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import numpy as np |
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import pandas as pd |
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from sklearn.ensemble import RandomForestClassifier |
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import gradio as gr |
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df = pd.read_csv("credit_risk_dataset.csv") |
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df = df.dropna() |
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df.columns |
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X =df.drop(["loan_status", "loan_percent_income"], axis = 1) |
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y = df['loan_status'] |
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categorical_features = ["person_home_ownership", "loan_intent", "loan_grade", "cb_person_default_on_file"] |
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X = pd.get_dummies(X, categorical_features) |
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X.columns |
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from sklearn.model_selection import train_test_split |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2) |
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from sklearn.preprocessing import StandardScaler |
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scaler_normal = StandardScaler() |
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def scaler(data, runtime = False): |
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normal_col = ['person_income','person_age','person_emp_length', 'loan_amnt','loan_int_rate','cb_person_cred_hist_length'] |
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if(runtime == False): |
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data.loc[:,normal_col] = scaler_normal.fit_transform(data.loc[:,normal_col]) |
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else: |
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data.loc[:,normal_col] = scaler_normal.transform(data.loc[:,normal_col]) |
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return data |
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X_train = scaler(X_train) |
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X_test = scaler(X_test, True) |
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rf_model = RandomForestClassifier(max_depth = 5) |
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rf_model.fit(X_train, y_train) |
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y_predict = rf_model.predict(X_test) |
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y_predict |
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features = { |
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"person_home_ownership": ['MORTGAGE', 'OTHER','OWN', 'RENT',], |
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"loan_intent": ['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE'], |
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"loan_grade": ['A','B', 'C', 'D', 'E','F', 'G'], |
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"cb_person_default_on_file": ['N', 'Y'] |
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} |
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def preprocess(model_input): |
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for feature in features: |
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for option in features[feature]: |
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selection = model_input[feature] |
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if option is selection: |
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model_input[f'{feature}_{option}'] = 1 |
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else: |
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model_input[f'{feature}_{option}'] = 0 |
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model_input.drop([_ for _ in features], inplace = True, axis = 1) |
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return model_input |
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def credit_run(person_age, person_emp_length,person_home_ownership,cb_person_default_on_file,loan_intent,loan_grade,person_income, loan_amnt, |
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loan_int_rate, cb_person_cred_hist_length): |
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model_input = preprocess( |
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pd.DataFrame( { 'person_age': person_age, |
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'person_income': person_income, |
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'person_home_ownership': person_home_ownership, |
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'person_emp_length': person_emp_length, |
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'loan_intent': loan_intent, |
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'loan_grade': loan_grade, |
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'loan_amnt': loan_amnt, |
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'loan_int_rate': loan_int_rate, |
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'cb_person_default_on_file': cb_person_default_on_file, |
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'cb_person_cred_hist_length': cb_person_cred_hist_length |
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}, index = [0] |
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)) |
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out = rf_model.predict(model_input) |
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return "High risk of defaulting" if out[0] == 1 else "Low risk of defaulting" |
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import gradio as gr |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(scale=1,min_width=600): |
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gr.Image("non_payment_logo.jpg").style(height='7') |
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person_age=gr.Slider(label="Person Age(In Years)", minimum=18, maximum=90, step=1) |
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Pererson_Emp_Length=gr.Slider(label="Pererson Emp Length(In Years)", minimum=0, maximum=60, step=1) |
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with gr.Column(scale=2,min_width=600): |
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with gr.Row(): |
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with gr.Column(scale=1,min_width=400): |
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Home_Ownership_Status=gr.Radio(['MORTGAGE', 'OTHER','OWN', 'RENT'],label="Home Ownership Status") |
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with gr.Column(scale=2,min_width=100): |
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Person_Defaulted_in_History=gr.Radio(['0', '1'],label="Person Defaulted in History") |
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with gr.Row(): |
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with gr.Column(scale=3,min_width=300): |
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Credit_Intent=gr.Dropdown(['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE'],label="Credit Intent") |
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with gr.Column(scale=4,min_width=300): |
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Type_Of_Credit=gr.Dropdown(['A','B', 'C', 'D', 'E','F', 'G'],label="Type Of Credit") |
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with gr.Row(): |
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with gr.Column(scale=3,min_width=300): |
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Person_Income=gr.Number(label="Person Income(per month)") |
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with gr.Column(scale=4,min_width=300): |
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Loan_Amount=gr.Number(label="Loan Amount") |
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with gr.Row(): |
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with gr.Column(scale=3,min_width=300): |
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Loan_Interest_Rate=gr.Number(label="Loan Interest Rate") |
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with gr.Column(scale=4,min_width=300): |
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Person_Credit_History_Length=gr.Number(label="Person's Credit History Length") |
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with gr.Row(): |
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with gr.Column(): |
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default= gr.Radio(['Low risk of defaulting', 'High risk of defaulting']) |
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btn = gr.Button("PREDICT") |
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btn.click(fn=credit_run, inputs=[person_age,Person_Income,Home_Ownership_Status,Pererson_Emp_Length,Credit_Intent,Type_Of_Credit,Loan_Amount,Loan_Interest_Rate,Person_Defaulted_in_History,Person_Credit_History_Length], outputs=[default]) |
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demo.launch(debug=True) |
