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app.py

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+# Import Library
+import streamlit as st
+
+# Import Halaman Streamlit yang telah dibuat
+import eda
+import prediction
+
+# Tombol Navigasi
+navigasi = st.sidebar.selectbox(label='Select Page:', options=['Home Page', 'Exploration Data Analysis', 'Model Prediksi'])
+
+# Looping untuk navigasi
+if navigasi == 'Home Page':
+    st.image("https://storage.googleapis.com/danacita-website-v3-prd/website_v3/images/biaya_bootcamp__kursus_hacktiv8_6.original.png")
+    st.header('Graded Chalenge 5') 
+    st.write('')
+    st.write('Nama      : Salsa Sabitha Hurriyah')
+    st.write('Batch     : HCK - 014')
+    st.write('Model Classification untuk memprediksi pembayaran kredit card pada bulan berikutnya')
+    st.write('')
+    st.caption('Silahkan pilih menu lain pada Select Box di sebelah kiri layar anda untuk memulai!')
+
+# Menampilkan halam EDA
+elif navigasi == 'Exploration Data Analysis':
+    eda.run()
+
+# Menampilkan halaman prediksi
+elif navigasi == 'Model Prediksi':
+    prediction.run()

eda.py

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+# import library
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+
+# Set Config Halaman
+st.set_page_config(
+    page_title = "Exploratory Data Analysis (EDA)",
+)
+
+def run():
+
+    # Set Judul
+    st.title("Exploration Data Analysis (EDA) dari Dataset")
+
+    # Memasukkan Gambar
+    st.image("https://images.bisnis.com/posts/2022/11/08/1595896/kartu_kredit_freepik.jpg")
+
+    # Load Data
+    data = pd.read_csv("P1G5_Set_1_salsa_sabitha.csv")
+
+    # Membuat header dataset
+    st.subheader("Dataset Kartu Kredit")
+    st.markdown('Ukuran dataset : 2814 rows, diperoleh dari 201 * 14 (tahun lahir dikalikan batch) yang disesuaikan')
+
+    # Menampilkan dataframe pada streamlit
+    st.dataframe(data)
+    st.markdown('---')
+
+    # Menampilkan distribusi data default payment status
+    st.subheader("Distribusi data default payment status")
+
+    # Distribusi data default payment status
+    # Membuat data untuk menampilkan pie chart
+    labels = [0, 1]
+    sizes = data['default_payment_next_month'].value_counts()
+    colors = ['pink', 'grey'] 
+    
+    # Membuat pie chart
+    fig, ax = plt.subplots()
+    ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)
+    
+    # Menampilkan pie chart
+    ax.axis('equal') 
+    st.pyplot(fig)
+    st.write('Diketahui bahwa status payment dengan status 0 (tidak gagal bayar) lebih banyak dibandingkan dengan status 1 (gagal bayar) dengan persentase "78.6%" dan "21.4%"')
+
+if __name__== '__main__':
+    run()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0537ea04193b9e36045dc3ebabb22a269fe327a2d410b7b7df2164f3423ffe3
+size 1716

prediction.py

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+# import library
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+
+# Set Config Halaman
+st.set_page_config(
+    page_title = "Prediksi Payment Status",
+    layout = 'wide',
+    initial_sidebar_state = 'expanded'
+)
+
+# Load Model
+with open('model.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+# Function untuk menjalankan streamlit model predictor
+def run():
+
+   # Set judul
+    st.title("Memprediksi Kemungkinan Seseorang Mampu Membayar Kredit atau Tidak")
+    st.markdown('---')
+    st.image("https://dlabs.ai/wp-content/uploads/2021/09/price-prediction-1024x538.png")
+
+    # Buat Form Untuk Data Inference
+    st.markdown('## Input Data')
+    with st.form('my_form'):
+        Limit_balance = st.number_input('Limit Balance', min_value=10000, max_value=999999999)
+        Pay_1 = st.selectbox('Repayment Status in September', (-2,-1,0,1,2,3,4,5,6,7,8))
+        Pay_2 = st.selectbox('Repayment Status in August', (-2,-1,0,1,2,3,4,5,6,7,8))
+        Pay_3 = st.selectbox('Repayment Status in July', (-2,-1,0,1,2,3,4,5,6,7,8))
+        Pay_4 = st.selectbox('Repayment Status in June', (-2,-1,0,1,2,3,4,5,6,7,8))
+        Pay_5 = st.selectbox('Repayment Status in May', (-2,-1,0,1,2,3,4,5,6,7,8))
+        Pay_6 = st.selectbox('Repayment Status in April', (-2,-1,0,1,2,3,4,5,6,7,8))
+        Pay_amt_1 = st.number_input('Bill Statement in September', min_value=-999999999, max_value=999999999)
+        Pay_amt_2 = st.number_input('Bill Statement in August', min_value=-999999999, max_value=999999999)
+        Pay_amt_3 = st.number_input('Bill Statement in July', min_value=-999999999, max_value=999999999)
+        Pay_amt_4 = st.number_input('Bill Statement in June', min_value=-999999999, max_value=999999999)
+        Pay_amt_5 = st.number_input('Bill Statement in May', min_value=-999999999, max_value=999999999)
+        Pay_amt_6 = st.number_input('Bill Statement in April', min_value=-999999999, max_value=999999999)
+
+        # Membuat tombol untuk melakukan prediksi
+        submitted = st.form_submit_button("Predict")
+
+    # dataframe
+    data = {'limit_balance': Limit_balance,
+            'pay_1': Pay_1,
+            'pay_2': Pay_2,
+            'pay_3': Pay_3,
+            'pay_4': Pay_4,
+            'pay_5': Pay_5,
+            'pay_6': Pay_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}
+
+    df = pd.DataFrame([data])
+    st.dataframe(df)
+
+    if submitted:
+        y_pred_inf = model.predict(df)
+        if y_pred_inf[0] == 0:
+            st.write('✅ Tidak Gagal Bayar')
+        else:
+            st.write('❌ Gagal Bayar')
+
+if __name__== '__main__':
+    run()