Create train_model.py

train_model.py

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+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+import joblib
+
+# Load your dataset
+df = pd.read_csv("mask_dataset.csv")
+
+# Encode features
+face_shape_encoder = LabelEncoder()
+skin_tone_encoder = LabelEncoder()
+style_encoder = LabelEncoder()
+
+X = pd.DataFrame({
+    'face_shape': face_shape_encoder.fit_transform(df['face_shape']),
+    'skin_tone': skin_tone_encoder.fit_transform(df['skin_tone'])
+})
+
+y = style_encoder.fit_transform(df['recommended_style'])
+
+# Train the model
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X, y)
+
+# Save the model and encoders
+joblib.dump(model, "mask_model.pkl")
+joblib.dump(face_shape_encoder, "face_shape_encoder.pkl")
+joblib.dump(skin_tone_encoder, "skin_tone_encoder.pkl")
+joblib.dump(style_encoder, "style_encoder.pkl")
+
+print("✅ Training complete. Model and encoders saved.")