ium 5

model.py

@@ -26,7 +26,7 @@ preprocessor = ColumnTransformer(
 pipeline = Pipeline(steps=[
     ('preprocessor', preprocessor),
     ('model', Sequential([
-        Dense(64, activation='relu', input_dim=4),  # Liczba wejść musi zgadzać się z wynikowym wymiarem preprocessingu
+        Dense(64, activation='relu', input_dim=4),
         Dense(64, activation='relu'),
         Dense(1)
     ]))

predict.py

@@ -8,7 +8,7 @@ from sklearn.model_selection import train_test_split
 loaded_model = tf.keras.models.load_model('powerlifting_model.h5')
 
 data = pd.read_csv('openpowerlifting.csv')
-data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()  # Usunięcie wierszy z brakującymi danymi
+data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
 features = data[['Sex', 'Age', 'BodyweightKg']]
 target = data['TotalKg']