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@@ -5,24 +5,29 @@ tags:
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  - imbalanced-classification
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  ---
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- ## Model description
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-
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- More information needed
 
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  ## Intended uses & limitations
 
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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  ## Training procedure
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-
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- ### Training hyperparameters
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-
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  The following hyperparameters were used during training:
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- - optimizer: {'name': 'Adam', 'learning_rate': 0.01, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
 
 
 
 
 
 
 
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  - training_precision: float32
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  ## Training Metrics
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  | 28| 0.0| 9.0| 5305.0| 222124.0| 408.0| 0.071| 0.978| 0.026| 9.0| 398.0| 56488.0| 66.0| 0.142| 0.88|
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  | 29| 0.0| 5.0| 4846.0| 222583.0| 412.0| 0.078| 0.988| 0.242| 6.0| 7883.0| 49003.0| 69.0| 0.009| 0.92|
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  | 30| 0.0| 5.0| 5193.0| 222236.0| 412.0| 0.074| 0.988| 0.026| 7.0| 449.0| 56437.0| 68.0| 0.132| 0.907|
 
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  ## Model Plot
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  <details>
 
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  - imbalanced-classification
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  ---
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+ ## Model Description
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+ ### Keras Implementation of Imbalanced classification: credit card fraud detection
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+ This repo contains the trained model of [Imbalanced classification: credit card fraud detection](https://keras.io/examples/structured_data/imbalanced_classification/).
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+ The full credit goes to: [fchollet](https://twitter.com/fchollet)
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  ## Intended uses & limitations
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+ - The trained model is used to detect of a specific transaction is fraudulent or not.
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+ ## Training dataset
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+ - [Credit Card Fraud Detection](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)
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+ - Due to the high imbalance of the target feature (417 frauds or 0.18% of total 284,807 samples), training weight was applied to reduce the False Negatives to the lowest level as possible.
 
 
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  ## Training procedure
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+ ### Training hyperparameter
 
 
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  The following hyperparameters were used during training:
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+ - optimizer: 'Adam'
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+ - learning_rate: 0.01
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+ - loss: 'binary_crossentropy'
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+ - epochs: 30
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+ - batch_size: 2048
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+ - beta_1: 0.9
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+ - beta_2: 0.999
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+ - epsilon: 1e-07
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  - training_precision: float32
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  ## Training Metrics
 
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  | 28| 0.0| 9.0| 5305.0| 222124.0| 408.0| 0.071| 0.978| 0.026| 9.0| 398.0| 56488.0| 66.0| 0.142| 0.88|
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  | 29| 0.0| 5.0| 4846.0| 222583.0| 412.0| 0.078| 0.988| 0.242| 6.0| 7883.0| 49003.0| 69.0| 0.009| 0.92|
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  | 30| 0.0| 5.0| 5193.0| 222236.0| 412.0| 0.074| 0.988| 0.026| 7.0| 449.0| 56437.0| 68.0| 0.132| 0.907|
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+
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  ## Model Plot
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  <details>