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.

i built this project for predicts the house prices using random forest algorithm

Predicting Survivability of Titanic Passenger

Realiza selecao de Beams com diferentes tecnicas

Using census data with a random forest, we will try to predict whether or not a person makes more than $50,000.

Use given data to create machine learning models to classify the risk level of given loans.

This project uses machine learning models to classify whether a job posting is fraudulent or not.

Supervised machine learning analysis using Random Forest Classifier and Logistic Regression to predict risk levels of given loans.

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.

Udemy Machine Learning project

ExcelR_Assignment---Random-Forests---Assignment---15

Udemy Machine Learning project

Regression with Linear Regression, Decision Trees, KNN and Random Forest.

Environmental Justice Concern Prediction Using Random Forest

Random Forest, Decision tree and Logistic Regression

In this repository, we will investigate various regression models to predict whether an individual being interviewed is misrepresenting their salary.

Developed a machine learning model using the Cleveland Heart Disease dataset to accurately predict heart disease presence in individuals based on 14 medical attributes. Conducted comprehensive data exploration, visualization, model selection, training, hyperparameter tuning, and evaluation. Identified crucial features to aid diagnosis and treatment

I used lending data to create machine learning models that classify the risk level of given loans. Specifically, I compared the performance of the Logistic Regression model and the Random Forest Classifier.

Predicting House Prices using Linear Regression, Random Forest and GBT Regression