metadata
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
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"))