---
language: en
license: mit
tags:
  - sklearn
  - xgboost
  - kmeans
  - education
  - skill-recommendation
library_name: sklearn
---

# SkillBridge — Coding in Color Recommendation Models

Two ML models that power skill recommendations for the Coding in Color program.

## Architecture

```
Model 1 (K-Means)  →  student archetype
                          ↘
                           LLM  →  skill rec + project idea
                          ↗
Model 2 (XGBoost)  →  follow-through probability
```

### Model 1: Skill Cluster (K-Means)
- **Input:** 36 integer skill columns (checkin counts per skill)
- **Output:** Cluster name, confidence, distances

### Model 2: Engagement Predictor (XGBoost)
- **Input:** 7 engagement features (no skill selection — that's the LLM's job)
- **Output:** Follow-through probability (0-1)

## Usage

```python
import joblib
import xgboost as xgb
from huggingface_hub import hf_hub_download

cluster_model = joblib.load(hf_hub_download("Dc-4nderson/cic-skillbridge-models", "model1/cluster_model.joblib"))
scaler = joblib.load(hf_hub_download("Dc-4nderson/cic-skillbridge-models", "model1/scaler.joblib"))

eng_model = xgb.XGBClassifier()
eng_model.load_model(hf_hub_download("Dc-4nderson/cic-skillbridge-models", "model2/engagement_model.json"))
```