Random forest is a machine learning algorithm that combines the output of multiple decision trees to reach a single result. It is a flexible and easy-to-use algorithm that handles both classification and regression problems. Random forest models are popular because they produce great results most of the time even without hyperparameter tuning. Random forest models are popular because they offer a variety of advantages such as accuracy, efficiency, versatility, and relative ease of use. They can handle large datasets with minimal data transformations and work fine with large datasets also datasets with a higher dimension. Random forest models can handle both classification and regression problems and can build prediction models using random forest regression trees. They are based on ensemble learning, which integrates multiple classifiers to solve a complex issue and increases the model's performance.