41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
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import tensorflow as tf
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import pandas as pd
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from sklearn.preprocessing import LabelEncoder, StandardScaler
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from sklearn.model_selection import train_test_split
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import json
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df = pd.read_csv('OrangeQualityData.csv')
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encoder = LabelEncoder()
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df["Color"] = encoder.fit_transform(df["Color"])
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df["Variety"] = encoder.fit_transform(df["Variety"])
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df["Blemishes"] = df["Blemishes (Y/N)"].apply(lambda x: 1 if x.startswith("Y") else 0)
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df.drop(columns=["Blemishes (Y/N)"], inplace=True)
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X = df.drop(columns=["Quality (1-5)"])
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y = df["Quality (1-5)"]
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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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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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model = tf.keras.Sequential([
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tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train_scaled.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='sgd', loss='mse')
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history = model.fit(X_train_scaled, y_train, epochs=100, verbose=0, validation_data=(X_test_scaled, y_test))
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model.save('orange_quality_model_tf.h5')
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predictions = model.predict(X_test_scaled)
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with open('predictions_tf.json', 'w') as f:
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json.dump(predictions.tolist(), f, indent=4)
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