🌍 Big Data Architecture for Pandemic Risk Prediction

Welcome to the BigData-Architecture repository! This project focuses on predicting pandemic risk, specifically COVID-19, through data analysis, machine learning modeling, and a real-time dashboard. Our goal is to provide a robust system that helps in understanding and assessing risks associated with pandemics.

Table of Contents

1. Introduction
2. Features
3. Technologies Used
4. Installation
5. Usage
6. Real-Time Dashboard
7. Data Analysis
8. Machine Learning Modeling
9. Contributing
10. License
11. Links

Introduction

In the face of global health challenges, the ability to predict pandemic risks is crucial. This project employs big data analytics to assess risks, using various data sources and machine learning techniques. By analyzing patterns and trends, we aim to provide insights that can guide decision-making.

Features

• Data Analysis: Analyze large datasets to identify trends and patterns.
• Machine Learning Models: Implement classification models to predict risks.
• Real-Time Dashboard: Visualize data and predictions in an interactive dashboard.
• Risk Assessment: Provide assessments based on data-driven insights.

Technologies Used

• Big Data Technologies: Hadoop, HDFS
• Machine Learning: Scikit-learn, TensorFlow
• Data Visualization: D3.js, Plotly
• Database: Real-time databases for live data updates
• Languages: Python, JavaScript

Installation

To get started with the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/Flixteu356/BigData-Architecture.git

2. Navigate to the project directory:

   cd BigData-Architecture

3. Install the required dependencies:

   pip install -r requirements.txt

4. Set up the Hadoop environment. Follow the Hadoop installation guide.

5. Download the necessary datasets from the Releases section and execute the required scripts.

Usage

To run the system, use the following command:

python main.py

This command will start the data processing and machine learning tasks. You can monitor the progress in the console.

Real-Time Dashboard

The real-time dashboard provides an interactive way to visualize data and predictions. It displays key metrics and trends related to pandemic risk. To access the dashboard, open your web browser and navigate to:

http://localhost:5000

The dashboard updates automatically as new data comes in, allowing users to see the latest insights.

Data Analysis

Data analysis is a critical component of this project. We use various techniques to clean, preprocess, and analyze the data. Key steps include:

1. Data Cleaning: Remove inconsistencies and missing values.
2. Exploratory Data Analysis (EDA): Use statistical methods to explore the data.
3. Feature Engineering: Create new features that enhance model performance.

We analyze data from multiple sources, including health organizations and social media, to gather a comprehensive view of the pandemic landscape.

Machine Learning Modeling

Machine learning plays a vital role in predicting pandemic risks. We implement various classification models, including:

• Logistic Regression: A simple yet effective model for binary classification.
• Random Forest: An ensemble method that improves accuracy by combining multiple decision trees.
• Support Vector Machines (SVM): A powerful model for high-dimensional data.

Each model undergoes rigorous testing and validation to ensure accuracy and reliability.

Contributing

We welcome contributions from the community! If you want to help improve the project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with clear messages.
4. Push your branch to your forked repository.
5. Submit a pull request.

Please ensure that your code adheres to our coding standards and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Links

For the latest releases, please visit the Releases section. Here, you can download necessary files and execute them as needed.

Thank you for your interest in the BigData-Architecture project! Together, we can make a difference in understanding and mitigating pandemic risks.