry price by giving a series of features which describes their ideal house and can get the recommended price. This will be a responsive inquiry system that can greatly improve user's experience.