Project Overview
This project addresses the pressing issue of student dropouts in Indian schools by leveraging machine learning and mobile app development. It provides a comprehensive solution for predicting dropout likelihood and facilitating collaboration between educators, parents, and students.
Key Features
- Data-Driven Analysis: Employs advanced data analysis techniques to identify key factors influencing dropout rates.
- Predictive Model: Utilizes a Random Forest classifier, trained on a robust dataset, to accurately predict dropout probability.
- Mobile App Interface: Offers a user-friendly mobile app built with Android Studio, allowing for easy data input and prediction retrieval.
- Collaborative Platform: Facilitates collaboration between counselors and skill-based centers to provide timely support to students at risk.
Dataset Overview
Technologies
- Programming Languages: Python and Java
- Machine Learning: Random Forest
- Development Environments: Google Colab and Android Studio
- Libraries and Frameworks: Matplotlib, Scikit-learn, Pandas, NumPy, Android SDK
To run the project locally on your machine:
- Clone the Repository:
git clone https://github.com/Anwarulh007/EduProtect--Student-Dropout-Analysis
- Open the Project: Navigate to the project folder and open the index.html file in your web browser to explore the website locally.
Live Demo🔗 Visit EduProtect
Visualization
Let's see the distribution of Dropout Rates with respect to School Type using bar chart or pie chart
School wise Dropout Rates
Insights 🔹
There has been maximum number of dropouts from Government School.
Location wise Dropout Rates
Insights 🔹
The greatest percentage of dropouts has come from rural areas.
Gender wise Dropout Rates
Insights 🔹
The largest percentage of dropouts have been women.
Caste wise Dropout Rates
Insights 🔹
The ST caste has had the highest percentage of dropouts.
Standard wise Dropout Rates
Insights🔹
The highest percentage of dropouts came from the eighth standard.
Age wise Dropout Rates
Insights🔹
The age group of 12 years old accounts for the highest percentage of dropouts.
Overall Dropout Rates based on all Categories
Insights🔹
Age/Standard category dropout rates have been the highest.
Total Dropout Percentage
Link to download apk
Run the Application
- Start the development server for the backend (Python) using Google Colab or your local environment.
- Build and run the Android app using Android Studio.
App Interface
Output
Contributing 🤝
We welcome your contributions to enhance the platform and improve user experience! Feel free to open a pull request or issue if you have any suggestions or features to add.
Contribution Guidelines:
Fork the repository. Create a new branch for your feature or bug fix. Make your changes and submit a pull request.
Usage
- Login or Create an Account: Users can create an account or login to access the application.
- Input Student Data: Enter relevant information about the student, such as socio-economic background, academic performance, and other factors.
- Receive Prediction: The application will use the predictive model to calculate the dropout likelihood and provide a prediction.
- Access Resources: Counselors and skill-based centers can join the app to access resources, connect with students, and offer support.
License
This project is licensed under the MIT License.