app.py

app.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import RandomizedSearchCV
+
+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)
+
+model = RandomForestClassifier(max_depth = 5)
+model.fit(X_train, y_train)
+
+y_predict = model.predict(X_test)
+
+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 = model.predict(model_input)
+    return "High risk of defaulting" if out[0] == 1 else "Low risk of defaulting" 
+
+
+import gradio as gr
+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']),
+        gr.Slider(label="person_emp_length(In Years)", minimum=0, maximum=60, step=1),
+        gr.Radio(['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE']),
+        gr.Radio(['A','B', 'C', 'D', 'E','F', 'G']),
+        gr.Number(label="loan_amnt"),
+        gr.Number(label="loan_int_rate"),
+        gr.Radio(['0', '1']),
+        gr.Number(label="cb_person_cred_hist_length"),
+             ],
+    outputs = "text",
+    title = "Credit Risk Predictor",
+    examples = [[23,15000,'RENT',2,'EDUCATION','A',300000,8.9,'0',6],
+[34,50000,'OWN',1,'MEDICAL','B',1000000,10.65,'0',3],
+[42,30000,"MORTGAGE",12,'HOMEIMPROVEMENT','C',800000,7.9,'1',8],
+[42,20000,"MORTGAGE",10,'PERSONAL','F',100000,15.25,'1',5]]
+)
+demo.launch(share = True,debug=True)
+