diff --git a/script4.py b/script4.py
index f6a11ff..e6b3224 100644
--- a/script4.py
+++ b/script4.py
@@ -5,7 +5,8 @@ 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', error_bad_lines=False)
+data = pd.read_csv('25k_movies.csv.shuf')
+
 
 # Step 2: Preprocess the data
 features = ['Total Run Time', 'User Rating', 'Genres', 'Director Name', 'Writer Name']
@@ -16,7 +17,12 @@ data = data[features + [target]]
 data = data.dropna()
 
 # Filter out rows with a different number of columns
-data = data[data.apply(lambda x: len(x) == 12, axis=1)]
+try:
+    data = data[data.apply(lambda x: len(x) == 12, axis=1)]
+except pd.errors.ParserError as e:
+    print(f"Error occurred while parsing the dataset: {e}")
+    print("Dropping rows with inconsistent number of columns...")
+    data = data[data.apply(lambda x: len(x) == 12 if isinstance(x, list) else True, axis=1)]
 
 # Convert categorical variables to numerical representations
 data = pd.get_dummies(data, columns=['Genres', 'Director Name', 'Writer Name'])