Decision-Tree-Regressor-Modeling

Comprehensive exploration of decision tree regressors, including data cleaning, model building, and performance evaluation on various datasets.

Contents

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

Introduction

Decision tree regressors are powerful and interpretable machine learning algorithms used for predicting continuous outcomes. This repository showcases various aspects of decision tree 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 decision tree 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 decision tree models.

Future Work

I plan to expand this repository with more advanced techniques and applications related to decision tree regressors, including pruning techniques, feature importance analysis, and ensemble methods.

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