50 lines
1.6 KiB
Python
50 lines
1.6 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
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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', on_bad_lines='skip', engine='python')
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# Prepare the data
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X = data[['movie title', 'User Rating', '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_genres = MultiLabelBinarizer()
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X_genres = mlb_genres.fit_transform(data['Generes'])
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X.loc[:, 'Generes'] = X_genres.tolist()
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mlb_keywords = MultiLabelBinarizer()
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X_keywords = mlb_keywords.fit_transform(data['Plot Kyeword'])
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X.loc[:, 'Plot Kyeword'] = X_keywords.tolist()
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mlb_casts = MultiLabelBinarizer()
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X_casts = mlb_casts.fit_transform(data['Top 5 Casts'].astype(str))
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X.loc[:, 'Top 5 Casts'] = X_casts.tolist()
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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(Dense(32, activation='relu', input_dim=X.shape[1]))
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model.add(Dense(16, 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)
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