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credit_card_default_app.py
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credit_card_default_app.py
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import pickle
import pandas as pd
import streamlit as st
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import xgboost as xgb
from lightgbm import LGBMRegressor
#Loading dataset
df=pd.read_csv(r"C:\Users\prati\OneDrive\Desktop\mlproject\src\google colab\default of credit card clients.csv",skiprows = 1)
st.title('Credit Card Default Prediction')
#Taking stores number as input
cust_id = st.number_input('Enter customer ID',min_value=1, max_value=30000)
st.write('Customer ID selected is ',cust_id)
#Function to process input dataframe
def data_predict(df,cust_id):
#converting all others values on education to 4
df['EDUCATION']=df['EDUCATION'].map({0:4,1:1,2:2,3:3,4:4,5:4,6:4})
#Converting others in marriage to 3
df['MARRIAGE']=df['MARRIAGE'].map({0:3,1:1,2:2,3:3})
#Selected features
numeric_col = ['LIMIT_BAL', 'AGE', 'PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',
'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',
'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']
# Transform Your data
pt=preprocessing.PowerTransformer(copy=False)
df[numeric_col]=pt.fit_transform(df[numeric_col])
# Scaling your data
X=df.drop(['default payment next month','ID'],axis=1)
#Creating object
X_scaler = StandardScaler()
#Fit on data
X_scaled=X_scaler.fit_transform(X)
#Converting to dataframe
X_scaled_df=pd.DataFrame(data=X_scaled,columns=X.columns,index=X.index)
#Adding customer ID on dataset
X_scaled_df['ID']=df['ID']
#Taking selected input from dataset
input_df=X_scaled_df[(X_scaled_df["ID"]==cust_id)]
input_df=input_df.drop(['ID'],axis=1)
# Load the model File and predicting on unseen data.
loaded_model = pickle.load(open(r'C:\Users\prati\OneDrive\Desktop\mlproject\credit_card_default.sav', 'rb'))
default_prediction= loaded_model.predict(input_df)
if (default_prediction==1):
return 'There is high probablity that selected customer might default'
else:
return 'Selected customer is higly unlikely to default'
#Caling function using predict button
if st.button('Default Prediction'):
default_predict=data_predict(df,cust_id)
st.write(default_predict)