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app.py

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+import streamlit as st
+import eda
+import prediction
+from PIL import Image
+
+# Set Config dan icon
+st.set_page_config(
+        page_title='Churn Prediction',
+        layout='wide',
+        )
+
+#klik eda, bawa ke page eda
+navigation = st.sidebar.selectbox('Select Page (EDA/Churn Prediction):', ('EDA', 'Churn Prediction'))
+
+
+#Run modul dengan if else
+if navigation == 'EDA':
+    eda.run()
+else :
+    prediction.run()

churn_best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1b8f9f4329696af5ceb64050282eca7f556586ef77213e85cfb571c80e8de09
+size 262152

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 Sub Header
+    st.title('**EDA Churn Customer**')
+    st.write('~ P2M1-Desti Ratna Komala-RMT-020')
+    #menambahkan Gambar 
+    image = Image.open('image_pred.jpg')
+    st.image(image, caption='churn.jpg')
+
+    # Import df
+    df_eda = pd.read_csv('https://raw.githubusercontent.com/destiratnakomala/Hacktiv8_Folder/main/churn.csv')
+
+    #membuat garis lurus 
+    st.markdown('-----')
+
+    #magic syntax
+    '''
+    Pada page ini, akan dilakukan ekplorasi dataset sederhana. 
+    Berikut adalah dataset Customer Churn yang digunakan
+    '''
+    #membuat title
+    st.subheader('Customer Churn EDA')
+    
+    # Import df
+    df = pd.read_csv('https://raw.githubusercontent.com/destiratnakomala/Hacktiv8_Folder/main/churn.csv')
+    st.dataframe(df)
+
+
+
+
+
+
+    st.write('Dari visualisasi dibawah dapat disimpulkan bahwa :')
+    st.markdown('- *Customer* yang *churn* lebih banyak dari pada *customer* yang tidak *churn*')
+    #membuat garis lurus 
+    st.markdown('-----')
+    pilihanuser=st.selectbox('Pilih Feature:', ('gender', 'region_category', 'membership_category', 'joined_through_referral', 'preferred_offer_types', 'medium_of_operation', 'internet_option', 'used_special_discount', 'offer_application_preference', 'past_complaint', 'complaint_status', 'feedback'))
+
+
+    #--------------------------------------------
+
+    fig, ax =plt.subplots(1,3,figsize=(25,8))
+    fig.suptitle(f'{pilihanuser} terhadap Customer Churn', fontsize=17, fontweight='bold')
+    #visualisasikan data
+
+    plt.subplot(1,3,1)
+    plt.title(f'{pilihanuser} terhadap Customer Churn', fontsize=14)
+    ax = sns.countplot(data = df, x = df[pilihanuser], hue="churn_risk_score", palette = 'pastel')
+    plt.ylabel("#churn", fontsize= 14)
+    plt.xticks(rotation=90)
+    plt.legend(loc=0)
+
+   
+    plt.subplot(1,3,2)
+    plt.title(f'{pilihanuser} terhadap %Customer Churn', fontsize=12)
+    ax = sns.barplot(x = df[pilihanuser], y = "churn_risk_score", data = df, palette = sns.color_palette('pastel')[1:2], errorbar= None)
+    plt.ylabel("%Churn", fontsize= 14)
+    plt.xticks(rotation=90)
+    
+    for p in ax.patches:
+        ax.annotate("%.2f" %(p.get_height()), (p.get_x()+0.3, p.get_height()+0.005),fontsize=12)
+        
+    plt.subplot(1,3,3)
+    round(df[pilihanuser].value_counts()/df.shape[0]*500,2).plot.pie(autopct= '%1.1f%%',colors=sns.color_palette('pastel')[0:10],shadow=True, legend=None)
+    plt.ylabel("")
+    plt.xticks(rotation=45)
+    plt.title(pilihanuser, fontsize=14)
+    st.pyplot(fig)
+
+
+    st.markdown('-----')
+    fig=plt.figure(figsize=(15, 5))
+    pilihanuser2=st.selectbox('Pilih Feature Numerik:',('age','days_since_last_login', 'avg_time_spent','avg_transaction_value', 'avg_frequency_login_days', 'points_in_wallet'))
+    plt.subplot(1,2,1)
+    sns.distplot(df[pilihanuser2])
+    plt.ticklabel_format(style='plain', axis='x')
+    plt.ylabel('')
+
+    plt.subplot(1,2,2)
+    sns.kdeplot(df.loc[(df['churn_risk_score'] == 0), pilihanuser2], label = 'not_churn', fill = True)
+    sns.kdeplot(df.loc[(df['churn_risk_score'] == 1), pilihanuser2], label = 'churn', fill = True)
+    plt.ylabel('')
+    plt.legend()
+    st.pyplot(fig)
+
+
+
+
+    #membuat garis lurus 
+    st.markdown('-----')
+
+
+if __name__ == '__main__':
+    run()

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+from tensorflow.keras.models import load_model
+
+
+def run() :
+  # Load Model
+  with open('preprocessor_churn.pkl', 'rb') as file_1:
+    preprocessor = pickle.load(file_1)
+  model_churn = load_model('churn_best_model.h5', compile=False)
+
+  # Membuat Title 
+  st.markdown("<h1 style='text-align: center;'>Churn Customer Prediction</h1>", unsafe_allow_html=True)
+
+  # Menambahkan Deskripsi form
+  st.write('Page ini berisi pemodelan untuk memprediksi churn customer. Silakan masukkan data Anda pada form dibawah ini.')
+
+  #Membuat Form
+  with st.form(key= 'form_customer'):
+      
+      st.markdown('### **Data Customer**')
+      user_id = st.text_input('User ID',value= '')
+      gender = st.selectbox('Gender',('M','F'),index=1)
+      age = st.slider('Age',10,90,30)
+      region_category = st.radio('Region', options=['City','Village','Town'], horizontal=True)
+      internet_option = st.selectbox('Internet Option',('Wi-Fi','Fiber_Optic', 'Mobile_Data'),index=1)
+      medium_of_operation = st.radio('Medium', options=['Desktop','Smartphone','Both'], horizontal=True)
+      st.markdown('---')
+      st.markdown('### **Login Data**')
+      days_since_last_login = st.slider('Days Since Last Login',0,30,3)
+      avg_frequency_login_days = st.slider('Avg Frequency Login Days',0,90,14)
+      st.markdown('---')
+      st.markdown('### **Membership Data**')
+      joined_through_referral = st.selectbox('Referral',('Yes','No'),index=1)
+      membership_category = st.selectbox('Membership Category',('No Membership','Basic Membership','Silver Membership', 'Premium Membership', 'Gold Membership', 'Platinum Membership'),index=1)
+      st.markdown('---')
+      st.markdown('### **Transaction Data**')
+      points_in_wallet = st.number_input('Points in Wallet', min_value=0, max_value=2070, value=600 ,step=1)
+      avg_transaction_value = st.number_input('Avg Transaction Value', min_value=800, max_value=90000, value=30000 ,step=1)
+      preferred_offer_types = st.radio('Offer Types', options=['Without Offers','Credit/Debit Card Offers','Gift Vouchers/Coupons'], horizontal=True)
+      used_special_discount = st.selectbox('Used Special Discount',('Yes','No'),index=1)
+      past_complaint = st.selectbox('Past Complaint',('Yes','No'),index=1)
+      feedback = st.selectbox('Feedback',('Poor Website','Poor Customer Service', 'Too many ads', 'Poor Product Quality', 'No reason specified', 'Products always in Stock', 'Reasonable Price', 'Quality Customer Care', 'User Friendly Website'),index=1)
+      submitted = st.form_submit_button('Predict')
+
+  # Create New Data 
+  data_inf = {
+      'user_id' : user_id,
+      'age' : age,
+      'gender' : gender,
+      'region_category' : region_category,
+      'internet_option' : internet_option, 
+      'medium_of_operation' : medium_of_operation,
+      'days_since_last_login' : days_since_last_login, 
+      'avg_frequency_login_days' : avg_frequency_login_days, 
+      'joined_through_referral' : joined_through_referral,
+      'membership_category' : membership_category,  
+      'points_in_wallet' : points_in_wallet, 
+      'avg_transaction_value' : avg_transaction_value,  
+      'used_special_discount' : used_special_discount,
+      'past_complaint' : past_complaint, 
+      'preferred_offer_types' : preferred_offer_types,                
+      'feedback' : feedback                                   
+  }
+
+  data_inf = pd.DataFrame([data_inf])
+  data_inf
+
+  if submitted :
+    # Feature Scaling and Feature Encoding
+    data_final = preprocessor.transform(data_inf)
+
+    # Predict using Linear Regression
+    y_inf_pred = np.where(model_churn.predict(data_final) >= 0.5, 1, 0)
+    
+    if y_inf_pred == 1:
+      prediction = 'Churn'
+    else:
+      prediction = 'Not Churn'
+
+    st.write('##### This customer is predicted:', prediction)
+
+if __name__ == '__main__':
+    run()

preprocessor_churn.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:53f9ce0813c933ea397ed161e771c8cf61281e54e1d41745100fb6636ed89aeb
+size 3148

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+tensorflow == 2.11.0
+scikit-learn == 1.0.2
+numpy
+plotly