https://github.com/ahmad-ali-rafique/decision-tree-classifier-modeling

👏Comprehensive exploration of decision tree classifiers, including data cleaning, model building🏩, and performance evaluation on various datasets.
https://github.com/ahmad-ali-rafique/decision-tree-classifier-modeling

analytics classification classification-models data data-science dataanalytics datacleaning dataset decision-tree-classifier models

Last synced: about 2 months ago
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👏Comprehensive exploration of decision tree classifiers, including data cleaning, model building🏩, and performance evaluation on various datasets.

• Host: GitHub
• URL: https://github.com/ahmad-ali-rafique/decision-tree-classifier-modeling
• Owner: Ahmad-Ali-Rafique
• Created: 2024-05-19T14:47:55.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-19T15:01:15.000Z (about 2 years ago)
• Last Synced: 2025-01-16T04:22:03.291Z (over 1 year ago)
• Topics: analytics, classification, classification-models, data, data-science, dataanalytics, datacleaning, dataset, decision-tree-classifier, models
• Language: Jupyter Notebook
• Homepage:
• Size: 167 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Decision-Tree-Classifier-Modeling

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

## Contents

- [Introduction](#introduction)

- [Data Cleaning](#data-cleaning)

- [Model Building](#model-building)

- [Model Evaluation](#model-evaluation)

- [Future Work](#future-work)

## Introduction

Decision tree classifiers are versatile and interpretable machine learning algorithms. This repository showcases various aspects of decision tree classifiers, 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 classifiers, 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 accuracy, precision, recall, F1-score, and ROC-AUC 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 classifiers, including pruning techniques, feature importance analysis, and ensemble methods.

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