diff --git a/Jenkinsfile-lab5 b/Jenkinsfile-lab5
index 02b8094..631bb73 100644
--- a/Jenkinsfile-lab5
+++ b/Jenkinsfile-lab5
@@ -39,9 +39,9 @@ pipeline {
                 echo("run data script")
                 //sh "source docker_ium/bin/activate"
                 sh "ls -a"
-                sh "chmod u+x script5.py"
+                sh "chmod u+x script5_1.py"
                 //sh "pip3 show pandas"
-                sh "python3 script5.py"
+                sh "python3 script5_1.py"
                 
             }
         }
diff --git a/script5_1.py b/script5_1.py
new file mode 100644
index 0000000..7756243
--- /dev/null
+++ b/script5_1.py
@@ -0,0 +1,49 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MultiLabelBinarizer
+from tensorflow.keras.models import Sequential
+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')
+
+
+
+# Prepare the data
+X = data[['movie title', 'User Rating', 'Director', 'Top 5 Casts', 'Writer', 'year']]
+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()
+
+mlb_keywords = MultiLabelBinarizer()
+X_keywords = mlb_keywords.fit_transform(data['Plot Kyeword'])
+X.loc[:, 'Plot Kyeword'] = X_keywords.tolist()
+
+mlb_casts = MultiLabelBinarizer()
+X_casts = mlb_casts.fit_transform(data['Top 5 Casts'].astype(str))
+X.loc[:, 'Top 5 Casts'] = X_casts.tolist()
+
+# 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)
+
+# Create the neural network model
+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')
+
+# Train the model
+model.fit(X_train, y_train, batch_size=64, epochs=10, validation_data=(X_test, y_test))
+
+# Evaluate the model
+mse = model.evaluate(X_test, y_test)
+print("Mean Squared Error:", mse)