Prediction of Disease Outbreaks Using ML

📌 Project Overview

This project leverages Machine Learning (ML) techniques to predict potential disease outbreaks based on various health and environmental factors. The goal is to provide early warnings and insights to help mitigate the impact of outbreaks.

🚀 Features

• Data Collection & Preprocessing: Gathers real-world datasets for analysis.
• ML Model Training: Uses supervised learning techniques for disease prediction.
• Visualization & Insights: Presents outbreak trends using graphical analysis.
• Deployment: A web-based or API service to provide real-time predictions.

🛠️ Technologies Used

• Python 🐍
• Machine Learning (Scikit-Learn, TensorFlow/PyTorch)
• Pandas & NumPy (Data Manipulation)
• Matplotlib & Seaborn (Data Visualization)
• Flask/Streamlit (For Web Deployment)
• GitHub Actions (For CI/CD)

📂 Project Structure

📂 Prediction-of-Disease-Outbreaks-Using-ML
├── 📄 app.py                  # Main application script
├── 📄 model.py                # Machine Learning model training
├── 📄 data_preprocessing.py   # Data cleaning and feature engineering
├── 📄 requirements.txt        # Required dependencies
├── 📄 README.md               # Project documentation
├── 📂 data/                   # Dataset files
│   ├── train.csv
│   ├── test.csv
├── 📂 models/                 # Saved trained models
├── 📂 notebooks/              # Jupyter notebooks for EDA & experiments
└── 📂 static/                 # CSS, JS, Images (if applicable)

📊 Dataset Details

The dataset includes:

• Epidemiological Data: Past outbreak records, symptoms, transmission modes.
• Environmental Data: Temperature, humidity, pollution levels.
• Demographic Data: Population density, age distribution.

🔮 Machine Learning Approach

1. Data Cleaning & Feature Engineering 📊
2. Exploratory Data Analysis (EDA) 🔍
3. Model Selection & Training 🤖 (Logistic Regression, Decision Trees, Random Forest, Neural Networks)
4. Hyperparameter Tuning 🎯
5. Model Evaluation (Accuracy, Precision, Recall, F1-score) 📈
6. Deployment & Real-time Predictions 🌐

💻 How to Run the Project

1️⃣ Clone the Repository

git clone https://github.com/AnandKumar56/Prediction-of-Disease-Outbreaks-Using-ML.git
cd Prediction-of-Disease-Outbreaks-Using-ML

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Application

python app.py

🚀 Future Enhancements

• Integrate Deep Learning models for improved predictions.
• Implement real-time data ingestion from APIs.
• Develop a mobile-friendly dashboard for visualization.