TOWARDSDATASCIENCE . COM {}

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Keywords {🔍}

features, feature, data, transaction, learning, values, machine, technique, encoding, target, missing, numeric, algorithms, techniques, fraud, number, transactions, outliers, distribution, risk, device, indicator, category, engineering, models, model, onehot, science, processing, information, case, card, min, time, problem, credit, nonnumeric, read, create, high, applying, observations, scaling, normal, algorithm, multiple, apply, count, lowvalue, common,

Topics {✒️}

current-day transaction count numeric-categorical form refers aggregated customer-level view sumit makashir machine learning algorithms machine learning models machine learning modeling artificial intelligence professionals machine learning algorithm python data science bottom chart shows recent login event supervised learning problem exploit feature engineering feature engineering principles tree-based algorithms high transaction count successive processing steps supervised learning problems support vector machines predictions manually based positive event probability highest eigenvalues captures past transaction behavior feature selection algorithms current transaction behavior data processing steps feature processing steps average transaction amount attempt multiple low structuring unstructured data principal component analysis box-cox transformation anomalous transaction behavior machine learning divide feature engineering current transaction date geographical region codes linkedin threads bluesky fraud risk levels multiple successive low significantly high ratio multiple parameter values numeric data points credit card transaction transaction zip code require additional modeling shapiro-wilk test ratio-based feature log transformation works

Questions {❓}

• So, then, how can we create these meaningful features that will maximize our model’s performance?

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