Update model card: document isolation_forest.py dependency and KPIs

model/README.md

@@ -22,6 +22,25 @@ observations by randomly partitioning the feature space with binary trees; point
 (i.e. require few splits) are anomalous. Because scikit-learn is not installed in the pipeline runtime, the
 model is shipped as a self-contained `joblib` blob with no external runtime dependency beyond NumPy.
 
+## Loading the model (reproducible)
+
+```python
+import sys, joblib
+from huggingface_hub import hf_hub_download
+
+# 1) put the implementation module on the path
+sys.path.insert(0, "model")           # model/isolation_forest.py
+# (or: pip-style: download model/isolation_forest.py and import it)
+
+# 2) download and load the serialized model
+path = hf_hub_download("toolathon123/project_20260813_014231_9dbe9212",
+                       "model/isolation_forest_quality.joblib", repo_type="dataset")
+model = joblib.load(path)             # -> isolation_forest.IsolationForest
+```
+
+`model/isolation_forest.py` is the importable source of the model class — required so the pickle can
+resolve the `IsolationForest` class on any machine.
+
 ## Model inputs (features)
 
 The model consumes the 6 numeric features of each shipment:
@@ -41,8 +60,8 @@ the dataset for drill-down and reporting.
 ## Outputs
 
 - **`anomaly_score`** ∈ [0.5, 1.0] — Isolation Forest anomaly score. Higher ⇒ more anomalous.
-- **`high_risk`** ∈ {0, 1} — flag = 1 when `anomaly_score >= threshold` (0.6044), i.e. the shipment is
-  flagged for manual review as a **high-quality-risk order**.
+- **`high_risk`** ∈ {0, 1} — flag = 1 when `anomaly_score >= threshold` (0.6044), i.e.
+  the shipment is flagged for manual review as a **high-quality-risk order**.
 
 ## Daily update strategy
 
@@ -67,8 +86,8 @@ the dataset for drill-down and reporting.
 | Avg temperature deviation | 0.63 °C |
 | Avg humidity deviation | 4.57 %RH |
 
-*Delay improvement rate = relative reduction in mean delay if the 100 high-risk shipments were remediated to
-the low-risk mean delay.*
+*Delay improvement rate = relative reduction in mean delay if the 100 high-risk shipments
+were remediated to the low-risk mean delay.*
 
 ## Intended use
 
@@ -78,4 +97,5 @@ targeting the QIP goal of pushing the high-risk ratio below 5%.
 ## Files
 
 - `isolation_forest_quality.joblib` — serialized model (joblib)
+- `isolation_forest.py` — importable NumPy IsolationForest implementation
 - `model_metadata.json` — training metadata, features, threshold and daily KPIs