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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('Customer will be : ', str(y_pred_inf)) | |
| if __name__ == '__main__': | |
| run() | |