Random Forest is a machine learning algorithm that uses multiple decision trees to make predictions. Each tree is trained on a random subset of the data, which helps improve accuracy and reduce overfitting. The final prediction is made by averaging the results from all the trees, making it more robust than a single decision tree. This technique is widely used for both classification and regression tasks. It can handle large datasets with many features and is effective in identifying important variables. Random Forest is popular in various fields, including finance, healthcare, and marketing, due to its versatility and reliability.