add nn script

Jenkinsfile-lab5

@@ -39,9 +39,9 @@ pipeline {
                 echo("run data script")
                 //sh "source docker_ium/bin/activate"
                 sh "ls -a"
-                sh "chmod u+x script2.py"
+                sh "chmod u+x script4.py"
                 //sh "pip3 show pandas"
-                sh "python3 script3.py"
+                sh "python3 script4.py"
                 
             }
         }

script3.py

@@ -9,7 +9,7 @@ from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.utils import to_categorical
 
 # Step 1: Data Preprocessing
-df = pd.read_csv('25k_movies.csv.shuf')  # Replace with the actual file name or path
+df = pd.read_csv('25k_movies.csv.shuf')  
 text_data = df['review']
 labels = df['sentiment']
 

script4.py

@@ -0,0 +1,49 @@
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+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
+
+# Step 2: Preprocess the data
+features = ['Total Run Time', 'User Rating', 'Genres', 'Director Name', 'Writer Name']
+target = 'Movie Rating'
+data = data[features + [target]]
+
+# Handle missing values if any
+data = data.dropna()
+
+# Convert categorical variables to numerical representations
+data = pd.get_dummies(data, columns=['Genres', 'Director Name', 'Writer Name'])
+
+# Split the data into features and target variables
+X = data.drop(target, axis=1)
+y = data[target]
+
+# 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)
+
+# Standardize the feature data
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Step 3: Build and train the neural network model
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
+    tf.keras.layers.Dense(32, activation='relu'),
+    tf.keras.layers.Dense(1)
+])
+
+model.compile(optimizer='adam', loss='mean_squared_error')
+
+model.fit(X_train, y_train, epochs=10, batch_size=32)
+
+# Step 4: Evaluate the model
+y_pred = model.predict(X_test)
+mse = np.mean((y_pred - y_test)**2)
+
+print(f"Mean Squared Error (MSE): {mse}")