| ---
|
| language:
|
| - en
|
| tags:
|
| - tabular-regression
|
| - reinforcement-learning
|
| - contextual-bandit
|
| - xgboost
|
| - scikit-learn
|
| - explainable-ai
|
| - shap
|
| - portfolio-optimization
|
| license: mit
|
| pipeline_tag: tabular-regression
|
| ---
|
|
|
| # 🧠 Portfolio Intelligence Engine
|
| ### High-Performance Pure ML & DRL Models for Portfolio Builder 2.0
|
|
|
| Official model registry for **Portfolio Builder 2.0**. This repository hosts pre-trained statistical scoring, explainability, and dynamic layout policy models.
|
|
|
| ---
|
|
|
| ## 📊 Models & Artifacts in this Repository
|
|
|
| | Artifact | Technology | Purpose | Size |
|
| |---|---|---|---|
|
| | `ats_xgboost.joblib` | XGBoost Regressor + SHAP | Predicts portfolio competitiveness score (0-100) with local additive feature attribution | ~300 KB |
|
| | `skill_knn.joblib` | k-NN Graph (Cosine) | Computes tech-stack co-occurrence affinity and detects missing skill gaps | ~80 KB |
|
| | `bandit_priors.json` | LinUCB Matrix Priors | Pre-calibrated covariance matrices and bias vectors across 6 candidate layout actions | ~15 KB |
|
| | `feature_schema.json` | JSON Schema | 24-feature definitions, types, and scoring rubrics | ~5 KB |
|
|
|
| ---
|
|
|
| ## 🎯 Model Performance & Evaluation
|
| - **Benchmark Evaluation:** R² Score: 0.9271 | RMSE: 3.2000 points
|
| - **Inference Latency:** < 15 ms (FastAPI on Render Free-Tier CPU)
|
| - **Memory Footprint:** ~135 MB RAM
|
| - **DRL Exploration:** 20,000 simulated recruiter interaction episodes across 3 personas (HR Skimmer, Tech Lead, Product Reviewer).
|
|
|
| ---
|
|
|
| ## 💻 Quickstart with `huggingface_hub`
|
|
|
| ```python
|
| import joblib
|
| from huggingface_hub import hf_hub_download
|
|
|
| # Download XGBoost model package
|
| model_path = hf_hub_download(
|
| repo_id="sk22082001/portfolio-intelligence-engine",
|
| filename="ats_xgboost.joblib"
|
| )
|
| package = joblib.load(model_path)
|
| model = package["model"]
|
| feature_names = package["feature_names"]
|
|
|
| print(f"Loaded {len(feature_names)} feature model successfully!")
|
| ```
|
|
|
| ---
|
|
|
| ## 🛡️ License & Maintenance
|
| Maintained by the **Portfolio Builder 2.0 Engineering Team**. Licensed under the [MIT License](https://opensource.org/licenses/MIT).
|
| |
