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cda2726 7d408f4 ba99e6f 7d408f4 ba99e6f 7d408f4 cda2726 7d408f4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import streamlit as st
import pandas as pd
# import joblib
import pickle
# scaler = joblib.load('scaler.pkl')
with open('src/scaler.pkl', 'rb') as file:
scaler = pickle.load(file)
with open('src/credit_default.pkl', 'rb') as file:
model = pickle.load(file)
st.title('Prediksi Default Loan Customer')
Name = st.text_input('Name', placeholder='Input Your Name..')
# Streamlit input widgets
GENDER = st.radio("Jenis Kelamin", ["Laki-laki", "Perempuan"])
AGE = st.slider('Umur (Tahun)', 0, 130, 20)
Type_Occupation = st.selectbox(
"Jenis Pekerjaan",
("High skill tech staff", 'Core staff', 'Sales staff', 'Laborers',
'Cooking staff', 'Managers', 'Accountants', 'Cleaning staff', 'Drivers',
'Private service staff', 'Low-skill Laborers', 'IT staff',
'Waiters/barmen staff', 'Medicine staff', 'Security staff', 'HR staff',
'Secretaries', 'Realty agents'),
placeholder="Pilih Pekerjaanmu...",
)
Marital_status = st.selectbox(
"Status Pernikahan",
('Married', 'Single / not married', 'Civil marriage', 'Separated', 'Widow'),
placeholder="Pilih Jenis Pendapatanmu...",
)
Family_Members = st.slider('Jumlah Anggota Keluarga', 0, 20, 2)
Type_Income = st.selectbox(
"Jenis Pendapatan",
('Commercial associate', 'Pensioner', 'Working', 'State servant'),
placeholder="Pilih Jenis Pendapatanmu...",
)
YEAR_EMPLOYED = st.slider('Lama Bekerja (Tahun)', 0, 60, 5)
EDUCATION = st.selectbox(
"Pendidikan",
('Higher education', 'Secondary / secondary special', 'Lower secondary',
'Incomplete higher', 'Academic degree'),
placeholder="Pilih Pendidikan Terakhirmu...",
)
Housing_type = st.selectbox(
"Tipe Rumah",
('House / apartment', 'With parents', 'Rented apartment',
'Municipal apartment', 'Co-op apartment', 'Office apartment'),
placeholder="Pilih Tipe Rumahmu...",
)
# Mapping dictionaries
Housing_type_map = {
'House / apartment': 0, 'Rented apartment': 1, 'With parents': 2,
'Municipal apartment': 3, 'Co-op apartment': 4, 'Office apartment': 5
}
EDUCATION_map = {
'Higher education': 0, 'Secondary / secondary special': 1, 'Lower secondary': 2,
'Incomplete higher': 3, 'Academic degree': 4
}
Type_Occupation_map = {
'Private service staff': 0, 'Laborers': 1, 'Managers': 2, 'Medicine staff': 3,
'Cooking staff': 4, 'Sales staff': 5, 'Accountants': 6, 'High skill tech staff': 7,
'Cleaning staff': 8, 'Drivers': 9, 'Low-skill Laborers': 10, 'IT staff': 11,
'Waiters/barmen staff': 12, 'Core staff': 13, 'Security staff': 14, 'HR staff': 15,
'Secretaries': 16, 'Realty agents': 17
}
GENDER_map = {'Laki-laki': 1, 'Perempuan': 0}
Marital_status_map = {
'Married': 0, 'Single / not married': 1, 'Civil marriage': 2, 'Separated': 3, 'Widow': 4
}
Type_Income_map = {
'Commercial associate': 0, 'Pensioner': 1, 'Working': 2, 'State servant': 3
}
df = pd.DataFrame()
if st.button('Prediksi Loan Customer'):
Name = Name
Housing_type_value = Housing_type_map[Housing_type]
EDUCATION_value = EDUCATION_map[EDUCATION]
Type_Occupation_value = Type_Occupation_map[Type_Occupation]
GENDER_value = GENDER_map[GENDER]
Marital_status_value = Marital_status_map[Marital_status]
Type_Income_value = Type_Income_map[Type_Income]
card_credit = [GENDER_value, Type_Occupation_value, Type_Income_value, Marital_status_value, EDUCATION_value, AGE, Housing_type_value, YEAR_EMPLOYED]
df = pd.DataFrame([card_credit], columns=['GENDER', 'Type_Occupation', 'Type_Income', 'Marital_status', 'EDUCATION', 'AGE', 'Housing_type', 'YEAR_EMPLOYED'])
if not df.empty:
c_scaler = scaler.transform(df.values.reshape(1, -1))
loan_prediction = model.predict(c_scaler)
if loan_prediction[0] == 1:
loan_diagnose = f"Pengajuan Kartu Kredit Atas Nama {Name} Ditolak"
else:
loan_diagnose = f"Pengajuan Kartu Kredit Atas Nama {Name} Diterima"
if loan_prediction[0] == 1:
st.error(loan_diagnose, icon="❌")
else:
st.success(loan_diagnose, icon="✅")
else:
st.error("Harap isi semua form terlebih dahulu.") |