diff --git a/script5_2.py b/script5_2.py
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+++ b/script5_2.py
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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from keras.models import Sequential
+from keras.layers import Dense
+
+# Load the dataset
+df = pd.read_csv('movie_dataset.csv')
+
+# Select the relevant columns (e.g., 'Rating' and 'Writer')
+data = df[['Rating', 'Writer']]
+
+# Drop rows with missing values
+data = data.dropna()
+
+# Convert the 'Writer' column to numeric using label encoding
+encoder = LabelEncoder()
+data['Writer'] = encoder.fit_transform(data['Writer'])
+
+# Split the data into training and testing sets
+X = data['Writer']
+y = data['Rating']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Create the neural network model
+model = Sequential()
+model.add(Dense(64, activation='relu', input_dim=1))
+model.add(Dense(1))
+
+model.compile(loss='mean_squared_error', optimizer='adam')
+
+# Train the model
+model.fit(X_train, y_train, batch_size=64, epochs=10, validation_data=(X_test, y_test))
+
+# Make predictions on new data
+new_writer = 'John Smith'
+new_writer_encoded = encoder.transform([new_writer])
+
+rating_prediction = model.predict(new_writer_encoded)
+print("Predicted rating for the writer 'John Smith':", rating_prediction)
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