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Non_Payment_Credit_Risk_Automation
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Non_Payment_Credit_Risk_Automation
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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(),
    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",
    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',80000,7.9,'Y',8],
[52,20000,"MORTGAGE",10,'PERSONAL','F',100000,15.25,'Y',5]]
)
demo.launch(debug=True,favicon_path= "Non_payment_logo.jpg")