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README.md
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## Model
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## Intended uses & limitations
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer:
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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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## 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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## Model Plot
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<details>
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