upd51

script5_1.py

@@ -22,6 +22,8 @@ y = data['Rating'].astype('float64')
 print("Unique values in 'Rating' column:", data['Rating'].unique())
 print("Data type of 'Rating' column:", y.dtype)
 
+mean_rating = data['Rating'].mean()
+data['Rating'].fillna(mean_rating, inplace=True)
 
 # Preprocess the data
 # Convert the categorical columns into numerical representations
@@ -40,6 +42,12 @@ X = pd.concat([X, X_genres, X_keywords, X_casts], axis=1)
 # Split the data into training and testing sets
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
+# Convert the modified 'Rating' column to a numpy array
+y_train_updated = data['Rating'].to_numpy()
+
+# Update the 'y_train' array with the modified values
+y_train = y_train_updated
+
 # Create the neural network model
 model = Sequential()
 model.add(Dense(32, activation='relu', input_dim=X.shape[1]))