Update app.py

app.py

@@ -21,11 +21,12 @@ 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']
+    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:
@@ -35,8 +36,8 @@ def scaler(data, runtime = False):
 X_train = scaler(X_train)
 X_test = scaler(X_test, True)
 
-rfn_model = RandomForestClassifier(max_depth = 5)
-rfn_model.fit(X_train, y_train)
+rf_model = RandomForestClassifier(max_depth = 5)
+rf_model.fit(X_train, y_train)
 
 features = {
     "person_home_ownership": ['MORTGAGE', 'OTHER','OWN', 'RENT',],
@@ -52,13 +53,15 @@ def preprocess(model_input):
                 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):
+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,
@@ -68,12 +71,13 @@ def credit_run(person_age, person_emp_length,person_home_ownership,cb_person_def
                    '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 = rfn_model.predict(model_input)
-    return "High risk of defaulting" if out[0] == 1 else "Low risk of defaulting"
+    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: