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EDA.py

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+import streamlit as st 
+import pandas as pd 
+import seaborn as sns 
+import matplotlib.pyplot as plt 
+import plotly.express as px 
+from PIL import Image 
+
+# Set page config
+st.set_page_config(
+    page_title= 'Credit_Card_Default_EDA',
+    layout= 'wide',
+    initial_sidebar_state= 'expanded'
+)
+
+# Create Function for EDA
+def run():
+    #Create title
+    st.title('CREDIT CARD CUSTOMER')
+
+    # Create Sub Header atau Sub Judul
+    st.subheader('EDA untuk Analisis Dataset Credit Card Default')
+
+    # Add Image
+    st.image('https://www.cimbniaga.co.id/content/dam/cimb/inspirasi/Apa-Itu-Kartu-Kredit-Seperti-Apa-Memanfaatkannya.webp', caption= 'Credit Card Customer')
+
+    # Create a Description
+    st.write('Page Made by Allen')
+
+
+    # Magic Syntax
+    '''
+    Pada page kali ini, penulis akan melakukan eksplorasi sederhana,
+    Dataset yang digunakan adalah Credit Card Default.
+    Dataset ini berasal dari Big Query Google
+
+    '''
+
+    # Create Straight Line
+    st.markdown('---')
+
+    # Show Dataframe
+    df = pd.read_csv('P1G5_Set_1_Allen.csv')
+    st.dataframe(df)
+
+    # Create BarPlot
+    st.write('### Plotly Plot - Education Level vs Credit')
+    fig= px.bar(df, x= 'education_level', y= 'default_payment_next_month',
+                    hover_data=['education_level','default_payment_next_month'])
+    st.plotly_chart(fig)
+
+    # Statement menggunakan st.write
+    st.write('Dari data diatas kita mendapatkan informasi mengenai tingkat pendidikan yang berpengaruh pada pembayaran utang dan mempengaruhi limit balance, semakin rendah pendidikan ternyata mempengaruhi tingkat utang')
+
+
+    st.write('### Plotly Plot - Umur vs Credit')
+    fig= px.bar(df, x= 'age', y= 'default_payment_next_month',
+                    hover_data=['age','default_payment_next_month'])
+    st.plotly_chart(fig)
+    
+    # Statement menggunakan st.write
+    st.write('Dari data diatas kita mendapatkan informasi bahwa customer yang paling banyak melakukan credit berada pada usia 30-40 tahun yaitu dimana ada pada usia kerja atau usia produktif')
+
+    #Membuktikan umur paling banyak ada pada umur 30
+    st.write('### Plot age')
+    fig= plt.figure(figsize=(20,5))
+    sns.countplot(x='age', data=df)
+    st.pyplot(fig)
+    st.write('Umur 30 paling banyak melakukan credit customer')
+if __name__ == '__main__':
+    run()

Prediction.py

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+import pickle
+import json
+import pandas as pd 
+import numpy as np
+import streamlit as st
+
+# Load All Files
+with open('best_param.pkl', 'rb') as file_1:
+ best_params = pickle.load(file_1)
+
+with open('preprocessing_pipeline.pkl', 'rb') as file_2:
+ preprocessing_pipeline= pickle.load(file_2)
+
+def run ():
+  with st.form(key ='Credit FORM'): #Nulis nama sendiri menggunakan name= st.text_input('')
+    education_level= st.radio('Select Education_level', options=['1','2','3','4','5','6'])
+    sex = st.radio(
+    'Select gender',
+    options=['male','female'])
+    limit_balance= st.text_input('limit_balance', value= 'None')
+    st.markdown('---')
+    pay_0= st.slider('pay_0', min_value=-2,max_value=2,)
+    pay_2= st.slider('pay_2', min_value=-2,max_value=2,)
+    pay_3= st.slider('pay_3', min_value=-2,max_value=2,)
+    pay_4= st.slider('pay_4', min_value=-2,max_value=2,)
+    pay_5= st.slider('pay_5', min_value=-2,max_value=2,)
+    pay_6= st.slider('pay_6', min_value=-2,max_value=2,)
+     
+    submitted = st.form_submit_button('Predict')
+
+  # Create New Data
+  df_inf={
+      'limit_balance': limit_balance,
+       'sex': sex, 
+       'education_level': education_level , 
+       'pay_0': pay_0 , 
+       'pay_2': pay_2, 
+       'pay_3': pay_3,
+       'pay_4': pay_4, 
+       'pay_5': pay_5, 
+       'pay_6': pay_6,
+        }
+  df_inf = pd.DataFrame([df_inf])
+
+  if submitted: 
+    df_inf_best_params = df_inf[best_params]
+    df_inf_classifier= df_inf[preprocessing_pipeline]
+    df_inf_final = np.concatenate([preprocessing_pipeline], axis=1)
+    y_pred_inf = best_params.predict(df_inf_final) 
+    st.write(f'# Rating {best_params}:', int(y_pred_inf))
+
+if best_params == '__main__':
+  run()

app.py

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+import streamlit as st
+import EDA
+import Prediction
+
+Navigation = st.sidebar.selectbox('Pilih Halaman:', ('EDA','Predict Credit Card Customer'))
+if Navigation == 'EDA':
+    EDA.run()
+else:
+    Prediction.run()