Upload train_model.py

train_model.py

@@ -0,0 +1,58 @@
+# pages/train_model.py
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from sklearn.neural_network import MLPClassifier
+from sklearn.pipeline import Pipeline
+import joblib
+
+# Load dataset
+data = pd.read_csv("size_dataset.csv")
+
+# Features
+X = data[[
+    "gender",
+    "shoulder",
+    "chest",
+    "waist",
+    "hip",
+    "chest_depth",
+    "hip_depth",
+    "height",
+    "weight"
+]]
+
+# Target
+y = data["size"]
+
+# Encode labels
+le = LabelEncoder()
+y_encoded = le.fit_transform(y)
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y_encoded, test_size=0.2, random_state=42
+)
+
+#  Scaling + MLP in Pipeline
+model = Pipeline([
+    ("scaler", StandardScaler()),
+    ("mlp", MLPClassifier(
+        hidden_layer_sizes=(128, 64, 32),
+        max_iter=2000,
+        random_state=42
+    ))
+])
+
+# Train
+model.fit(X_train, y_train)
+
+# Accuracy
+accuracy = model.score(X_test, y_test)
+print("Model Accuracy:", accuracy)
+
+# Save model + encoder
+joblib.dump(model, "size_model.pkl")
+joblib.dump(le, "label_encoder.pkl")
+
+print("Model saved successfully!")