A production-ready framework for building AI/ML solutions to real-world telecom challenges, emphasizing domain expertise and practical problem-solving.
- What Is This?
- Who Should Use This?
- What's Included
- Quick Start
- Use Cases
- Project Structure
- Documentation
- Philosophy
- License
This is a FRAMEWORK, not an implementation.
The Telecom ML Framework provides:
✅ 6 Production-Ready ML Project Templates covering the most common telecom AI/ML use cases
✅ Complete Technical Specifications with problem framing, data requirements, and model architectures
✅ Domain-Informed Data Generators embedding real telecom physics (SINR, QoE, congestion patterns)
✅ Unified Technical Standards ensuring consistency across projects (dependencies, plotting, interpretability)
✅ Portfolio Documentation demonstrating domain expertise and ML problem-solving approach
What this is NOT:
- ❌ Not a trained model or production system
- ❌ Not a Python package to install via pip
- ❌ Not a data science library with APIs
This framework serves as both a project template generator for rapid ML project creation and a portfolio documentation hub showcasing telecom domain expertise applied to ML.
This framework is designed for:
- Telecom professionals transitioning to AI/ML who need structured project templates
- Data scientists entering telecom domain who need problem framing guidance
- ML engineers building telecom analytics solutions
- Portfolio builders demonstrating end-to-end ML thinking
- How to frame business problems as ML tasks
- Domain-driven feature engineering for telecom data
- Proper handling of temporal leakage in time-series problems
- Model interpretability for business stakeholders
- Production-ready project structure and standards
telecom-ml-framework/
├── template/ # Project template (copy this to start)
│ ├── src/__project_name__/ # Python package structure
│ ├── notebooks/ # Jupyter notebook templates
│ ├── data/ # Data directories
│ ├── tests/ # Test templates
│ └── pyproject.toml # Dependencies with SHAP compatibility
│
├── docs/ # Documentation
│ ├── USE_CASES.md # Index of all 6 use cases
│ ├── GETTING_STARTED.md # Detailed usage guide
│ ├── PORTFOLIO_OVERVIEW.md # Portfolio context
│ └── 01-06 use case specs # Individual specifications
│
└── examples/ # Usage examples
└── create_project.py # Template instantiation script
| # | Use Case | ML Type | Key Algorithms | Status |
|---|---|---|---|---|
| UC1 | Churn Prediction | Binary Classification | XGBoost, LightGBM | ✅ Spec Complete |
| UC2 | Root Cause Analysis | Ranking / Causal Inference | Gradient Boosting, GNN | ✅ Spec Complete |
| UC3 | Anomaly Detection | Unsupervised Learning | Isolation Forest, LSTM AE | ✅ Spec Complete |
| UC4 | QoE Prediction | Regression | LightGBM, CatBoost | ✅ Spec Complete |
| UC5 | Capacity Forecasting | Time-Series Forecasting | Prophet, ARIMA, LSTM | ✅ Spec Complete |
| UC6 | Network Optimization | Reinforcement Learning | Q-Learning, Genetic Algo | ✅ Spec Complete |
📖 View detailed use case documentation →
# Install uv if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh# 1. Copy the template directory
cp -r template/ ../my-churn-prediction
cd ../my-churn-prediction
# 2. Customize the project
# - Rename src/__project_name__/ to your project name
# - Update pyproject.toml with your details
# - Customize data_generator.py for your use case
# 3. Install dependencies
uv sync
# 4. Generate synthetic data
uv run python -m your_project_name.data_generator
# 5. Start working!
uv run jupyter lab notebooks/# Create a new project from template
python examples/create_project.py \
--name churn-prediction \
--use-case UC1 \
--output ../my-projects/
cd ../my-projects/churn-prediction
uv sync
uv run python -m churn_prediction.data_generator- Read the documentation: Start with GETTING_STARTED.md
- Choose a use case: Review USE_CASES.md to select your focus
- Customize the template: Adapt data generation and features to your needs
- Build your portfolio: Each project becomes a standalone repository
Business Problem: Which customers are likely to cancel their subscription?
ML Approach: Binary classification with temporal feature engineering
Key Challenge: Preventing future data leakage, handling class imbalance
Output: Churn probability + SHAP interpretability for retention campaigns
Business Problem: When network issues occur, what was the original cause?
ML Approach: Ranking/classification on event-alarm-ticket causal chains
Key Challenge: Multi-label problem with correlated failure modes
Output: Ranked root cause hypotheses with causal graphs
Business Problem: Detect cell towers behaving abnormally before they fail
ML Approach: Unsupervised learning on multivariate KPI time-series
Key Challenge: Defining "normal" in highly dynamic networks
Output: Anomaly scores and severity ranking
Business Problem: Predict user-perceived quality from network conditions
ML Approach: Regression on session-level features (throughput, latency, loss)
Key Challenge: QoE is subjective and application-dependent
Output: Predicted MOS score and QoE class
Business Problem: Predict future network load to plan capacity expansions
ML Approach: Time-series forecasting with seasonal decomposition
Key Challenge: Capturing diurnal patterns, weekend effects, growth trends
Output: Load forecasts with confidence intervals
Business Problem: Recommend parameter adjustments to improve KPIs
ML Approach: Reinforcement learning with state-action-reward formulation
Key Challenge: Delayed rewards, exploration vs exploitation
Output: Recommended actions and expected KPI improvements
Each project created from this framework follows this structure:
your-project-name/
├── README.md # Project-specific documentation
├── QUICKSTART.md # Quick setup guide
├── CONTRIBUTING.md # Contribution guidelines
├── pyproject.toml # Dependencies (uv-managed)
├── .gitignore # Python + data exclusions
│
├── data/
│ ├── raw/ # Generated synthetic data
│ └── processed/ # Feature-engineered datasets
│
├── src/your_project_name/
│ ├── __init__.py
│ ├── config.py # Centralized configuration
│ ├── data_generator.py # Domain-informed data generation
│ ├── features.py # Feature engineering pipeline
│ └── models.py # ML model implementations
│
├── notebooks/
│ └── 01_analysis.ipynb # Main analysis notebook
│
└── tests/
└── test_data_quality.py # Data validation tests
- Getting Started Guide - Step-by-step first project walkthrough
- Use Cases Index - Comparison and selection guide for all 6 use cases
- Portfolio Overview - Context and career transition narrative
Each use case has a detailed specification document covering:
- Objective and business context
- ML problem framing
- Input features and forbidden data (temporal leakage prevention)
- Label definitions
- Model architecture recommendations
- Evaluation metrics
- Notebook structure and plotting standards
- SHAP interpretability requirements
- Template README - How to use the template
- Template Quickstart - Fast setup commands
- Contributing Guide - For collaborative projects
This framework emphasizes:
✅ Problem Framing - Translating business problems into well-defined ML tasks
✅ Domain Knowledge - Embedding telecom physics in data and features
✅ Interpretability - SHAP explanations for business stakeholders
✅ Practical Solutions - Fit-for-purpose algorithms, not bleeding-edge research
✅ End-to-End Thinking - Data → Features → Model → Insights → Impact
Production telecom data is proprietary and sensitive. Instead of using off-the-shelf synthetic data tools, this framework provides hand-crafted data generators that:
- Embed real telecom physics (SINR, Shannon capacity, congestion patterns)
- Control data quality and realism (class imbalance, temporal patterns)
- Maintain interpretability (every data point has a clear causal story)
- Demonstrate domain expertise in how signals propagate and networks behave
All templates enforce:
- Python 3.11+ for modern language features
- uv for fast, deterministic dependency management
- SHAP-compatible versions:
numpy<2.0,xgboost<2.0,numba>=0.59.0 - Unified plotting: Seaborn with context switching (notebook vs presentation)
- Testing: pytest for data quality and pipeline validation
- Linting: Ruff for code quality
- ✅ 6 use cases fully specified with problem framing
- ✅ Production-ready project template
- ✅ Domain-informed data generation helpers
- ✅ Unified technical standards (SHAP compatibility, plotting)
- ✅ Complete documentation and usage guides
- v1.1.0: Add notebook templates for each use case
- v1.2.0: Enhanced create_project.py with interactive prompts
- v2.0.0: Cookiecutter integration for easier project generation
This is primarily a portfolio/framework project, but suggestions and improvements are welcome!
- Found a bug? Open an issue
- Have an enhancement idea? Start a discussion
- Want to contribute? See CONTRIBUTING.md for guidelines
This framework is released under the MIT License - feel free to use it for learning, portfolio building, or commercial projects.
See LICENSE for full details.
Framework structure inspired by:
- cookiecutter-data-science - Project templates for data science
- scikit-learn-contrib - ML framework organization
- FastAPI - Documentation best practices
Telecom domain knowledge from:
- 3GPP standards (LTE, 5G NR)
- ITU-T QoE recommendations
- 19+ years in network operations and optimization
Adityo Nugroho
- Portfolio: https://adityonugrohoid.github.io
- GitHub: https://github.com/adityonugrohoid
- LinkedIn: https://www.linkedin.com/in/adityonugrohoid/
Last Updated: January 2025 | Framework Status: Stable (v1.0.0)