📊 Handling Imbalanced Datasets in Environmental, Ecological, and Health Studies

🌍 Introduction

Imbalanced datasets are a prevalent challenge in fields like environmental science, ecology, and health studies, where critical problems often hinge on detecting rare events or minority classes. Examples include identifying endangered species, predicting disease outbreaks, and spotting environmental anomalies. In these cases, the underrepresentation of crucial minority classes presents unique difficulties for data analysis and model accuracy. For instance, misclassifying rare diseases or environmental threats can have significant real-world consequences.

Addressing issues related to imbalanced datasets, such as biased model predictions and poor minority class detection, is essential to improving the reliability of predictions in these areas. This project aims to tackle these challenges and enhance model performance when working with imbalanced data.

🎯 Research Objective

The objective of this project is to develop a structured, step-by-step pipeline to handle imbalanced datasets specifically tailored for environmental, ecological, and health studies. Our approach aims to enhance model performance by focusing on rare event detection for both classification and regression tasks.

🎯 Key Goals

• 🔍 Improve Model Performance: Enhance the accuracy and reliability of minority class detection, especially for rare event prediction.
• 📈 Comprehensive Coverage: Develop a pipeline that supports both classification and regression problems.
• 🛠️ Effective Techniques: Apply various imbalance handling techniques to improve model outcomes.

🗂️ Project Structure

This repository contains:

• 🔄 Data Preprocessing: Preparing data for analysis, including cleaning, normalization, and encoding.
• 📊 Model Selection and Evaluation: Implementing and evaluating different models and metrics to handle imbalanced data.
• ⚙️ Imbalance Handling Techniques: Strategies like oversampling, undersampling, SMOTE, cost-sensitive learning, and more.

🚀 Getting Started

🧰 Prerequisites

• 🐍 Python 3.x
• Required libraries (install via requirements.txt)

⚙️ Installation

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git

2. Navigate to the repository folder:

   cd your-repo-name

3. Install dependencies:

   pip install -r requirements.txt

📐 Usage

1. Load and preprocess the dataset.
2. Follow the pipeline steps to handle imbalances and build a model.
3. Evaluate performance with metrics suited to imbalanced data.

🤝 Contributing

Contributions are welcome! 🎉 Please open issues to discuss improvements, or create a pull request to suggest changes.