initial commit

app.py

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+import streamlit as st
+import eda
+import pred
+
+page = st.sidebar.selectbox('Page Selection: ', ('Prediction', 'EDA'))
+
+if page == 'EDA':
+    eda.run()
+else:
+    pred.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
+
+st.set_page_config(
+    page_title= 'EDA',
+    layout='wide',
+    initial_sidebar_state='expanded'
+)
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+
+
+def run():
+
+    st.title('EDA')    
+
+
+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
+
+#Load all files
+
+with open('final_pipeline.pkl', 'rb') as file_1:
+  model_pipeline = pickle.load(file_1)
+
+model_ann = load_model("model1.h5", compile=False)
+
+def run():
+  
+  hide_streamlit_style = """
+              <style>
+              #MainMenu {visibility: hidden;}
+              footer {visibility: hidden;}
+              </style>
+              """
+  st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+
+
+  with st.form(key='Form Hotel'):
+        age = st.number_input('Age',0,70,step=1)
+        gender = st.selectbox('Gender',('M', 'F'))
+        region_category = st.selectbox('Hometown',('City', 'Village', 'Town'))
+        membership_category = st.selectbox('Membershipo Tier',('No Membership', 'Basic Membership', 'Silver Membership',
+ 'Premium Membership', 'Gold Membership', 'Platinum Membership'))
+        joining_date = st.date_input('Input date')
+        joined_through_referral = st.selectbox('Joined with referral?',('Yes', 'No'))
+        preferred_offer_types = st.selectbox('Preferred offer types',('Without Offers', 'Credit/Debit Card Offers', 'Gift Vouchers/Coupons'))
+        medium_of_operation = st.selectbox('Medium of operation',('Desktop', 'Smartphone', 'Both'))
+        internet_option = st.selectbox('Internet option',('Wi-Fi', 'Fiber_Optic', 'Mobile_Data'))
+        last_visit_time = st.number_input('Last visit time',0,1,step=1)
+        days_since_last_login = st.number_input('Days since last login',-150,0,step=1)
+        avg_time_spent = st.number_input('Average time spent',0,3000,step=1)
+        avg_transaction_value = st.number_input('Average transaction value',0,100000,step=1)
+        avg_frequency_login_days = st.number_input('Average frequency login day',0,90,step=1)
+        points_in_wallet = st.number_input('Points in wallet',0,2000,step=1)
+        used_special_discount = st.selectbox('Used special discount?',('Yes', 'No'))
+        offer_application_preference = st.selectbox('Offer app preference?',('Yes', 'No'))
+        past_complaint = st.selectbox('Any past complaint?',('Yes', 'No'))
+        complaint_status = st.selectbox('Complaint status',('No Information Available', 'Not Applicable', 'Unsolved', 'Solved',
+ 'Solved in Follow-up'))
+        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'))
+        submitted = st.form_submit_button('Predict')
+    
+      
+  data_inf = {
+        'user_id' : 'ac6e97806267549f',
+        'age' : age ,
+        'gender' : gender ,
+        'region_category' : region_category ,
+        'membership_category' : membership_category ,
+        'joining_date' : joining_date ,
+        'joined_through_referral' : joined_through_referral ,
+        'preferred_offer_types' : preferred_offer_types ,
+        'medium_of_operation' : medium_of_operation ,
+        'internet_option' : internet_option ,
+        'last_visit_time' : last_visit_time ,
+        'days_since_last_login' : days_since_last_login ,
+        'avg_time_spent' : avg_time_spent ,
+        'avg_transaction_value' : avg_transaction_value ,
+        'avg_frequency_login_days' : avg_frequency_login_days ,
+        'points_in_wallet' : points_in_wallet ,
+        'used_special_discount' : used_special_discount ,
+        'offer_application_preference' : offer_application_preference ,
+        'past_complaint' : past_complaint ,
+        'complaint_status' : complaint_status ,
+        'feedback' : feedback ,
+  }
+
+  data_inf = pd.DataFrame([data_inf])
+  st.dataframe(data_inf)
+  
+  data_inf['joining_date'] = pd.to_datetime(data_inf['joining_date'])
+  data_inf['joining_day'] = data_inf['joining_date'].dt.day
+  data_inf['joining_month'] = data_inf['joining_date'].dt.month
+  data_inf['joining_year'] = data_inf['joining_date'].dt.year
+  
+  if submitted:
+      data_inf_transform = model_pipeline.transform(data_inf)
+      y_pred_inf = model_ann.predict(data_inf_transform)
+      y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0)
+      y_pred_inf
+      
+      if y_pred_inf == 1:
+        st.write('likely to be churn')
+      else:
+        st.write('likely will not be churn')
+      #st.write(f'# (1 = Yes, 0 = No) : {str(int(data_pred_inf))}')
+      
+if __name__ == '__main__':
+    run()

requirements.txt

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+streamlit
+pandas
+numpy
+seaborn
+matplotlib
+Pillow
+plotly
+scikit-learn==1.2.2
+tensorflow