Disease Outbreak Prediction using Machine Learning

This repository contains code and documentation for predicting disease outbreaks using machine learning techniques. By leveraging historical data, environmental factors, and socio-economic indicators, the project aims to develop predictive models to identify the likelihood and intensity of disease outbreaks in specific regions.

Features

• Data Preprocessing: Handle missing values, normalize data, and engineer features relevant to disease outbreaks.
• Exploratory Data Analysis (EDA): Visualize trends, correlations, and spatial distributions.
• Machine Learning Models: Implement various models including Random Forest, Gradient Boosting, Neural Networks, and more.
• Evaluation Metrics: Assess model performance using metrics like accuracy, precision, recall, F1-score, and AUC-ROC.
• Prediction Visualization: Display predictions on maps and charts for intuitive understanding.

Table of Contents

• Getting Started
• Prerequisites
• Installation
• Usage
• Dataset
• Models
• Results
• Contributing
• Contact Information

Getting Started

Follow the instructions below to set up the project and run the models on your system.

Prerequisites

• Python 3.8+
• pip package manager

Installation

1. Clone the repository:

   git clone https://github.com/your-username/disease-outbreak-prediction.git
   cd disease-outbreak-prediction

2. Create a virtual environment:

   python -m venv env
   source env/bin/activate   # On Windows: env\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Prepare your dataset by placing it in the data/ directory. Ensure it matches the expected format.

2. Run the preprocessing script:

   python preprocess.py

3. Train the machine learning models:

   python train.py

4. Evaluate the models and visualize results:

   python evaluate.py

5. Generate predictions for new data:

   python predict.py --input new_data.csv

Dataset

Supported datasets:

• Heart Disease: Heart Disease Dataset on Kaggle
• Diabetes: Diabetes Dataset on Kaggle
• Parkinson's Disease: Parkinson's Dataset on Kaggle

Models

This project supports various machine learning models, including but not limited to:

• Decision Trees
• Random Forest
• Gradient Boosting (e.g., XGBoost, LightGBM)
• Neural Networks
• Support Vector Machines (SVM)

Hyperparameter tuning and model optimization are included to enhance accuracy.

Results

Evaluation metrics used to assess model performance:

• Accuracy
• Precision
• Recall
• F1-score
• AUC-ROC

Visualization tools display spatial and temporal predictions for better interpretation.

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature-name

3. Make your changes and commit:

   git commit -m "Description of changes"

4. Push to the branch:

   git push origin feature-name

5. Create a pull request.

Contact Information

For questions, feedback, or contributions, please contact Janani at jananiviswa05@gmail.com.