TensorFlow Machine Learning Projects

Defining and training a fraud detection model 

The following are the steps for defining and training the model:

1. Transform 'Amount' by removing the mean and scaling it to the unit's variance:

def preprocess_data(data):data = data.drop(['Time'], axis=1)data['Amount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))return data

Note that we use the StandardScaler utility from scikit-learn for this purpose.

2. To model our dataset, split it into train and test data, with train consisting of only non-fraudulent transaction and test consisting of both fraudulent and non-fraudulent transactions:

def get_train_and_test_data(processed_data):X_train, X_test = train_test_split(processed_data, test_size=0.25, random_state=RANDOM_SEED) ...