50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
import pandas as pd
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from tensorflow import keras
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from tensorflow.keras import layers
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class RegressionModel:
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def __init__(self, optimizer="adam", loss="mean_squared_error"):
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self.model = keras.Sequential([
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layers.Input(shape=(5,)), # Input layer
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layers.Dense(32, activation='relu'), # Hidden layer with 32 neurons and ReLU activation
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layers.Dense(1) # Output layer with a single neuron (for regression)
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])
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self.optimizer = optimizer
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self.loss = loss
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self.X_train = None
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self.X_test = None
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self.y_train = None
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self.y_test = None
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def load_data(self, train_path, test_path):
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data_train = pd.read_csv(train_path)
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data_test = pd.read_csv(test_path)
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self.X_train = data_train.drop("Performance Index", axis=1)
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self.y_train = data_train["Performance Index"]
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self.X_test = data_test.drop("Performance Index", axis=1)
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self.y_test = data_test["Performance Index"]
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def train(self, epochs=30):
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self.model.compile(optimizer=self.optimizer, loss=self.loss)
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self.model.fit(self.X_train, self.y_train, epochs=epochs, batch_size=32, validation_data=(self.X_test, self.y_test))
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def predict(self, data):
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prediction = self.model.predict(data)
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return prediction
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def evaluate(self):
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test_loss = self.model.evaluate(self.X_test, self.y_test)
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print(f"Test Loss: {test_loss:.4f}")
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def save_model(self):
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self.model.save("model.keras")
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model = RegressionModel()
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model.load_data("df_train.csv", "df_test.csv")
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model.train()
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model.evaluate()
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model.save_model()
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