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style: format code with Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, Google Java Format, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt, Scalafmt, StandardJS, StandardRB, swift-format and Yapf #155

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merged 2 commits into from
May 12, 2024
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style: format code with Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, Google Java Format, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt, Scalafmt, StandardJS, StandardRB, swift-format and Yapf #155

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style: format code with Autopep8, Black, ClangFormat, dotnet-format, …
deepsource-autofix[bot] May 11, 2024
83e9075
Merge branch 'main' into deepsource-transform-1ac8796d
KOSASIH May 12, 2024
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17 changes: 11 additions & 6 deletions ai/risk_assessment/data_preparation.py
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Original file line number Diff line number Diff line change
@@ -1,14 +1,16 @@
import pandas as pd
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split


def load_data(data_path):
"""
Loads the data from the given file path.
"""
data = pd.read_csv(data_path)
return data


def preprocess_data(data):
"""
Preprocesses the data by cleaning, transforming, and encoding the features.
Expand All @@ -17,18 +19,21 @@ def preprocess_data(data):
data = data.dropna()

# Transform the data
data['amount'] = np.log(data['amount'])
data["amount"] = np.log(data["amount"])

# Encode the categorical features
data = pd.get_dummies(data, columns=['category', 'merchant'])
data = pd.get_dummies(data, columns=["category", "merchant"])

return data


def split_data(data, test_size=0.2):
"""
Splits the data into training and testing sets.
"""
X = data.drop('fraud', axis=1)
y = data['fraud']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
X = data.drop("fraud", axis=1)
y = data["fraud"]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=test_size, random_state=42
)
return X_train, X_test, y_train, y_test
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