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churn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c852ec4021925e200375a5330e0e74ca88dc735dde1d7048497de30e2f1b847d
+size 54056

predict.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+import pandas as pd
+import numpy as np
+import pickle
+from datetime import datetime , time
+
+# Load All Files
+with open('prep_pipeline.pkl','rb') as file1:
+    prep = pickle.load(file1)
+
+ann = load_model('churn_model.h5')
+
+tanggal_waktu_sekarang = datetime.now().time()
+waktu_sekarang = datetime.now().date()
+
+
+def run():
+  with st.form('key=Churn_prediction'):
+      user_id = st.text_input('user_id:', '')
+      age = st.number_input("age:" ,min_value=18 , max_value=100,step=1)
+      gender = st.radio('gender', ('F','M'))
+      region_category = st.radio('region_category', ('Town','City','Village'))
+      membership_category = st.selectbox('membership_category', ('No Membership','Basic Membership','Silver Membership','Gold Membership','Platinum Membership','Premium Membership'), index=1)
+      st.markdown('---')
+
+      joining_date = st.date_input('joining_date', waktu_sekarang)
+      joined_through_referral = st.radio('joined_through_referral',(True,False))
+      preferred_offer_types = st.selectbox('preferred_offer_types',('Gift Vouchers/Coupons','Credit/Debit Card Offers','Without Offers'))
+      medium_of_operation = st.selectbox('medium_of_operation',('Desktop','Smartphone','Both'))
+      internet_option = st.radio('internet_option',('Wi-Fi','Mobile_Data','Fiber_Optic'))
+      last_visit_time = st.time_input('last_visit_time', tanggal_waktu_sekarang)
+      days_since_last_login = st.number_input('days_since_last_login', min_value=0 , max_value=1000,step=1)
+      avg_time_spent = st.number_input('avg_time_spent' , min_value=0 , max_value=3300,step=1)
+      avg_transaction_value = st.number_input('avg_transaction_value', min_value=500 , max_value=100000,step=5)
+      avg_frequency_login_days = st.number_input('avg_frequency_login_days', min_value=0 , max_value=75,step=1)
+      points_in_wallet = st.number_input('points_in_wallet',min_value=0 , max_value=2100,step=5)
+      used_special_discount = st.radio('used_special_discount', ('Yes','No'))
+      offer_application_preference = st.radio('offer_application_preference',('Yes','No'))
+      past_complaint = st.radio('past_complaint',('Yes','No'))
+      complaint_status = st.selectbox('complaint_status', ('Not Applicable' , 'Unsolved','Solved','Solved in Follow-up','No Information Available'))
+      feedback = st.selectbox('feedback', ('Poor Product Quality','No reason specified','Too many ads','Poor Website','Poor Customer Service','Reasonable Price','User Friendly Website','Products always in Stock','Quality Customer Care'))
+      submitted = st.form_submit_button('Predict')
+
+  data_inf = {
+      'user_id': user_id,
+      '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])
+  positive_words = ['reasonable', 'friendly', 'always', 'stock','care','price','user','products','in']
+  negative_words = ['poor', 'too', 'many', 'terrible','product quality','ads','service']
+  def classify_sentiment(text):
+        words = text.lower().split()
+        num_positive = sum(1 for word in words if word in positive_words)
+        num_negative = sum(1 for word in words if word in negative_words)
+    
+        if num_positive > num_negative:
+            return 'positif'
+        elif num_positive < num_negative:
+            return 'negatif'
+        else:
+            return 'netral'
+# Menerapkan fungsi klasifikasi sentimen menggunakan apply pada DataFrame
+  data_inf['sentimen'] = data_inf['feedback'].apply(classify_sentiment)
+
+  final_data = prep.transform(data_inf)
+
+
+  if submitted:
+    y_pred_inf = ann.predict(final_data)
+    y_pred_inf = np.where(y_pred_inf >= 0.5 , 'Churn' , 'Not Churn')
+    st.write('Price of the car : ', str(y_pred_inf))
+
+
+if __name__ == '__main__':
+    run()

prep_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1cdd856beb751eb858e47e0512c620ba33fe7b6571bffdd450dcef21b749c1de
+size 2797

requirements.txt

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