| 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", "loan_percent_income"], 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) | |
| 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'] | |
| 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) | |
| y_predict = rf_model.predict(X_test) | |
| y_predict | |
| 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_emp_length,person_home_ownership,cb_person_default_on_file,loan_intent,loan_grade,person_income, loan_amnt, | |
| loan_int_rate, 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, | |
| '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" | |
| import gradio as gr | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=1,min_width=400): | |
| gr.Image("Non_Payment_Logo.png").style(height='5') | |
| with gr.Column(scale=1,min_width=600): | |
| person_age=gr.Slider(label="Customer Age(In Years)", minimum=18, maximum=90, step=1) | |
| Person_Emp_Length=gr.Slider(label="Customer Employement Length(In Years)", minimum=0, maximum=60, step=1) | |
| with gr.Column(scale=2,min_width=600): | |
| with gr.Row(): | |
| with gr.Column(scale=1,min_width=500): | |
| Home_Ownership_Status=gr.Radio(['MORTGAGE', 'OTHER','OWN', 'RENT'],label="Home Ownership Status") | |
| with gr.Column(scale=2,min_width=100): | |
| Person_Defaulted_in_History=gr.Radio(['N', 'Y'],label="Missed Payment in History") | |
| with gr.Row(): | |
| with gr.Column(scale=3,min_width=300): | |
| Credit_Intent=gr.Dropdown(['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE'],label="Intent") | |
| with gr.Column(scale=4,min_width=300): | |
| Type_Of_Credit=gr.Dropdown(['A','B', 'C', 'D', 'E','F', 'G'],label="Type Of Credit") | |
| with gr.Row(): | |
| with gr.Column(scale=3,min_width=300): | |
| Person_Income=gr.Number(label="Customer Income(per month)") | |
| with gr.Column(scale=4,min_width=300): | |
| Credit_Amount=gr.Number(label="Premium Amount") | |
| with gr.Row(): | |
| with gr.Column(scale=3,min_width=300): | |
| Interest_Rate=gr.Number(label="Interest Rate") | |
| with gr.Column(scale=4,min_width=300): | |
| Person_Credit_History_Length=gr.Number(label="Customer's Credit History Length") | |
| with gr.Row(): | |
| with gr.Column(): | |
| default= gr.Radio(['Low risk of defaulting', 'High risk of defaulting'],label="Chances Of Defaulting") | |
| btn = gr.Button("PREDICT") | |
| btn.click(fn=credit_run, inputs=[person_age,Person_Emp_Length,Home_Ownership_Status,Person_Defaulted_in_History,Credit_Intent,Type_Of_Credit,Person_Income,Credit_Amount,Interest_Rate,Person_Credit_History_Length], outputs=[default]) | |
| gr.Examples( | |
| [["23","2","RENT","N","EDUCATION","A","12000","30000","8.9","6"], | |
| ["35","10","OWN","N","MEDICAL","C","20000","40000","8.3","8"], | |
| ["28","6","RENT","N","VENTURE","B","32000","30000","8.2","6"], | |
| ["32","10","MORTGAGE","Y","HOMEIMPROVEMENT","E","20000","600000","8.6","8"], | |
| ["41","18","OWN","Y","PERSONAL","A","10000","300000","14.3","4"], | |
| ["30","7","OTHER","Y","MEDICAL","C","13000","1000000","9.5","10"]], | |
| inputs=[person_age,Person_Emp_Length,Home_Ownership_Status,Person_Defaulted_in_History,Credit_Intent,Type_Of_Credit,Person_Income,Credit_Amount,Interest_Rate,Person_Credit_History_Length] | |
| ) | |
| demo.launch(debug=True) |