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inference.py

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+import joblib
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+
+# Load the trained model
+model = joblib.load('time_slot_model.joblib')
+
+# Load the LabelEncoder used in training (if applicable)
+# label_encoder = joblib.load('label_encoder.joblib')  # Uncomment if you used a LabelEncoder
+
+def preprocess_input(user_pref):
+    # Example preprocessing; adjust based on your actual preprocessing steps
+    user_input = pd.get_dummies(pd.Series([user_pref]), prefix='Pref')
+    return user_input
+
+def predict(user_pref):
+    # Preprocess the input
+    user_input = preprocess_input(user_pref)
+    
+    # Ensure the input has the same columns as the training data
+    user_input = user_input.reindex(columns=model.feature_importances_, fill_value=0)
+    
+    # Make a prediction
+    prediction = model.predict(user_input)
+    return prediction[0]
+
+# Example usage:
+# user_pref = 'noon'  # Replace with actual user preference
+# prediction = predict(user_pref)
+# print(f"Predicted next rescheduled time: {prediction}")

requirements.txt

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+pandas
+scikit-learn
+joblib

time_slot_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a54db130c87e319dd2680f789c20d6058064cc3004c061483d4b13b1f4ed61ab
+size 102729