diff --git a/script5_1.py b/script5_1.py
index 7756243..2d46019 100644
--- a/script5_1.py
+++ b/script5_1.py
@@ -7,27 +7,28 @@ from tensorflow.keras.layers import Dense
 from tensorflow.keras.optimizers import Adam
 
 # Load the dataset from the CSV file
-data = pd.read_csv('data.csv', on_bad_lines='skip', engine='python')
-
+data = pd.read_csv('data.csv')
 
+# Drop a specific row
+data = data.drop(index=5059)
 
 # Prepare the data
-X = data[['movie title', 'User Rating', 'Director', 'Top 5 Casts', 'Writer', 'year']]
+X = data[['movie title', 'User Rating', 'Director', 'Top 5 Casts', 'Writer']]
 y = data['Rating']
 
 # Preprocess the data
 # Convert the categorical columns into numerical representations
 mlb_genres = MultiLabelBinarizer()
-X_genres = mlb_genres.fit_transform(data['Generes'])
-X.loc[:, 'Generes'] = X_genres.tolist()
+X_genres = pd.DataFrame(mlb_genres.fit_transform(data['Generes']), columns=mlb_genres.classes_)
 
 mlb_keywords = MultiLabelBinarizer()
-X_keywords = mlb_keywords.fit_transform(data['Plot Kyeword'])
-X.loc[:, 'Plot Kyeword'] = X_keywords.tolist()
+X_keywords = pd.DataFrame(mlb_keywords.fit_transform(data['Plot Kyeword']), columns=mlb_keywords.classes_)
 
 mlb_casts = MultiLabelBinarizer()
-X_casts = mlb_casts.fit_transform(data['Top 5 Casts'].astype(str))
-X.loc[:, 'Top 5 Casts'] = X_casts.tolist()
+X_casts = pd.DataFrame(mlb_casts.fit_transform(data['Top 5 Casts'].astype(str)), columns=mlb_casts.classes_)
+
+# Concatenate the transformed columns with the remaining columns
+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)
@@ -37,7 +38,7 @@ model = Sequential()
 model.add(Dense(32, activation='relu', input_dim=X.shape[1]))
 model.add(Dense(16, activation='relu'))
 model.add(Dense(1))
- 
+
 # Compile the model
 model.compile(optimizer=Adam(), loss='mse')