58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
import pandas as pd
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import numpy as np
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import tensorflow as tf
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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# Step 1: Load the dataset
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data = pd.read_csv('25k_movies.csv.shuf')
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# Step 2: Preprocess the data
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features = ['Total Run Time', 'User Rating', 'Genres', 'Director Name', 'Writer Name']
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target = 'Movie Rating'
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data = data[features + [target]]
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# Handle missing values if any
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data = data.dropna()
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# Filter out rows with a different number of columns
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try:
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data = data[data.apply(lambda x: len(x) == 12, axis=1)]
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except pd.errors.ParserError as e:
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print(f"Error occurred while parsing the dataset: {e}")
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print("Dropping rows with inconsistent number of columns...")
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data = data[~data.apply(lambda x: len(x) != 12, axis=1)]
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# Convert categorical variables to numerical representations
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data = pd.get_dummies(data, columns=['Genres', 'Director Name', 'Writer Name'])
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# Split the data into features and target variables
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X = data.drop(target, axis=1)
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y = data[target]
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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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# Standardize the feature data
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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# Step 3: Build and train the neural network model
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model = tf.keras.Sequential([
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tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
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tf.keras.layers.Dense(32, activation='relu'),
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tf.keras.layers.Dense(1)
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])
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model.compile(optimizer='adam', loss='mean_squared_error')
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model.fit(X_train, y_train, epochs=10, batch_size=32)
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# Step 4: Evaluate the model
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y_pred = model.predict(X_test)
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mse = np.mean((y_pred - y_test)**2)
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print(f"Mean Squared Error (MSE): {mse}")
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