Random-Forest-Regressor-Modeling

Detailed exploration of random forest regressors, including data cleaning, model building, and performance evaluation on various datasets.

Contents

• Introduction
• Data Cleaning
• Model Building
• Model Evaluation
• Future Work

Introduction

Random forest regressors are powerful ensemble learning techniques that build multiple decision trees and merge them together to predict continuous outcomes. This repository showcases various aspects of random forest regressors, from data preparation to model evaluation.

Data Cleaning

Data cleaning is a critical step in the machine learning pipeline. In this section, I demonstrate techniques to preprocess and clean datasets to ensure high-quality inputs for the models.

Model Building

This section covers the implementation of random forest regressors, highlighting different approaches and techniques used to build and refine the models.

Model Evaluation

Evaluating the performance of a model is crucial. Here, I use various metrics such as R-squared, mean squared error, and mean absolute error to assess the effectiveness of the random forest models.

Future Work

I plan to expand this repository with more advanced techniques and applications related to random forest regressors, including hyperparameter tuning, feature importance analysis, and comparisons with other ensemble methods.

Thank you for exploring my random forest regressor project. I hope you find it insightful and valuable!