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app.py

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

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 title
+    st.title('Deposito Simulation')
+
+    #Tambahkan gambar
+    image = Image.open('Deposito.jpg')
+    st.image(image, caption = 'Deposito')
+
+    #Menambahkan deskripsi
+    st.write('Page ini dibuat oleh Mardhya Malik Nurbani')
+
+    #Membuat garis
+    st.markdown('----')
+
+    #Masukkan pandas dataframe
+
+    #Show dataframe
+    df = pd.read_csv('Banking Marketing.csv')
+    st.dataframe(df)
+
+    #Membuat bar plot
+    # st.write('#### Plot AttackingWorkRate')
+    # fig = plt.figure(figsize=(15,5))
+    # sns.countplot(x='AttackingWorkRate', data = df)
+    # st.pyplot(fig)
+
+    # #Membuat histogram
+    # st.write('#### Histogram of Age')
+    # fig = plt.figure(figsize=(15,5))
+    # sns.histplot(df['Overall'], bins = 30, kde = True)
+    # st.pyplot(fig)
+
+    # #membuat histogram berdasarkan inputan user
+    # st.write('#### Histogram berdasarkan input user')
+    # #kalo mau pake radio button, ganti selectbox jadi radio
+    # option = st.selectbox('Pilih Column : ', ('Age', 'Weight', 'Height', 'ShootingTotal'))
+    # fig = plt.figure(figsize= (15,5))
+    # sns.histplot(df[option], bins = 30, kde = True)
+    # st.pyplot(fig)
+
+    # #Membuat Plotly plot
+
+    # st.write('#### Plotly Plot - ValueEUR vs Overall')
+    # fig = px.scatter(df, x = 'ValueEUR', y = 'Overall', hover_data = ['Name', 'Age'])
+    # st.plotly_chart(fig)
+
+if __name__ == '__main__':
+    run()

model_rf.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2a744cb5943a7967f73796630256a9b43409871a3869784c11716314f6914ecc
+size 3708728

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib, pickle
+import json
+
+#Load All Files
+
+#load model dan files yang sudah di save pada framing
+# with open('model_rf.pkl', 'rb') as file_1:
+#     model_rf = pickle.load(file_1)
+# # model_patch = 'model_rf.pkl'
+# model_rf = joblib.load(model_patch)
+# with open('list_num_cols.txt', 'r') as file_1:
+#   list_num_cols = json.load(file_1)
+# with open('list_cat_cols.txt', 'r') as file_2:
+#     list_cat_cols = json.load(file_2)
+with open('model_rf.pkl', 'rb') as file3:
+    loaded_model = pickle.load(file3)
+
+def run():
+
+    with st.form('deposito_simulation'):
+        #Field Umur
+        Age = st.number_input('Age', min_value = 17, max_value = 75, value = 25, step = 1, help = 'Customers Age')
+        #Field Nama
+        Job = st.selectbox('Job', ('admin.', 'technician', 'services','management','retired','blue-collar','unemployed','entrepreneur','housemaid','unknown','self-employed','student'), index = 1)
+        #Marital Status
+        Marital = st.selectbox('Marital Status', ('married', 'single', 'divorced'), index = 1)
+        #Education Status
+        Education = st.selectbox('Education', ('secondary', 'tertiary', 'primary', 'unknown'), index = 1)
+        #Default Status
+        Default = st.selectbox('Default', ('no', 'yes'), index = 1, help = 'has credit in default?')
+        #Field Pace Total
+        Balance = st.number_input('Balance', min_value = 0, max_value=999999, value = 50)
+        #Housing Status
+        Housing = st.selectbox('Housing', ('yes', 'no'), index = 1, help = 'has housing loan?')
+        #Loan Status
+        Loan = st.selectbox('Loan', ('no', 'yes'), index = 1, help = 'has personal loan?')
+        #Contact Status
+        Contact = st.selectbox('Contact', ('unknown', 'cellular', 'telephone'), index = 1)
+        #Day
+        Day = st.number_input('Day', min_value = 1, max_value=31, value = 1, help = 'last contact day of the month')
+        #Month
+        Month = st.selectbox('Month', ('may', 'jun', 'jul', 'aug', 'oct', 'nov', 'dec', 'jan', 'feb', 'mar', 'apr', 'sep'), index = 1, help = 'last contact month of year')
+        #Duration
+        Duration = st.number_input('Duration', min_value = 0, max_value=999999, value = 50, help = 'last contact duration, in seconds')
+        #Day
+        Campaign = st.number_input('Campaign', min_value = 1, max_value=100, value = 1, help = 'number of contacts performed during this campaign and for this client')
+        #pDyas
+        Pdays = st.number_input('Pdays', min_value = -1 , max_value=9999999, value = 0, help = 'number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted')
+        #Previous
+        Previous = st.number_input('Previous', min_value = 0, max_value=100, value = 0, help = 'previous: number of contacts performed before this campaign and for this client')
+        #Poutcome Status
+        Poutcome = st.selectbox('P Outcome', ('unknown', 'other', 'failure', 'success'), index = 1, help = 'outcome of the previous marketing campaign')
+
+
+        #bikin submit button
+        submitted = st.form_submit_button('Predict')
+
+    #Inference
+    data_inf = {
+        'age' : Age,
+        'job' : Job,
+        'marital' : Marital,
+        'education' : Education,
+        'default' : Default,
+        'balance' : Balance,
+        'housing' :Housing,
+        'loan': Loan,
+        'contact' : Contact,
+        'day' :Day,
+        'month' :Month,
+        'duration':Duration,
+        'campaign': Campaign,
+        'pdays':Pdays,
+        'previous':Previous,
+        'poutcome': Poutcome,
+    }
+
+    data_inf = pd.DataFrame([data_inf])
+    st.dataframe(data_inf)
+
+    #Logic ketika predic button ditekan
+
+    if submitted:
+        
+        #predict using pipe rf model
+        predictions = loaded_model.predict(data_inf)
+        
+        st.write('## Deposit : ', str(int(predictions)))
+
+if __name__ == '__main__':
+   run()

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+numpy
+plotly
+pillow
+scikit-learn==1,2.2