diff --git a/script4.py b/script4.py
index 6959ae8..f6a11ff 100644
--- a/script4.py
+++ b/script4.py
@@ -5,8 +5,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 
 # Step 1: Load the dataset
-data = pd.read_csv('25k_movies.csv.shuf')
-# Replace 'path_to_dataset.csv' with the actual path to your dataset file
+data = pd.read_csv('25k_movies.csv.shuf', error_bad_lines=False)
 
 # Step 2: Preprocess the data
 features = ['Total Run Time', 'User Rating', 'Genres', 'Director Name', 'Writer Name']
@@ -16,6 +15,9 @@ data = data[features + [target]]
 # Handle missing values if any
 data = data.dropna()
 
+# Filter out rows with a different number of columns
+data = data[data.apply(lambda x: len(x) == 12, axis=1)]
+
 # Convert categorical variables to numerical representations
 data = pd.get_dummies(data, columns=['Genres', 'Director Name', 'Writer Name'])
 
@@ -46,4 +48,4 @@ model.fit(X_train, y_train, epochs=10, batch_size=32)
 y_pred = model.predict(X_test)
 mse = np.mean((y_pred - y_test)**2)
 
-print(f"Mean Squared Error (MSE): {mse}")
\ No newline at end of file
+print(f"Mean Squared Error (MSE): {mse}")