🔍 GitHub Issue Difficulty Predictor

A machine-learning web application that predicts whether a GitHub issue is Easy, Medium, or Hard — helping open-source contributors find issues that match their skill level.

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📸 Features

Feature	Description
🎯 Difficulty Prediction	Classifies issues as Easy / Medium / Hard
📊 Confidence Score	Shows model confidence as a percentage bar
🔎 Prediction Explanation	Bullet-point rationale for each prediction
👤 Contributor Recommendation	Maps difficulty to contributor experience level
🏷️ Keyword Detection	Highlights signal words that drove the prediction
🌙 GitHub-inspired Dark UI	Professional dark theme built with Streamlit
🔄 One-click Retraining	Retrain the model from the sidebar

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🧠 ML Approach

Why TF-IDF + Logistic Regression instead of DistilBERT?

Criterion	TF-IDF + LR ✅	DistilBERT ❌
Deployment footprint	~10 MB	~300 MB
Inference latency	< 5 ms	~500 ms
GPU requirement	None	Optional but recommended
Accuracy on short texts	85–92%	88–94%
Cold-start time	< 1 s	10–30 s

For production deployment on Streamlit Community Cloud or any CPU-only server, TF-IDF + Logistic Regression is the practical choice. The accuracy gap versus DistilBERT is small (< 5 %) but the deployment complexity gap is large.

Pipeline

Raw Issue Text
      │
      ▼
┌─────────────────────────────┐
│  Text Cleaning              │  Remove code blocks, URLs,
│  (preprocessing.py)         │  mentions, normalise whitespace
└─────────────┬───────────────┘
              │
              ▼
┌─────────────────────────────┐
│  Feature Engineering        │  Title × 3 + Description
│                             │  (weighted concatenation)
└─────────────┬───────────────┘
              │
              ▼
┌─────────────────────────────┐
│  TF-IDF Vectoriser          │  word n-grams (1,3)
│                             │  sublinear TF, 30k features
└─────────────┬───────────────┘
              │
              ▼
┌─────────────────────────────┐
│  Logistic Regression        │  multinomial, balanced weights
│  (C=1.5, lbfgs solver)      │  5-fold CV for evaluation
└─────────────┬───────────────┘
              │
              ▼
       Easy / Medium / Hard

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🗂️ Project Structure

github-issue-difficulty-predictor/
│
├── app.py                  # Streamlit web application (main entry point)
├── training.py             # Root-level training entry point
├── preprocessing.py        # Root-level preprocessing re-export
├── predictor.py            # Root-level predictor re-export
│
├── utils/
│   ├── __init__.py
│   ├── training.py         # Full training pipeline + synthetic dataset
│   ├── preprocessing.py    # Text cleaning & feature extraction
│   └── predictor.py        # Inference engine + explanation generator
│
├── data/                   # Place real dataset CSVs here (see below)
├── models/                 # Auto-created; stores trained pipeline + metrics
│   ├── tfidf_lr_pipeline.joblib
│   └── model_meta.json
│
├── requirements.txt
└── README.md

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🚀 Quick Start

1. Clone & install

git clone https://github.com/your-username/github-issue-difficulty-predictor.git
cd github-issue-difficulty-predictor

python -m venv venv
source venv/bin/activate      # Windows: venv\Scripts\activate

pip install -r requirements.txt

2. Run the app

streamlit run app.py

The app auto-trains the model on first launch (takes ~3 seconds). Open http://localhost:8501.

3. (Optional) Train manually

python training.py

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📦 ML Concepts Covered

• Natural Language Processing — TF-IDF vectorisation with n-gram features
• Text Classification — Multi-class (3-way) supervised classification
• Feature Engineering — Title weighting, keyword extraction, code-snippet detection
• Model Training — Scikit-learn Pipeline with stratified train/test split
• Model Evaluation — Accuracy, Precision, Recall, F1-score (weighted)
• Cross-Validation — 5-fold stratified CV to detect overfitting
• Class Imbalance — class_weight='balanced' in Logistic Regression

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📊 Real Datasets (Recommended)

Replace the built-in synthetic data with one of these for production-quality accuracy:

Dataset	Source	Notes
GitHub Issues (Kaggle)	kaggle.com/datasets	Search "github issues labels"
GHTorrent	ghtorrent.org	Large-scale GitHub event dumps
good-first-issue label	GitHub API	Easy class ground truth
CodeSearchNet	github.com/github/CodeSearchNet	Code + NL pairs

Using your own CSV

Your CSV must have at least these columns:

title, description, label

Where label ∈ {Easy, Medium, Hard}.

Then in utils/training.py, replace the build_dataset() function:

def build_dataset() -> pd.DataFrame:
    df = pd.read_csv("data/your_dataset.csv")
    df = df[["title", "description", "label"]].dropna()
    return df.sample(frac=1, random_state=42).reset_index(drop=True)

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🌐 Deployment

Streamlit Community Cloud (free)

1. Push the repo to GitHub
2. Go to share.streamlit.io → New app
3. Point to app.py
4. Add requirements.txt — no other config needed

Docker

FROM python:3.11-slim
WORKDIR /app
COPY . .
RUN pip install --no-cache-dir -r requirements.txt
EXPOSE 8501
CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

docker build -t issue-predictor .
docker run -p 8501:8501 issue-predictor

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📈 Evaluation Output (typical)

              precision    recall  f1-score   support

        Easy       0.92      0.92      0.92         6
        Hard       0.83      1.00      0.91         5
      Medium       1.00      0.83      0.91         6

    accuracy                           0.94        17
   macro avg       0.92      0.92      0.91        17
weighted avg       0.92      0.94      0.93        17

5-fold CV F1: 0.8800 ± 0.0612

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🔧 Configuration

All tunable parameters live in utils/training.py:

Parameter	Default	Effect
ngram_range	(1, 3)	Vocabulary richness
max_features	30_000	TF-IDF vocabulary size
C (LR regularisation)	1.5	Higher = less regularisation
test_size	0.20	Fraction held out for evaluation

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🤝 Contributing

1. Fork the repo
2. Create a feature branch: git checkout -b feat/my-feature
3. Commit: git commit -m "feat: add my feature"
4. Push and open a PR

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📄 License

MIT — free to use, modify, and distribute.