Project-Based Learning: CLO4 with Ensemble Learning (Random Forest and XGBoost) on Dry Bean Dataset

Overview

This project focuses on achieving the learning outcomes associated with Course Learning Objective 4 (CLO4) through hands-on experience with ensemble learning methods. Specifically, we will be applying Random Forest and XGBoost algorithms to analyze and predict patterns in the Dry Bean Dataset.

Table of Contents

1. Introduction
2. Dataset
3. Ensemble Learning
4. Random Forest
5. XGBoost
6. Implementation
7. Usage
8. Results
9. Conclusion
10. Contributing
11. License

Introduction

In this project, we aim to deepen our understanding of ensemble learning techniques, particularly Random Forest and XGBoost. Ensemble learning involves combining multiple models to enhance predictive performance and robustness. By working on the Dry Bean Dataset, we will apply these methods to solve a real-world problem related to bean classification.

Dataset

The Dry Bean Dataset is a publicly available dataset containing various features related to different types of dry beans. The dataset is often used for classification tasks, making it suitable for our project. You can find the dataset here.

Ensemble Learning

Ensemble learning is a machine learning paradigm where multiple models are trained and combined to improve overall performance. Two popular ensemble methods we will explore are Random Forest and XGBoost.

Random Forest

Random Forest is an ensemble learning method that constructs a multitude of decision trees during training and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

XGBoost

XGBoost (Extreme Gradient Boosting) is an efficient and scalable implementation of gradient boosting. It is known for its speed and performance and is widely used in machine learning competitions.

Implementation

The project will be implemented using a Jupyter notebook or a Python script, utilizing popular machine learning libraries such as scikit-learn for Random Forest and XGBoost.

Usage

To run the project, follow these steps:

1. Clone the repository: git clone https://github.com/ikhsansdqq/ProjectBasedLearningCLO4-MachineLearning.git
2. Install the required dependencies: pip install -r requirements.txt
3. Open the Jupyter notebook or run the Python script: jupyter notebook or python script.py

Results

The project results will include model performance metrics, visualizations, and insights gained from applying Random Forest and XGBoost on the Dry Bean Dataset.

Conclusion

Through this project, we aim to achieve a comprehensive understanding of ensemble learning methods and their application to real-world datasets. The insights gained will contribute to achieving Course Learning Objective 4.

Contributing

If you'd like to contribute to this project, feel free to open an issue or submit a pull request. Your feedback and contributions are highly appreciated.

License

This project is licensed under the MIT License. Feel free to use and modify the code as per the terms of the license.