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<h2>Custom Preprocessing Pipelines</h2>

<p>Create specialized preprocessing flows for your features with complete control over transformations</p>

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<p>KDP allows you to define custom preprocessing pipelines for your features, giving you complete control over how each feature is processed before being fed into your model. This is particularly useful when the standard preprocessing options don't meet your specific needs.</p>

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<h3>Specific Transformations</h3>

<p>Define custom preprocessing steps not covered by built-in options</p>

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<h3>Combined Techniques</h3>

<p>Combine multiple preprocessing techniques in a single pipeline</p>

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<h3>Domain-Specific</h3>

<p>Handle specialized data with custom preprocessing logic</p>

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<h3>Novel Approaches</h3>

<p>Experiment with new preprocessing methods</p>

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<h3>Legacy Integration</h3>

<p>Incorporate existing preprocessing logic</p>

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from kdp.features import NumericalFeature, FeatureType

from tensorflow.keras.layers import Normalization, Dense, Activation

log_transform_feature = NumericalFeature(

name="revenue",

feature_type=FeatureType.FLOAT_NORMALIZED,

preprocessors=[

"Lambda", # Using a standard Keras layer by name

"Dense", # Another standard layer

"ReLU" # Activation function

],

# Parameters for the layers

function=lambda x: tf.math.log1p(x), # For Lambda layer

units=16, # For Dense layer

)