AI Dropout Prediction

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Inspiration

The increasing dropout rate in educational institutions—especially in underserved regions—prompted us to build a solution that can predict students at risk of dropping out. By leveraging data-driven insights and AI, we aimed to provide early interventions that could help retain students and support their academic journey.

What it does

Our AI-powered system analyzes student data—such as attendance, performance, socio-economic indicators, and behavioral patterns—to predict which students are at risk of dropping out. The system presents this data in a user-friendly dashboard for school administrators, allowing them to take timely action and provide personalized support.

How we built it

Backend: Trained a machine learning classification model using Python (scikit-learn) with preprocessed student datasets

Frontend: Designed a web dashboard using HTML, CSS, and Flask to display predictions and insights

Data Processing: Handled missing values, performed feature engineering, and used visualization tools (Matplotlib, Seaborn)

Deployment: Hosted the application locally for demo purposes and demonstrated its usability with real-world-like sample data

Challenges we ran into

Finding quality datasets that resembled real-world dropout cases

Balancing accuracy vs interpretability in model design

Ensuring that predictions were explainable and actionable for non-technical school staff

Limited time to integrate real-time database functionality and live deployment

Accomplishments that we're proud of

Successfully built a functional AI system that predicts dropouts with high accuracy

Designed an intuitive dashboard that’s accessible even for non-tech users

Delivered a solution that has real-world social impact potential, especially in government and rural schools

Completed a working prototype under tight deadlines

What we learned

How to apply machine learning for social good in the education domain

Improved our skills in model evaluation, feature selection, and UI/UX thinking

Understood the importance of human-centered design in AI systems

Gained experience presenting technical work to non-technical stakeholders

What's next for AI Dropout Prediction

Integrate real-time data collection from schools (e.g., attendance and marks entry via mobile apps)

Deploy the system using cloud platforms like Heroku or AWS

Build automated SMS/email alert systems for teachers or guardians

Expand the model with deep learning techniques for improved accuracy

Partner with local schools to run a pilot program and gather real feedback

Built With

• css
• flask
• html5
• matplotlib
• scikit-learn