70 lines
2.4 KiB
Python
70 lines
2.4 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
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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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# Prepare the data
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X = data[['movie title', 'User Rating', 'Director', 'Top 5 Casts', 'Writer']]
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#y = data['Rating'].apply(lambda x: float(x) if isinstance(x, (int, float)) else np.nan) # Convert 'Rating' column to float data type
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#y = pd.Series(data['Rating'], dtype=float)
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y = data['Rating'].astype('float64')
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#y = y.fillna(y.mean())
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#y = np.array(y)
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print("Unique values in 'Rating' column:", data['Rating'].unique())
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print("Data type of 'Rating' column:", y.dtype)
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mean_rating = data['Rating'].mean()
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data['Rating'].fillna(mean_rating, inplace=True)
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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 = pd.DataFrame(mlb_genres.fit_transform(data['Generes']), columns=mlb_genres.classes_)
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mlb_keywords = MultiLabelBinarizer()
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X_keywords = pd.DataFrame(mlb_keywords.fit_transform(data['Plot Kyeword']), columns=mlb_keywords.classes_)
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mlb_casts = MultiLabelBinarizer()
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X_casts = pd.DataFrame(mlb_casts.fit_transform(data['Top 5 Casts'].astype(str)), columns=mlb_casts.classes_)
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# Concatenate the transformed columns with the remaining columns
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X = pd.concat([X, X_genres, X_keywords, X_casts], axis=1)
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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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# Convert the modified 'Rating' column to a numpy array
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y_train_updated = data['Rating'].to_numpy()
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# Update the 'y_train' array with the modified values
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y_train = y_train_updated
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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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print("Data type of 'Rating' column:", y_train.dtype)
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print("First few rows of 'y_train':", y_train[:10])
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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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