🏪 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.

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🎥 Demo: Disease Prediction Web App

Watch the demo of the application in action:

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✨ 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 accuracy, precision, recall, F1-score, and AUC-ROC.
• Prediction Visualization: Display predictions on maps and charts for intuitive understanding.
• ✅ Multilingual Support: Switch between English and Tamil within the web app.
• 🧾 PDF Report Generation: Download medical prediction reports in PDF format.
• 🔤 Tamil Font Integration: Ensures proper rendering of Tamil characters in generated PDFs. 👉 Download Tamil Font - Latha.ttf

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📚 Table of Contents

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

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🚀 Getting Started

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

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📦 Prerequisites

• Python 3.8+
• pip package manager

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🛠️ Installation

Clone the repository:

git clone https://github.com/Janviswa/Disease-outbreak-prediction-using-Machine-Learning.git
cd Disease-outbreak-prediction-using-Machine-Learning

Create a virtual environment:

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

Install the required dependencies:

pip install -r requirements.txt

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▶️ 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

6. Run the Streamlit web application:

   streamlit run app.py

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🗃 Dataset

Supported datasets:

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

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🧠 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)

Includes hyperparameter tuning and model optimization.

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📊 Results

Evaluation metrics used to assess model performance:

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

Visualizations display predictions and insights in spatial and temporal formats.

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🌐 Multilingual Support

Feature	Language
Interface Texts	English, Tamil
PDF Reports	English, Tamil

Tamil fonts are embedded into the PDF reports. If you’re facing any font rendering issues, download the Latha Tamil font here and install it locally.

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✍️ 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.

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📬 Contact Information

For questions, feedback, or collaborations, feel free to reach out:

📧 Email: jananiviswa05@gmail.com 🔗 LinkedIn: linkedin.com/in/janani-v

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