Delete engine_model.pkl

engine_model.pkl

@@ -1,28 +0,0 @@
-import pandas as pd
-import xgboost as xgb
-import joblib
-
-# Load the dataset directly from the web
-url = "https://raw.githubusercontent.com/datasets-machine-learning/nasa-turbofan-failure-prediction/master/data/train_FD001.txt"
-cols = ['unit', 'cycles', 'os1', 'os2', 'os3'] + [f's{i}' for i in range(1, 22)]
-df = pd.read_csv(url, sep='\s+', header=None, names=cols)
-
-# Calculate RUL (Remaining Useful Life)
-max_cycles = df.groupby('unit')['cycles'].max().reset_index()
-max_cycles.columns = ['unit', 'max_of_unit']
-df = df.merge(max_cycles, on='unit', how='left')
-df['RUL'] = df['max_of_unit'] - df['cycles']
-
-# We use exactly 15 features to match the app.py logic
-# (cycles + 14 sensor/settings columns)
-features = ['cycles', 's2', 's3', 's4', 's7', 's8', 's11', 's12', 's13', 's15', 's17', 's20', 's21', 'os1', 'os2']
-X = df[features]
-y = df['RUL']
-
-# Train the model
-model = xgb.XGBRegressor(n_estimators=100, learning_rate=0.1)
-model.fit(X, y)
-
-# SAVE THE FILE
-joblib.dump(model, 'engine_model.pkl')
-print("✅ Done! 'engine_model.pkl' created in your folder.")