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Random Forest is an ensemble machine learning algorithm used for classification and regression. It creates a collection of decision trees and combines their results to make a final prediction. The algorithm randomly selects features and samples, creating diverse trees that help reduce overfitting and improve the overall accuracy of the model.
This repository contains a Python implementation of the Random Forest Regressor and Classifier. Random Forest is an ensemble learning method that combines multiple decision trees to make predictions. It is a powerful and widely used machine learning algorithm that can be applied to both regression and classification tasks.
The Loan Prediction project aims to determine whether a loan should be approved or rejected by considering various factors. It uses various machine learning algorithms to reach out the best result.