This repository contains three coding challenges designed for the "Bridging AI and Society" summer school program. Each challenge focuses on a different aspect of AI's societal impact and is designed to be completed within approximately three hours. The challenges include educational notebooks to guide students and real-world datasets for authentic learning experiences.
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Challenge 1: Wine Quality Prediction AI Project
- Theme: AI for quality assessment and decision-making in agriculture and food industry.
- Description: Build an AI system to predict wine quality based on physicochemical properties, demonstrating how AI can augment human expertise in specialized domains.
- Dataset: Real wine quality data from Kaggle (1,143 authentic wine samples) ✅ Ready to use!
- Files:
challenge_1/ - 🚀 Run in Google Colab:
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Challenge 2: Fake News Detector Project in AI
- Theme: AI for combating misinformation and promoting media literacy.
- Description: Develop a system to identify and classify news articles as either real or fake using NLP techniques.
- Dataset: Real fake news data from Kaggle (40,000+ news articles) ✅ Ready to use!
- Files:
challenge_2/ - 🚀 Run in Google Colab:
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Challenge 3: Social Media Spam Detection
- Theme: AI for digital well-being and combating online harassment.
- Description: Build an AI model to automatically detect and filter out spam comments on social media platforms using real YouTube data.
- Dataset: Real YouTube spam data from UCI ML Repository (1,956 comments) ✅ Ready to use!
- Files:
challenge_3/ - 🚀 Run in Google Colab:
summer_school_challenges/
├── challenge_1/
│ ├── data/ # Dataset for Challenge 1
│ ├── notebooks/ # Jupyter notebooks for Challenge 1
│ │ └── educational_notebook_1.ipynb
│ ├── requirements.txt # Python dependencies for Challenge 1
│ └── README.md # Challenge-specific README
├── challenge_2/
│ ├── data/ # Dataset for Challenge 2
│ ├── notebooks/ # Jupyter notebooks for Challenge 2
│ │ └── educational_notebook_2.ipynb
│ ├── requirements.txt # Python dependencies for Challenge 2
│ └── README.md # Challenge-specific README
├── challenge_3/
│ ├── data/ # Dataset for Challenge 3
│ ├── notebooks/ # Jupyter notebooks for Challenge 3
│ │ └── educational_notebook_3.ipynb
│ ├── create_dataset.py # Script to generate sample dataset
│ ├── requirements.txt # Python dependencies for Challenge 3
│ └── README.md # Challenge-specific README
├── requirements.txt # Global project dependencies
└── README.md # Main repository README
Each challenge now uses authentic real-world datasets for meaningful learning experiences:
- Source: Kaggle Wine Quality Dataset
- Size: 1,143 authentic wine samples with 11 chemical properties
- Features: Fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, sulfur dioxide, density, pH, sulphates, alcohol
- Target: Quality scores (3-8) from professional wine tasters
- Key Learning: Feature engineering, regression vs classification, model interpretation
- Societal Impact: Understanding AI's role in augmenting human expertise in specialized domains
- Source: Kaggle Fake News Dataset
- Size: 40,000+ real news articles (mix of authentic and fake news)
- Features: Article titles, content, publication info
- Target: Binary classification (Real vs Fake)
- Key Learning: Binary text classification with real misinformation patterns
- Societal Impact: Building tools for media literacy and information integrity
- Source: UCI ML Repository YouTube Spam Collection
- Size: 1,956 authentic YouTube comments from popular music videos
- Features: Comment text from real YouTube users
- Target: Binary classification (Ham vs Spam)
- Key Learning: Short-text classification with real social media language
- Societal Impact: Protecting digital well-being and reducing online harassment
- Python 3.8+ with pip
- Jupyter Notebook or JupyterLab (or use Google Colab - no setup required!)
- Git (for cloning the repository)
No installation required! Click any of the Colab badges above to run the notebooks directly in your browser:
- ✅ Zero Setup: No need to install Python, Jupyter, or dependencies
- ☁️ Cloud Computing: Free GPU/TPU access for faster training
- 💾 Auto-Save: Your work is saved to Google Drive automatically
- 🤝 Easy Sharing: Share your results with instructors or classmates
- 📱 Any Device: Works on laptops, tablets, and even phones
How to use Colab:
- Click a Colab badge above for any challenge
- Sign in with your Google account
- Click "Copy to Drive" to save your own version
- Start coding immediately!
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Clone the repository:
git clone <repository_url> cd summer_school_challenges
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Navigate to the desired challenge directory:
cd challenge_1 # or challenge_2, challenge_3
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Install dependencies:
pip install -r requirements.txt
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Set up datasets:
- All Challenges: All datasets are pre-installed and ready to use! ✅
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Launch Jupyter Notebook:
jupyter notebook notebooks/educational_notebook_1.ipynb # or 2, 3
Follow the instructions within the educational notebook to complete the challenge.
Core dependencies across all challenges:
pandas>=1.3.0
numpy>=1.21.0
scikit-learn>=1.0.0
matplotlib>=3.5.0
seaborn>=0.11.0
jupyter>=1.0.0
nltk>=3.7
ucimlrepo>=0.0.7
Missing Dependencies:
# Update pip and install requirements
pip install --upgrade pip
pip install -r requirements.txt
# If issues persist, try creating a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt- Educational Use: These materials are designed for educational purposes
- Dataset Credits:
- Wine Quality: UCI ML Repository via Kaggle
- Fake News: Kaggle community dataset
- YouTube Spam: UCI ML Repository
- Open Source: Code and notebooks available for educational use
Ready to explore AI's impact on society? Start with Challenge 1 and dive into the world of machine learning with real data! 🚀