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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+bank_portugal.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -2,9 +2,11 @@ import streamlit as st
 import eda
 import prediction
 
-page = st.sidebar.selectbox('Pilih Halaman: ', ('Exploratory Data Analysis', 'Prediction'))
+sc = st.sidebar.image('banco_de_portugal.png')
 
-if page == 'EDA':
+page = st.sidebar.selectbox('Select Page: ', ('Exploratory Data Analysis', 'Prediction'))
+
+if page == 'Exploratory Data Analysis':
     eda.run()
 else:
     prediction.run()

eda.py

@@ -1,20 +1,19 @@
 import streamlit as st
 import pandas as pd
 import seaborn as sns
-import matplotlib.pyplot as plt 
-import plotly.express as px
+import matplotlib.pyplot as plt
 from PIL import Image
 
 def run():
 
-    #Membuat title
+    #Title
     st.title('Bank Marketing Classification')
 
-    #Tambahkan gambar
-    image = Image.open("banco_de_portugal.png")
+    #Image
+    image = Image.open("bank_portugal.jpg")
     st.image(image)
     
-    st.write('---')
+    st.markdown('---')
     st.write('## Exploratory Data Analysis')
 
     #Load data

prediction.py

@@ -10,7 +10,7 @@ def run():
     with st.form('bank_form'):
 
         #Age
-        age = st.number_input('Age: ', min_value = 18, max_value = 100, value = 25, help = 'Isi usia user')
+        age = st.number_input('Age: ', min_value = 18, max_value = 100, value = 30, help = 'Isi usia user')
 
         #Marital
         marital = st.selectbox('Marital : ', ('married', 'single marital', 'divorce'), index = 0)
@@ -27,63 +27,67 @@ def run():
         #Housing Loan
         housing = st.selectbox('Housing Loan : ', ('yes', 'no'), index = 0)
 
-        #Conctact
-        contact = st.number_input('Conctact: ', min_value = 18, max_value = 100, value = 25, help = 'Isi usia user')
+        #Contact
+        contact = st.selectbox('Communication type : ', ('cellular', 'telephone', 'unknown'), index = 0)
         
-        #Conctact
-        duration = st.number_input('Conctact: ', min_value = 18, max_value = 100, value = 25, help = 'Isi usia user')
+        #Duration
+        duration = st.number_input('Last contact duration: ', min_value = 0, value = 0, help = 'Isi usia user')
 
-        #Conctact
-        campaign = st.number_input('Conctact: ', min_value = 18, max_value = 100, value = 25, help = 'Isi usia user')
+        #Campaign
+        campaign = st.number_input('Number of marketing team contact: ', min_value = 0, value = 0, help = 'Isi usia user')
 
-        #Conctact
-        previous = st.number_input('Conctact: ', min_value = 18, max_value = 100, value = 25, help = 'Isi usia user')
+        #Previous
+        previous = st.number_input('Number of marketing team contact before: ', min_value = 0, value = 0, help = 'Isi usia user')
 
-        #Conctact
-        outcome = st.number_input('Conctact: ', min_value = 18, max_value = 100, value = 25, help = 'Isi usia user')
+        #Outcome
+        outcome = st.selectbox('Outcome of the previous marketing campaign: ', ('failure', 'nonexistent', 'unknown', 'success'), index = 0)
 
         st.markdown('---')
 
         #High Skill Job
-        job_high_skill = st.selectbox('High Skill Job : ', (1, 0), index = 0)
+        job_high_skill = st.selectbox('High Skill Job : ', (1, 0), index = 1)
 
         #High Skill Job
-        job_low_skill = st.selectbox('Low Skill Job : ', (1, 0), index = 0)
+        job_low_skill = st.selectbox('Low Skill Job : ', (1, 0), index = 1)
         
         #High Skill Job
-        job_no_skill = st.selectbox('No Skill Job : ', (1, 0), index = 0)
+        job_no_skill = st.selectbox('No Skill Job : ', (1, 0), index = 1)
 
         #Submit button
         submitted = st.form_submit_button('Predict')
     
     #Data Inference
     data_inf = {
-        'Age' : age,
-        'Marital' : marital,
-        'Education' : education,
-        'Balance' : balance,
-        'Housing Loan' : housing,
-        'Personal Loan' : loan,
-        'Communication type' : contact,
-        'Last contact duration' : duration,
-        'Number of marketing team contact' : campaign,
-        'Number of marketing team contact before' : previous,
-        'Outcome of the previous marketing campaign' : outcome,
-        'High Skill Job' : job_high_skill,
-        'Low Skill Job' : job_low_skill,
-        'No Skill Job' : job_no_skill
+        'age' : age,
+        'marital' : marital,
+        'education' : education,
+        'balance' : balance,
+        'housing' : housing,
+        'loan' : loan,
+        'contact' : contact,
+        'duration' : duration,
+        'campaign' : campaign,
+        'previous' : previous,
+        'outcome' : outcome,
+        'job_high_skill' : job_high_skill,
+        'job_low_skill' : job_low_skill,
+        'job_no_skill' : job_no_skill
     }
 
     data_inf = pd.DataFrame([data_inf])
     st.dataframe(data_inf)
-
     
-    #Jika tombol submit ditekan
+    st.write('### Has the client subscribed a term deposit?')
+
     if submitted:
-        #predict using linear reg model
+        # Prediction using saved models
         y_pred_inf = model.predict(data_inf)
-        
-        st.write('### Subcribed or no? ', str(int(y_pred_inf)))
+
+        # Convert prediction results to text
+        result = "Subscribed" if int(y_pred_inf) == 1 else "Not Subscribed"
+
+        # Show Results
+        st.write(result)
 
 if __name__ == '__main__':
     run()