📚 Study Resource Recommender

A Machine Learning-powered web application that recommends YouTube educational videos based on student quiz performance.

🎯 Project Overview

Input: Student quiz results
Output: Personalized YouTube video recommendations for weak topics

🚀 Features

• 🤖 5 ML Models trained to predict student mastery levels
• 📊 Skill Analysis showing mastered, learning, and struggling areas
• 🎬 Video Recommendations matched to weak skills
• 📈 Visual Analytics dashboard
• 🌐 Web Interface built with Streamlit

📁 Project Structure

study-resource-recommender/
├── app.py                      # Streamlit web application
├── recommender.py              # Recommendation engine module
├── requirements.txt            # Python dependencies
├── README.md                   # This file
│
├── data/
│   ├── student_data_for_app.csv    # Student performance data
│   └── skill_video_mapping.csv     # Skill to video mappings
│
├── models/
│   ├── recommender_package.pkl     # All models bundled
│   ├── best_model.pkl              # Best performing model
│   ├── scaler.pkl                  # Feature scaler
│   ├── label_encoder.pkl           # Label encoder
│   └── feature_config.pkl          # Feature configuration
│
└── notebooks/
    ├── Phase1_Data_Collection.ipynb
    ├── Phase2_ML_Models.ipynb
    └── Phase3_Recommendation_Engine.ipynb

🔧 Installation

1. Clone the repository

git clone https://github.com/yourusername/study-resource-recommender.git
cd study-resource-recommender

2. Install dependencies

pip install -r requirements.txt

3. Run the app

streamlit run app.py

📊 Datasets Used

Dataset 1: ASSISTments 2009-2010

• Source: ASSISTments Data
• Size: 346,860 interactions, 4,217 students, 123 skills
• Features: Student ID, Problem ID, Skill Name, Correctness, Hints, Response Time

Dataset 2: Khan Academy YouTube Videos

• Source: Kaggle
• Content: Educational videos with titles, views, likes, duration

Connection

Both datasets are connected through SKILL/TOPIC matching:

ASSISTments skill: "Adding Fractions" → Khan Academy video: "Adding Fractions Tutorial"

🤖 ML Models

Model	Type	Purpose
Random Forest	Ensemble	Predict mastery level
XGBoost	Gradient Boosting	Predict mastery level
Logistic Regression	Linear	Baseline model
KNN	Instance-based	Find similar students
Neural Network (MLP)	Deep Learning	Complex patterns

📈 Features Used

Feature	Description
accuracy	% correct answers
total_attempts	Total problems attempted
avg_hint_ratio	Average hints used / available
avg_response_time	Average time per problem
efficiency_score	Correct answers / hints used
struggle_score	Combined difficulty indicator

🎯 Target Variable

mastery_level:
  - needs_help: Struggling (accuracy < 60%)
  - learning: Progressing (60% ≤ accuracy < 80%)
  - mastered: Proficient (accuracy ≥ 80%)

🌐 Deployment

Option 1: Streamlit Cloud (Recommended)

1. Push code to GitHub
2. Go to share.streamlit.io
3. Connect your GitHub repo
4. Deploy!

Option 2: HuggingFace Spaces

1. Create a new Space on HuggingFace
2. Select "Streamlit" as the SDK
3. Upload your files
4. Deploy!

Option 3: Local

streamlit run app.py

📱 Screenshots

[Add screenshots of your app here]

👨‍💻 Author

Eman-Omar-Yehia-Abdelmawla Nile University ML project

📄 License

This project is for educational purposes.

🙏 Acknowledgments

• ASSISTments for the student performance dataset
• Khan Academy for educational content
• Streamlit for the web framework