Hackathon Models – Wonders of AI 2.0

This repository contains a collection of machine learning models and synthetic datasets developed during the Wonders of AI 2.0 Hackathon.

Our team Neo secured 5th place among 70+ teams, where we built multiple AI models aimed at improving student analytics, academic monitoring, and educational recommendations.

The repository includes five independent AI models, each designed to solve a different problem in education analytics and student performance monitoring.

These models demonstrate applications of machine learning, data generation, predictive analytics, and recommendation systems.

Team Information

Team Name: Neo

Achievement:

🏆 5th Place – Wonders of AI 2.0 Hackathon Competing against 70+ teams

Repository Structure

Hackathon-models
│
├── model1
│   ├── create_dataset.py
│   ├── performance_model.ipynb
│   ├── student_performance2.csv
│   └── studentperformancemodel.pkl
│
├── model2
│   ├── create_dataset.py
│   ├── student alerts.csv
│   ├── knn_model.pkl
│   ├── modelforalert.ipynb
│   └── scaler.pkl
│
├── model3
│   ├── create_dataset.py
│   ├── quiz.py
│   ├── tgpt_client.py
│   ├── tgpt_server.py
│   ├── quiz_client.py
│   └── quiz_server.py
│
├── model4
│   ├── create_dataset.py
│   ├── balancedreadingbehavior.csv
│   ├── modelfordoc.ipynb
│   └── randomforestmodel.pkl
│
├── model5
│   ├── enhancedstudentrecommendations.csv
│   ├── generatedstudentrecommendations.csv
│   ├── final.ipynb
│   └── recommendation_model.pkl

Technologies Used

This project uses the following technologies:

| Technology | Purpose | | ---------------- | ---------------------------------- | | Python | Core programming language | | Scikit-learn | Machine learning models | | Pandas | Data processing | | NumPy | Numerical operations | | Jupyter Notebook | Model training and experimentation |

Model 1 – Student Performance Prediction

This model predicts student academic performance based on multiple academic and extracurricular parameters.

Input Features

| Feature | | --------------------------- | | CGPA | | GPA | | Average Assignment Marks | | Average Project Marks | | Attendance Percentage | | Class Participation Credits | | Extracurricular Activities | | Achievements Credits | | Number of Students in Class | | Rank in Class | | Certifications Count |

Output

Performance category of the student.

Files

| File | Description | | ----------------------------- | --------------------------- | | create_dataset.py | Generates synthetic dataset | | performance_model.ipynb | Model training notebook | | student_performance2.csv | Dataset | | studentperformancemodel.pkl | Trained ML model |

Example Use Case

Predict whether a student is:

• High performer • Average performer • At risk

Model 2 – Student Alert Prediction System

This model predicts whether a student should receive academic alerts based on their academic activity.

Input Features

| Feature | | --------------------- | | CGPA | | GPA | | Attendance Percentage | | Assignment Due Days | | Project Due Days | | Fees Due |

Predicted Alerts

| Alert | | ---------------- | | Attendance Alert | | GPA Alert | | CGPA Alert | | Assignment Alert | | Project Alert | | Fee Alert |

Algorithm Used

K-Nearest Neighbors using K-Nearest Neighbors

Files

| File | Description | | --------------------- | ---------------------------- | | create_dataset.py | Synthetic dataset generation | | student alerts.csv | Dataset | | modelforalert.ipynb | Model training | | knn_model.pkl | Trained KNN model | | scaler.pkl | Feature scaler |

Model 3 – AI Quiz and GPT Learning System

This module provides a quiz-based AI learning system using client-server architecture.

It simulates interaction between:

• Quiz application • AI response server • GPT-style assistant

Components

| File | Purpose | | ----------------- | --------------------- | | create_dataset.py | Generate quiz dataset | | quiz.py | Quiz logic | | quiz_server.py | Quiz backend server | | quiz_client.py | Quiz frontend client | | tgpt_server.py | GPT simulation server | | tgpt_client.py | Client interaction |

Functionality

• Interactive quizzes • AI response generation • Client-server communication

Model 4 – Reading Behavior Detection Model

This model detects student reading engagement using behavioral metrics.

Input Features

| Feature | | ------------------------ | | Total Pages | | Book Complexity | | Readability Score | | Reading Engagement Index | | Estimated Reading Time | | Actual Reading Time | | Scroll Speed | | Scroll Depth | | Backtracking Rate | | Page Jump Rate | | Exit Frequency |

Output

Flag

Flag indicates abnormal reading behavior such as:

• Skimming • Lack of engagement • Possible plagiarism

Algorithm Used

Random Forest

Files

| File | Description | | ----------------------------- | ----------------------- | | create_dataset.py | Dataset generator | | balancedreadingbehavior.csv | Training dataset | | modelfordoc.ipynb | Model training notebook | | randomforestmodel.pkl | Trained model |

Model 5 – Student Event Recommendation System

This model recommends events and opportunities for students based on their interests and performance.

Input Features

| Feature | | ----------------- | | Student ID | | CGPA | | Past Events | | Interest Domain | | Performance Score |

Output

| Output | | -------------------- | | Recommended Events | | Personalized Message |

Files

| File | Description | | ------------------------------------- | ---------------------------- | | enhancedstudentrecommendations.csv | Base dataset | | generatedstudentrecommendations.csv | Generated recommendations | | final.ipynb | Model training | | recommendation_model.pkl | Trained recommendation model |

Synthetic Dataset Generation

Each model includes a script:

create_dataset.py

This script automatically generates synthetic training datasets, which helps simulate realistic educational data without using real student information.

How to Run the Models

Install dependencies:

pip install pandas numpy scikit-learn

Run dataset generation:

python create_dataset.py

Train or test models using:

Jupyter Notebook

Applications

These models can be used for:

• Student performance prediction • Academic early warning systems • Educational recommendation engines • Smart learning analytics • AI-assisted education platforms

Hackathon Impact

During the Wonders of AI 2.0 Hackathon, these models were designed as part of an AI-powered education analytics system.

The project demonstrated:

• Predictive analytics for students • AI-driven alerts and monitoring • Intelligent recommendation systems • Behavioral analysis in education

The project secured 5th place among 70+ teams.

Author

Abinesh N

GitHub https://github.com/Abineshabee