43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import pandas as pd
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MultiLabelBinarizer
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense, Embedding, Flatten
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from tensorflow.keras.optimizers import Adam
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# Load the dataset from the CSV file
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data = pd.read_csv('data.csv')
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data = data.drop(index=5059)
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# Prepare the data
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X = data[['movie title', 'User Rating', 'Genres', 'Plot Keyword', 'Director', 'Top 5 Casts', 'Writer', 'Year']]
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y = data['Rating']
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# Preprocess the data
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# Convert the categorical columns into numerical representations
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mlb = MultiLabelBinarizer()
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X['Genres'] = mlb.fit_transform(X['Generes'])
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X['Plot Keyword'] = mlb.fit_transform(X['Plot Kyeword'])
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X['Top 5 Casts'] = mlb.fit_transform(X['Top 5 Casts'])
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# Split the data into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Create the neural network model
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model = Sequential()
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model.add(Embedding(input_dim=len(mlb.classes_), output_dim=10, input_length=X.shape[1]))
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model.add(Flatten())
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model.add(Dense(32, activation='relu'))
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model.add(Dense(1))
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# Compile the model
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model.compile(optimizer=Adam(), loss='mse')
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# Train the model
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model.fit(X_train, y_train, batch_size=64, epochs=10, validation_data=(X_test, y_test))
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# Evaluate the model
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mse = model.evaluate(X_test, y_test)
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print("Mean Squared Error:", mse) |