Create README.md

README.md

@@ -0,0 +1,84 @@
+---
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: tabular-classification
+library_name: sklearn
+tags:
+- stacking-ensemble
+- xgboost
+- catboost
+- lightgbm
+- adaboost
+- randomforest
+model-index:
+- name: stacking-ensemble-learning
+  results:
+  - task:
+      type: tabular-classification
+      name: Network Intrusion Detection
+    dataset:
+      name: CSE-CIC-IDS2018 Cleaned
+      type: cicids2018
+      url: https://www.kaggle.com/datasets/ekkykharismadhany/csecicids2018-cleaned
+    metrics:
+    - type: accuracy
+      value: 1.0
+      name: Accuracy (Meta Model)
+    - type: f1
+      value: 1.0
+      name: F1 Score (Meta Model)
+    - type: precision
+      value: 1.0
+      name: Precision (Meta Model)
+    - type: recall
+      value: 1.0
+      name: Recall (Meta Model)
+---
+
+# IDS Stacking Ensemble Learning
+
+Stacking ensemble (XGBoost + CatBoost + LightGBM + AdaBoost) for base mdoel with Random Forest as meta-model for Network Intrusion Detection System (IDS).
+
+## Metrics (test set)
+
+| Model | Accuracy | F1 |
+|-------|----------|----|
+| XGBoost | 1.0000 | 1.0000 |
+| CatBoost | 0.9998 | 0.9998 |
+| LightGBM | 0.4441 | 0.5403 |
+| AdaBoost | 0.9911 | 0.9907 |
+
+## Requirements
+
+```bash
+pip install scikit-learn xgboost catboost lightgbm pandas numpy joblib huggingface_hub
+```
+
+## Usage
+
+```python
+import joblib
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+models = {k: joblib.load(hf_hub_download("mrsindhunugroho/stacking-ensemble-learning", f"models/{k}_model.pkl"))
+          for k in ["xgboost", "catboost", "lightgbm", "adaboost"]}
+meta = joblib.load(hf_hub_download("mrsindhunugroho/stacking-ensemble-learning", "models/meta_model.pkl"))
+le   = joblib.load(hf_hub_download("mrsindhunugroho/stacking-ensemble-learning", "models/label_encoder.pkl"))
+
+base_preds = np.column_stack([m.predict(X) for m in models.values()])
+y_pred = le.inverse_transform(meta.predict(base_preds))
+```
+
+## Dataset
+
+- **Source:** Kaggle — [CSE-CIC-IDS2018 Cleaned](https://www.kaggle.com/datasets/ekkykharismadhany/csecicids2018-cleaned)
+- **Original:** Canadian Institute for Cybersecurity (CSE-CIC-IDS2018)
+- **Preprocessing:** sampling, label encoding, imputation, feature sanitization
+
+## Author
+
+Sindhu Nugroho — [ORCID](https://orcid.org/0009-0002-1558-4574)