nn new try

script5.py

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+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, Embedding, Flatten
+from tensorflow.keras.optimizers import Adam
+
+# Load the dataset from the CSV file
+data = pd.read_csv('data.csv')
+
+# Prepare the data
+X = data[['movie title', 'User Rating', 'Genres', 'Plot Keyword', 'Director', 'Top 5 Casts', 'Writer', 'Year']]
+y = data['Rating']
+
+# Preprocess the data
+# Convert the categorical columns into numerical representations
+mlb = MultiLabelBinarizer()
+X['Genres'] = mlb.fit_transform(X['Generes'])
+X['Plot Keyword'] = mlb.fit_transform(X['Plot Kyeword'])
+X['Top 5 Casts'] = mlb.fit_transform(X['Top 5 Casts'])
+
+# 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(Embedding(input_dim=len(mlb.classes_), output_dim=10, input_length=X.shape[1]))
+model.add(Flatten())
+model.add(Dense(32, 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)