Create create_model.py

create_model.py

@@ -0,0 +1,35 @@
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import StandardScaler
+import pandas as pd
+import joblib
+import numpy as np
+
+# Create sample hospital data
+data = {
+    'age': [65, 72, 58, 81, 45],
+    'time_in_hospital': [5, 8, 3, 12, 4],
+    'num_lab_procedures': [45, 32, 28, 51, 38],
+    'num_medications': [15, 22, 8, 18, 12],
+    'readmitted': [1, 1, 0, 1, 0]
+}
+df = pd.DataFrame(data)
+
+# Prepare features and target
+X = df.drop('readmitted', axis=1)
+y = df['readmitted']
+
+# Create and train a simple model
+model = RandomForestClassifier(n_estimators=10)
+model.fit(X, y)
+
+# Create and fit a preprocessor
+preprocessor = StandardScaler()
+preprocessor.fit(X)
+
+# Save valid files
+joblib.dump(model, 'model.joblib', compress=3)
+joblib.dump(preprocessor, 'preprocessor.pkl', compress=3)
+
+print("Created valid model.joblib and preprocessor.pkl files!")
+print(f"Model size: {os.path.getsize('model.joblib')} bytes")
+print(f"Preprocessor size: {os.path.getsize('preprocessor.pkl')} bytes")