Dropout Analysis for School Education

Table Of Contents

• About the Project
• Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Features
• Contributing

About the Project

The Dropout Analysis and Intervention System is a comprehensive solution aimed at addressing the high dropout rates in schools, with a particular focus on mitigating the impact of poverty and various socio-economic factors. The project provides a strategic framework for governments to formulate targeted policies by analyzing dropout data across different categories.

Glimps of our Project

Built With

• Frontend: React
• Backend:
  • Node.js for general backend functionality
  • Flask for ML backend

Machine Learning Models

• K-Nearest Neighbors (KNN)
• Random Forest

Getting Started

Prerequisites

General:

1. Git:

   • Make sure Git is installed on your system. You can download it from here.

2. Node.js and npm:

   • Install Node.js and npm from https://nodejs.org/.

3. Python:

   • Install Python from https://www.python.org/.

Frontend:

1. React:

   • Ensure you have a basic understanding of React.
   • Documentation: https://reactjs.org/docs/getting-started.html

2. npm Packages:

   • Navigate to the frontend directory and install dependencies using npm install.

Backend:

1. Flask:

   • Install Flask using pip install Flask.

2. Machine Learning Libraries:

   • Install scikit-learn using pip install scikit-learn.

3. Other Backend Dependencies:

   • Navigate to the backend directory and install dependencies using pip install -r requirements.txt.

Database (if applicable):

1. Database System:

   • Ensure the necessary database system MongoDB or is installed.

2. Database Connection Configuration:

   • Provide configuration details for connecting the backend with the database in your backend code.

Environment Variables:

1. Set Up Environment Variables:
   • If your project uses sensitive information (e.g., API keys, database credentials), make sure you set up environment variables for security. You can use tools like dotenv for this purpose.

Installation

1. Clone the Repository:

   git clone https://github.com/UdayGohel/DropoutAnalysis.git

2. Navigate to Project Directory:

   cd DropoutAnalysis

3. Install Dependencies for Back-end:

   npm install

4. Run the Application for Back-end (Express or Node.js server):

   npm start

5. Navigate to Flask Project Directory:

   cd ML2  # Replace with the actual directory containing your Flask app

6. Install Dependencies for Flask:

   pip install -r requirements.txt

   If you don't have a requirements.txt file, make sure Flask is installed:

   pip install Flask

7. Run the Flask Application:

   python server.py

   Replace app.py with the actual name of your Flask application file.

8. Navigate to React Project Directory:

   cd client-web

9. Install Dependencies for React JS:

   npm install

10. Run the Application for Front-end:

    npm start

Usage

Policy Precision:

• Governments can make informed policy decisions based on clear visualizations and analyses.

Resource Optimization:

• Efficiently allocate resources to schools and areas with the highest dropout rates.

Reduced Dropout Rates:

• Targeted interventions will contribute to a significant reduction in dropout rates.

Social Inclusion:

• Address gender, caste, and age-related disparities for a more inclusive education system.

Data-Driven Decision-Making:

• Encourage evidence-based decision-making for sustainable improvements.

Features

Robust Feature Set:

The system offers a comprehensive and powerful array of features.

Visualization Tools:

Utilizes various charts (bar, line, pie) for effective data visualization. Enables easy comparison and tracking of dropout rates across different categories. Predictive Analytics:

Integrates machine learning models for predicting potential dropout cases. Features an early warning system with alerts for timely intervention and support.

Map Integration:

Includes an interactive map overlay for Gujarat districts. Utilizes regional heatmaps to identify high-risk areas for targeted interventions.

Contributors

• Devam Doshi
• Uday Gohel
• Dhruv Shah
• Harekrushna Tejani
• Harsh Nirmal
• Isha Sanghani

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project, you agree to abide by its terms.

We appreciate your contributions and look forward to working with you!