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README.md

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+
+---
+tags:
+- tabular-classification
+- scikit-learn
+- xgboost
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: XGBoost Predictive Maintenance Model
+  results:
+    - task:
+        type: tabular-classification
+        name: Tabular Classification
+      dataset:
+        name: Engine Sensor Data
+        type: custom
+      metrics:
+        - type: accuracy
+          value: 0.6647
+        - type: precision
+          value: 0.6479
+        - type: recall
+          value: 0.6647
+        - type: f1
+          value: 0.6358
+---
+# XGBoost Predictive Maintenance Model
+
+This model is an XGBoost Classifier trained to predict engine condition (healthy or failing) based on sensor data. It's part of a predictive maintenance system.
+
+## Model Details
+
+-   **Model Type:** XGBoost Classifier
+-   **Task:** Binary Classification (Engine Condition: 0 = Healthy, 1 = Failing)
+-   **Best Parameters (from GridSearchCV):**
+    ```json
+    {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'feature_types': None, 'feature_weights': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 6, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 200, 'n_jobs': None, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None, 'use_label_encoder': False}
+    ```
+
+## Training Data
+
+The model was trained on the `Engine Sensor Data` dataset, which includes various engine sensor readings such as RPM, oil pressure, fuel pressure, coolant pressure, and temperatures.
+
+-   **Features (X_train):** `X_train.csv` (scaled numerical features)
+-   **Target (y_train):** `y_train.csv` (Engine Condition)
+
+## Evaluation Results
+
+The model was evaluated on a held-out test set (`X_test.csv`, `y_test.csv`).
+
+-   **Accuracy:** 0.6647
+-   **Precision (weighted):** 0.6479
+-   **Recall (weighted):** 0.6647
+-   **F1-score (weighted):** 0.6358
+
+## Usage
+
+This model can be used to predict the `Engine Condition` for new engine sensor data. It was trained using `scikit-learn` and `xgboost` libraries.
+
+To load and use the model:
+
+```python
+import joblib
+
+# Load the model
+model = joblib.load('best_xgboost_model.joblib')
+
+# Make predictions (example with dummy data)
+# from sklearn.preprocessing import StandardScaler
+# scaler = StandardScaler()
+# new_data = scaler.fit_transform([[750, 3.0, 7.0, 2.5, 78.0, 80.0]]) # Example scaled data matching training features
+# prediction = model.predict(new_data)
+# print("Predicted Engine Condition: [predicted value]") # Modified to avoid NameError
+```