Upload 3 files

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+
+# Load All Files
+# Model Logistic Regresi
+with open('log_reg.pkl', 'rb') as file_1:
+  model_log_reg = pickle.load(file_1)
+
+# Model Suppor Vector Classifier
+with open('svc.pkl', 'rb') as file_2:
+  model_svc = pickle.load(file_2)
+
+
+st.subheader('Prediksi Kelas Pendapatan')
+
+# Nilai dari education num
+education_num = st.slider('Pilih Lama Waktu Pendidikan',3,16)
+
+# Nilai dari fitur capital gain
+capital_gain = st.slider('Masukkan Jumlah Capital Gain',0,100000)
+
+# Nilai dari fitur hours per week
+hours_per_week = st.number_input('Masukkan Total Waktu Kerja Per Minggu', 0, 80)
+
+# value dari fitur new_occupation
+occu = st.radio('Pilih Jenis Tingkat Pekerjaan',( 'Manager Up', 'Middle Worker', 'Low Worker', 'Others Service'))
+
+# Value fitur sex
+sex = st.radio('Pilih Jenis Kelamin',('Male', 'Female'))
+
+if st.button('Predict'):
+    data_inf = pd.DataFrame({'education_num': education_num, 'capital_gain': capital_gain,
+                             'hours_per_week': hours_per_week, 'new_occupation': occu, 'sex':sex},index=[0])
+
+    hasil_log_reg = model_log_reg.predict(data_inf)[0]
+    st.write(f'Kelas Pendapatan Anda Menurut Model Logistic Regression:     {hasil_log_reg}')
+
+    hasil_svm = model_svc.predict(data_inf)[0]
+    if hasil_svm == 0:
+        hasil_svm = '<=50K'
+    else:
+        hasil_svm = '>50K'
+    st.write(f'Kelas Pendapatan Anda Menurut Model Logistic SVC : {hasil_svm}')
+

log_reg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7d83bd276be3ff2035f33963121c8b3e7e13169ae81d5b088dc8b3ac56d569ae
+size 3321

svc.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b91380b0840842f5636bccff7c7202ee21276cf8e4c37a8cc48200e76f0f5be3
+size 255334