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b077775 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | from sklearn.linear_model import LogisticRegression
import pandas as pd
def compute_credit_score(
coef_dict,
num_credit_card, changed_credit_limit, delay_from_due_date, interest_rate,
outstanding_debt, credit_mix_good, credit_mix_standard
):
"""Computes a credit score based on the logistic regression coefficients and input features.
Args:
coef_dict (dict): A dictionary containing the logistic regression coefficients.
num_credit_card (float): The number of credit cards.
changed_credit_limit (float): The change in credit limit.
delay_from_due_date (float): The delay from the due date.
interest_rate (float): The interest rate.
outstanding_debt (float): The outstanding debt.
credit_mix_good (float): The proportion of good credit mix.
credit_mix_standard (float): The proportion of standard credit mix.
Returns:
float: The computed credit score.
"""
score = (
coef_dict['Num_Credit_Card'] * num_credit_card +
coef_dict['Changed_Credit_Limit'] * changed_credit_limit +
coef_dict['Delay_from_due_date'] * delay_from_due_date +
coef_dict['Interest_Rate'] * interest_rate +
coef_dict['Outstanding_Debt'] * outstanding_debt +
coef_dict['Credit_Mix_Good'] * credit_mix_good +
coef_dict['Credit_Mix_Standard'] * credit_mix_standard
)
return score
def real_data_credit_model(X_train, y_train, X_test):
"""
Trains a logistic regression model on the real training data and evaluates it on the real testing data.
Args:
X_train (pd.DataFrame): The training features.
y_train (pd.Series): The training target variable.
X_test (pd.DataFrame): The testing features.
Returns:
tuple: A tuple containing the computed credit scores and classifications for the testing data.
"""
model = LogisticRegression(
max_iter=1000,
class_weight='balanced'
)
model.fit(X_train, y_train)
coefficients = model.coef_[0]
coef_dict = dict(zip(X_train.columns, coefficients))
score = pd.Series([
compute_credit_score(
coef_dict,
row['Num_Credit_Card'], row['Changed_Credit_Limit'], row['Delay_from_due_date'],
row['Interest_Rate'], row['Outstanding_Debt'], row['Credit_Mix_Good'], row['Credit_Mix_Standard']
) for _, row in X_test.iterrows()
])
classification = model.predict(X_test)
return score, classification
def synthetic_data_credit_model(X_train, y_train, X_test):
"""Trains a logistic regression model on the synthetic training data and evaluates it on the real testing data.
Args:
X_train (pd.DataFrame): The synthetic training features.
y_train (pd.Series): The synthetic training target variable.
X_test (pd.DataFrame): The real testing features.
Returns:
tuple: A tuple containing the computed credit scores and classifications for the testing data.
"""
model = LogisticRegression(
max_iter=1_000,
)
model.fit(X_train, y_train)
coefficients = model.coef_[0]
coef_dict = dict(zip(X_train.columns, coefficients))
score = pd.Series([
compute_credit_score(
coef_dict,
row['Num_Credit_Card'], row['Changed_Credit_Limit'], row['Delay_from_due_date'],
row['Interest_Rate'], row['Outstanding_Debt'], row['Credit_Mix_Good'], row['Credit_Mix_Standard']
) for _, row in X_test.iterrows()
])
classification = model.predict(X_test)
return score, classification |