Upload 9 files

X_cat.txt

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+["HasCrCard", "IsActiveMember", "Geography", "Gender"]

X_num_norm.txt

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+["CreditScore", "Tenure", "Balance", "EstimatedSalary"]

X_num_skew.txt

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+["Age", "NumOfProducts"]

app.py

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+import streamlit as st
+import eda
+import prediction
+
+navigation = st.sidebar.selectbox('Pilih Halaman: ', ('EDA', 'predict'))
+
+if navigation == 'EDA':
+    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
+
+st.set_page_config(
+   page_title= 'pelanggan pergi?',
+   layout='wide',
+   initial_sidebar_state='expanded'
+)
+def run():
+    # membuat Tittle
+    st.title('prediksi pelanggan yang tak akan kembali')
+
+    # magic syntaxs
+    '''
+    pada page kali ini, penulis akan mencoba melihat apakah pelanggan akan pergi untuk selamanya atau tidak
+    '''
+
+    # show dataframe
+    data = pd.read_csv('gabungandata.csv')
+    st.dataframe(data)
+
+    # membuat barplot berdasarkan input user
+    st.write('#### barplot berdasarkan Input user')
+    pilihan = st.selectbox('pilih colomn: ', ('Gender', 'NumOfProducts', 'Geography', 'Exited'))
+    fig = plt.figure(figsize= (15,5))
+    sns.countplot(data[pilihan])
+    st.pyplot(fig)
+
+    # membuat histogram berdasarkan input user
+    st.write('#### Histogram berdasarkan Input user')
+    pilihan = st.selectbox('pilih colomn: ', ('CreditScore', 'Age', 'Balance', 'EstimatedSalary', 'Balance'))
+    fig = plt.figure(figsize= (15,5))
+    sns.histplot(data[pilihan], bins=30, kde=True)
+    st.pyplot(fig)
+
+    # membuat plotly plot
+    st.write('#### Plotly Plot - age with CreditScore')
+    fig = px.scatter(data, x='Age', y= 'CreditScore', hover_data=['Exited'])
+    st.plotly_chart(fig)
+
+if __name__== '__main__':
+    run()
+

pipelines.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:45ce4a964f252a7d5560a5a258ad2ed2f71ce9bff95cb61ca213d343ac468f85
+size 17582071

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle 
+import json
+
+# Load All Files
+with open('pipelines.pkl', 'rb') as file_1:
+  clf_rfc = pickle.load(file_1)
+
+with open('X_num_skew.txt', 'r') as file_2:
+  X_num_skew = json.load(file_2)
+
+with open('X_num_norm.txt', 'r') as file_3:
+  X_num_norm = json.load(file_3)
+
+with open('X_cat.txt', 'r') as file_4:
+  X_cat = json.load(file_4)
+
+def run():
+    # membuat Tittle
+    st.title('prediksi apakah konsumen anda pergi?')
+
+    with st.form(key='form_prediksi_konsumen_minggat'):
+        Surname = st.text_input('Surname', value='')
+        Age = st.number_input('Age', min_value=18, max_value=80, step=1, help='Usia pelanggan?')
+        Gender = st.radio('Gender', ('Male','Female'), index=1, help='jenis kelamin?')
+        Geography = st.selectbox('Geography', ('Spain','France', 'Germany'), index=0, help='kebangsaan?')
+        st.markdown('---')
+
+        HasCrCard = st.selectbox('HasCrCard', (0,1), index=0, help='apakah anda kartu kredit?')
+        IsActiveMember = st.selectbox('IsActiveMember', (0,1), index=0, help='apakah anda punya kartu member?')
+        st.markdown('---')
+
+        CreditScore = st.slider('CreditScore', 350, 850, 500)
+        Tenure = st.slider('Tenure', 0, 10, 0)
+        Balance = st.slider('Balance', 0, 250000, 50000)
+        EstimatedSalary = st.slider('EstimatedSalary', 1000, 150000, 34000)
+        NumOfProducts = st.slider('NumOfProducts', 1, 4, 1)
+        
+        submitted = st.form_submit_button('Predict')
+
+    data_inf = {
+        'Age': Age,
+        'Gender': Gender,
+        'Geography': Geography,
+        'HasCrCard': HasCrCard,
+        'IsActiveMember': IsActiveMember,
+        'CreditScore': CreditScore,
+        'Tenure': Tenure,
+        'Balance': Balance,
+        'EstimatedSalary': EstimatedSalary,
+        'NumOfProducts': NumOfProducts,
+    }
+
+    data_inf = pd.DataFrame([data_inf])
+    a = st.dataframe(data_inf)
+    b = ('')
+
+    if a == 0:
+        b = 'tidak pergi'
+    else:
+        b = 'pergi'
+
+    if submitted:
+
+        y_pred_inf = clf_rfc.predict(data_inf)
+        st.write('# berapa pelanggan anda pergi? \n', str(b))
+
+if __name__== '__main__':
+    run()
+

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+plotly
+numpy
+scikit-learn==1.2.1