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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.

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Team Information

Team Name: Neo

Achievement:

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

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Repository Structure

Hackathon-models
│
├── model1
│   ├── create_dataset.py
│   ├── performance_model.ipynb
│   ├── student_performance2.csv
│   └── student_performance_model.pkl
│
├── model2
│   ├── create_dataset.py
│   ├── student alerts.csv
│   ├── knn_model.pkl
│   ├── model_for_alert.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
│   ├── balanced_reading_behavior.csv
│   ├── model_for_doc.ipynb
│   └── random_forest_model.pkl
│
├── model5
│   ├── enhanced_student_recommendations.csv
│   ├── generated_student_recommendations.csv
│   ├── final.ipynb
│   └── recommendation_model.pkl

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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

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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
student_performance_model.pkl	Trained ML model

Example Use Case

Predict whether a student is:

• High performer • Average performer • At risk

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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
model_for_alert.ipynb	Model training
knn_model.pkl	Trained KNN model
scaler.pkl	Feature scaler

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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

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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
balanced_reading_behavior.csv	Training dataset
model_for_doc.ipynb	Model training notebook
random_forest_model.pkl	Trained model

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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
enhanced_student_recommendations.csv	Base dataset
generated_student_recommendations.csv	Generated recommendations
final.ipynb	Model training
recommendation_model.pkl	Trained recommendation model

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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.

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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

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Applications

These models can be used for:

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

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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.

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Author

Abinesh N

GitHub https://github.com/Abineshabee

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