House price prediction using machine learning. Leveraging machine learning models such as linear regression, random forest, neural networks and XGBoost, these supervised learning models are used to delve into house price forecasting. This research includes random forest and XGBoost, are implemented using Scikit-Learn tools.Data collectionA comprehensive dataset is needed that includes relevant property attributes, such as location. Feature selectionIt's important to select features that are likely to have a significant impact on house prices. Model selectionThere are many machine learning algorithms that can be used for house price prediction, including linear regression, random forest, neural networks, and XGBoost. TrainingThe dataset is split into training and testing sets. The training set is used to teach the algorithm the relationship between the features and the target variable. Model evaluationThe model's performance is evaluated using the testing set to determine its accuracy and reliability. DeploymentThe trained model can be implemented into a real-world application, such as a website or mobile app. BenefitsMachine learning algorithms can help buyers and sellers make better decisions and negotiate better prices.