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import tensorflow as tf | |
from sklearn.model_selection import train_test_split | |
def train_model(processed_data): | |
# Split data into training and testing sets | |
X_train, X_test, y_train, y_test = train_test_split(processed_data.drop("target", axis=1), processed_data["target"], test_size=0.2, random_state=42) | |
# Define and train model | |
model = tf.keras.models.Sequential([ | |
tf.keras.layers.Dense(64, activation="relu", input_shape=(X_train.shape[1],)), | |
tf.keras.layers.Dense(64, activation="relu"), | |
tf.keras.layers.Dense(1) | |
]) | |
model.compile(optimizer="adam", loss="mean_squared_error") | |
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test)) | |
return model |