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import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import gradio as gr
df = pd.read_csv("credit_risk_dataset.csv")
df = df.dropna()
df.columns
X =df.drop("loan_status", axis = 1)
y = df['loan_status']
categorical_features = ["person_home_ownership", "loan_intent", "loan_grade", "cb_person_default_on_file"]
X = pd.get_dummies(X, categorical_features)
X.columns
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
X_train.head()
from sklearn.preprocessing import StandardScaler
scaler_normal = StandardScaler()
def scaler(data, runtime = False):
normal_col = ['person_income','person_age','person_emp_length', 'loan_amnt','loan_int_rate','cb_person_cred_hist_length','loan_percent_income']
if(runtime == False):
data.loc[:,normal_col] = scaler_normal.fit_transform(data.loc[:,normal_col])
else:
data.loc[:,normal_col] = scaler_normal.transform(data.loc[:,normal_col])
return data
X_train = scaler(X_train)
X_test = scaler(X_test, True)
rf_model = RandomForestClassifier(max_depth = 5)
rf_model.fit(X_train, y_train)
features = {
"person_home_ownership": ['MORTGAGE', 'OTHER','OWN', 'RENT',],
"loan_intent": ['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE'],
"loan_grade": ['A','B', 'C', 'D', 'E','F', 'G'],
"cb_person_default_on_file": ['N', 'Y']
}
def preprocess(model_input):
for feature in features:
for option in features[feature]:
selection = model_input[feature]
if option is selection:
model_input[f'{feature}_{option}'] = 1
else:
model_input[f'{feature}_{option}'] = 0
model_input.drop([_ for _ in features], inplace = True, axis = 1)
return model_input
def credit_run(person_age, person_income, person_home_ownership,
person_emp_length, loan_intent, loan_grade, loan_amnt,
loan_int_rate, cb_person_default_on_file, cb_person_cred_hist_length):
model_input = preprocess(
pd.DataFrame( { 'person_age': person_age,
'person_income': person_income,
'person_home_ownership': person_home_ownership,
'person_emp_length': person_emp_length,
'loan_intent': loan_intent,
'loan_grade': loan_grade,
'loan_amnt': loan_amnt,
'loan_int_rate': loan_int_rate,
'loan_percent_income': loan_amnt / person_income,
'cb_person_default_on_file': cb_person_default_on_file,
'cb_person_cred_hist_length': cb_person_cred_hist_length
}, index = [0]
))
out = rf_model.predict(model_input)
return "High risk of defaulting" if out[0] == 1 else "Low risk of defaulting"
demo = gr.Interface(
fn = credit_run,
inputs = [
gr.Slider(label="Person Age(In Years)", minimum=18, maximum=90, step=1),
gr.Number(label="Person Income(per month)"),
gr.Radio(['MORTGAGE', 'OTHER','OWN', 'RENT'],label="Home Ownership Status"),
gr.Slider(label="Pererson Emp Length(In Years)", minimum=0, maximum=60, step=1),
gr.Radio(['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE'],label="Credit Intent"),
gr.Radio(['A','B', 'C', 'D', 'E','F', 'G'],label="Type Of Credit"),
gr.Number(label="Credit Amount"),
gr.Number(label="Credit Interest Rate"),
gr.Radio(['N', 'Y'],label="Person Defaulted in History"),
gr.Number(label="Person's Credit History Length"),
],
outputs = gr.Radio(['Low risk of defaulting', 'High risk of defaulting']),
title = "Non Payment Credit Risk Predictor",
theme=gr.themes.Soft(),
examples = [[23,25000,'RENT',2,'EDUCATION','A',30000,8.9,'N',6],
[34,50000,'OWN',1,'MEDICAL','B',62000,10.65,'N',3],
[32,30000,'RENT',5,'VENTURE','D',100000,8.65,'Y',5],
[42,30000,"MORTGAGE",12,'HOMEIMPROVEMENT','C',800000,7.9,'Y',8],
[52,20000,"MORTGAGE",10,'PERSONAL','F',100000,15.25,'Y',5]]
)
demo.launch(debug=True) |