Upload 11 files

app.py

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+import streamlit as st
+import eda
+import prediction
+
+page = st.sidebar.selectbox('Pilih halaman ', ('EDA', 'Prediction'))
+
+if page == 'EDA':
+    eda.run()
+else:
+    prediction.run()

best_pipe.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e28f18dca6ae109ea40a03b33973d4f5857b13d29c131bf10a0286af3f7b0119
+size 197681

cat_col.txt

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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
+
+def run():
+    # Membuat judul
+    st.title('Credit Card Default')
+
+    # Membuat Sub Header
+    st.header('EDA untuk Analisa Dataset Credit Card Default', divider='gray')
+
+    # Menambahkan Gambar
+    image = Image.open('image.png')
+    st.image(image, caption = 'Credit Card Default', channels='RGB')
+
+    # Menambahkan Divider
+    st.divider()
+
+    # Menampilkan Dataframe
+    st.header('Dataframe', divider='gray')
+    data = pd.read_csv('dataset.csv')
+    st.dataframe(data)
+    st.divider()
+
+    # Membuat Bar Plot 1
+    st.write('#### Hubungan Jenis Kelamin dengan Gagal Bayar')
+    fig = plt.figure(figsize=(12,8))
+    sns.barplot(x='sex', y='default_payment_next_month', data=data)
+     
+    # Menambah Title dan Label
+    plt.title('Hubungan Jenis Kelamin dengan Gagal Bayar')
+    plt.xlabel('Jenis Kelamin')
+    plt.ylabel('Gagal Bayar')
+
+    # Menampilkan Plot
+    plt.tight_layout()
+    st.pyplot(fig)
+    
+    # Keterangan
+    plot1 = '''Keterangan : 
+    1 = Pria
+    2 = Wanita'''
+    st.markdown(plot1)
+    st.divider()
+
+
+    # Membuat Bar Plot 2
+    st.write('#### Hubungan Tingkat Pendidikan dengan Gagal Bayar')
+    fig = plt.figure(figsize=(12,8))
+    sns.barplot(x='education_level', y='default_payment_next_month', data=data)
+     
+    # Menambah Title dan Label
+    plt.title('Hubungan Status Pernikahan dengan Gagal Bayar')
+    plt.xlabel('Status Pernikahan')
+    plt.ylabel('Gagal Bayar')
+
+    # Menampilkan Plot
+    plt.tight_layout()
+    st.pyplot(fig)
+    
+    # Keterangan
+    plot2 = '''Keterangan : 
+    1 = Graduate School, 2 = University, 3 = High School, 4 = Tidak Menjawab'''
+    st.markdown(plot2)
+    st.divider()
+
+
+    # Membuat Bar Plot 3
+    st.write('#### Hubungan Status Pernikahan dengan Gagal Bayar')
+    fig = plt.figure(figsize=(12,8))
+    sns.barplot(x='marital_status', y='default_payment_next_month', data=data)
+     
+    # Menambah Title dan Label
+    plt.title('Hubungan Status Pernikahan dengan Gagal Bayar')
+    plt.xlabel('Status Pernikahan')
+    plt.ylabel('Gagal Bayar')
+
+    # Menampilkan Plot
+    plt.tight_layout()
+    st.pyplot(fig)
+    
+     # Keterangan
+    plot3 = '''Keterangan : 
+    1 = Menikah, 2 = Belum Menikah, 3 = Tidak Menjawab'''
+    st.markdown(plot3)
+    st.divider()
+    
+
+    # Membuat Bar Plot 4
+    st.write('#### Status Pembayaran')
+    fig = plt.figure(figsize=(12,8))
+    sns.countplot(x='default_payment_next_month', data=data)
+     
+    # Menambahkan label Sumbu dan judul
+    plt.xlabel('Status Pembayaran')
+    plt.ylabel('Count')
+    plt.title('Kemungkinan Nasabah Mengalami Kegagalan Pembayaran')
+
+    # Menampilkan Plot
+    plt.tight_layout()
+    st.pyplot(fig)
+    
+    # Keterangan
+    plot4 = '''Keterangan : 
+    0 = Membayar Tagihan, 1 = Gagal Membayar Tagihan'''
+    st.markdown(plot4)
+    st.divider()
+
+
+if __name__ == '__main__':
+    run()

num_col.txt

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+["limit_balance", "sex", "education_level", "marital_status", "age", "pay_1", "pay_2", "pay_3", "pay_4", "pay_5", "pay_6", "bill_amt_1", "bill_amt_2", "bill_amt_3", "bill_amt_4", "bill_amt_5", "bill_amt_6", "pay_amt_1", "pay_amt_2", "pay_amt_3", "pay_amt_4", "pay_amt_5", "pay_amt_6"]

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import json
+
+#Load All files
+#Load model
+
+with open('best_pipe.pkl', 'rb') as file_1:
+    best_pipe = pickle.load(file_1)
+with open('num_col.txt', 'r') as file_2: 
+    num_col = json.load(file_2)
+with open('cat_col.txt', 'r') as file_3:
+    cat_col = json.load(file_3)
+
+def run():
+
+    with st.form('Credit_Card_Default'):
+        limit_balance = st.number_input('Limit Balance', value=80000)
+        sex = st.selectbox('Sex (1 = Male, 2 = Female)', (1, 2))
+        education_level = st.selectbox('Education Level (1 = Graduate School, 2 = University, 3 = High School, 4 = Others)', (1, 2, 3, 4))
+        marital_status = st.selectbox('Marital Status (1 = married, 2 = single, 3 = others)', (1, 2, 3))
+        age = st.number_input('Age', value = 25, min_value = 21, max_value = 69)
+        pay_1 = st.number_input('Repayment Status in September, 2005 (-2 = Others, -1 = Pay Duly, 1= Payment Delay for One Month, 2 = Payment Delay for Two Months, ... 8 = Payment Delay for Eight Months)', min_value=-2, max_value=8, value=3)  
+        pay_2 = st.number_input('Repayment Status in August, 2005 (-2 = Others, -1 = Pay Duly, 1= Payment Delay for One Month, 2 = Payment Delay for Two Months, ... 8 = Payment Delay for Eight Months)', min_value=-2, max_value=7, value=3)
+        pay_3 = st.number_input('Repayment Status in July, 2005 (-2 = Others, -1 = Pay Duly, 1= Payment Delay for One Month, 2 = Payment Delay for Two Months, ... 8 = Payment Delay for Eight Months)', min_value=-2, max_value=7, value=3)
+        pay_4 = st.number_input('Repayment Status in June, 2005 (-2 = Others, -1 = Pay Duly, 1= Payment Delay for One Month, 2 = Payment Delay for Two Months, ... 8 = Payment Delay for Eight Months)', min_value=-2, max_value=8, value=3)
+        pay_5 = st.number_input('Repayment Status in May, 2005 (-2 = Others, -1 = Pay Duly, 1= Payment Delay for One Month, 2 = Payment Delay for Two Months, ... 8 = Payment Delay for Eight Months)', min_value=-2, max_value=7, value=3)
+        pay_6 = st.number_input('Repayment Status in April, 2005 (-2 = Others, -1 = Pay Duly, 1= Payment Delay for One Month, 2 = Payment Delay for Two Months, ... 8 = Payment Delay for Eight Months)', min_value=-2, max_value=7, value=3)
+        bill_amt_1 = st.number_input('Amount of bill statement in September, 2005', value=5000)
+        bill_amt_2 = st.number_input('Amount of bill statement in August, 2005', value=6000)
+        bill_amt_3 = st.number_input('Amount of bill statement in July, 2005', value=7000)
+        bill_amt_4 = st.number_input('Amount of bill statement in June, 2005', value=8000)
+        bill_amt_5 = st.number_input('Amount of bill statement in May, 2005', value=9000)
+        bill_amt_6 = st.number_input('Amount of bill statement in April, 2005', value=10000)
+        pay_amt_1 = st.number_input('Amount of bill statement in September, 2005', value=11000)
+        pay_amt_2 = st.number_input('Amount of bill statement in August, 2005', value=12000)
+        pay_amt_3 = st.number_input('Amount of bill statement in July, 2005', value=13000)
+        pay_amt_4 = st.number_input('Amount of bill statement in June, 2005', value=14000)
+        pay_amt_5 = st.number_input('Amount of bill statement in May, 2005', value=15000)
+        pay_amt_6 = st.number_input('Amount of bill statement in April, 2005', value=16000)
+
+        # Bikin Submit Button Form
+        submitted = st.form_submit_button()
+
+    data_inf = {
+       'limit_balance' : limit_balance,
+       'sex' : sex,
+       'education_level' : education_level,
+       'marital_status' : marital_status,
+       'age' : age,
+       'pay_1' : pay_1,
+       'pay_2' : pay_2,
+       'pay_3' : pay_3,
+       'pay_4' : pay_4,
+       'pay_5' : pay_5,
+       'pay_6' : pay_6,
+       'bill_amt_1' : bill_amt_1,
+       'bill_amt_2' : bill_amt_2,
+       'bill_amt_3' : bill_amt_3,
+       'bill_amt_4' : bill_amt_4,
+       'bill_amt_5' : bill_amt_5,
+       'bill_amt_6' : bill_amt_6,
+       'pay_amt_1' : pay_amt_1,
+       'pay_amt_2' : pay_amt_2,
+       'pay_amt_3' : pay_amt_3,
+       'pay_amt_4' : pay_amt_4,
+       'pay_amt_5' : pay_amt_5,
+       'pay_amt_6' : pay_amt_6
+    }
+
+    data_inf = pd.DataFrame([data_inf])
+    st.dataframe(data_inf)
+
+    if submitted:
+
+        # Prediksi Model
+        y_pred_inf = best_pipe.predict(data_inf)
+
+        if y_pred_inf == "0":
+            st.write('## Rating : Successfully Paid')
+        else:
+            st.write('## Rating : Failed to pay')
+
+
+if __name__ == '__main__':
+   run()
+
+

requirements.txt

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+streamlit
+pandas
+seaborn
+numpy
+matplotlib
+scipy
+scikit-learn==1.22
+Pillow
+plotly