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import streamlit as st | |
import pickle | |
from tensorflow.keras.models import load_model | |
import pandas as pd | |
import numpy as np | |
# Load All Files | |
with open('final_pipeline.pkl', 'rb') as file_1: | |
model_pipeline = pickle.load(file_1) | |
model_ann = load_model('customer_churn.h5') | |
def run(): | |
with st.form(key='Churn_Customer_Prediction'): | |
churn_risk_score = st.selectbox('Churn Risk', (0, 1), index=1) | |
age = st.number_input('Age', min_value=23, max_value=65, value=23) | |
gender = st.selectbox('Gender', ('Male', 'Female'), index=1) | |
days_since_last_login = st.number_input('Last Login', min_value=0, max_value=26, value=0) | |
avg_time_spent = st.number_input('Avg. Time Spent', min_value=0, max_value=3236, value=0) | |
avg_transaction_value = st.number_input('Avg. Transaction Value', min_value=800, max_value=99915, value=29271) | |
avg_frequency_login_days = st.number_input('Avg. Frequency Login Days', min_value=0, max_value=73, value=0) | |
points_in_wallet = st.number_input('Points in Wallet', min_value=0, max_value=2070, value=0) | |
joining_date = st.date_input("Select Join Date") | |
last_visit_time = st.time_input('Last Visit Time') | |
st.markdown('---') | |
region_category = st.selectbox('Region Category', ('Village', 'Town', 'City'), index=1) | |
membership_category = st.selectbox('Membership Category', ('No Membership', 'Basic Membership', | |
'Silver Membership', 'Premium Membership', | |
'Gold Membership', 'Platinum Membership'), index=1) | |
preferred_offer_types = st.selectbox('Preffered Offer', ('Without Offers', 'Credit/Debit Card Offers', | |
'Gift Vouchers/Coupons'), index=1) | |
medium_of_operation = st.selectbox('Medium Ops', ('Desktop', 'Mobile', 'Both' | |
'Gift Vouchers/Coupons'), index=1) | |
internet_option = st.selectbox('Internet Ops', ('Wi-Fi', 'Fiber_Optic', 'Mobile-Data'), index=1) | |
feedback = st.selectbox('Feedback', ('Poor Website', 'Poor Customer Service', 'Poor Product Quality', | |
'Too many ads', 'No reason specified', 'Products always in Stock', | |
'Reasonable Price', 'Quality Customer Care', 'User Friendly Website'), index=1) | |
complaint_status = st.selectbox('Complaint Status', ('No Information Available', 'Not Aplicable', 'Unsolved', | |
'Solved', 'Solved in Follow-up'), index=1) | |
st.markdown('---') | |
joined_through_referral = st.selectbox('Join Through Referral', ('Yes', 'No'), index=1) | |
used_special_discount = st.selectbox('Use Special Discount', ('Yes', 'No'), index=1) | |
offer_application_preference = st.selectbox('Offer Application Preference', ('Yes', 'No'), index=1) | |
past_complaint = st.selectbox('Past Complaint', ('Yes', 'No'), index=1) | |
submitted = st.form_submit_button('Predict') | |
data_inf = { | |
'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, | |
'churn_risk_score': churn_risk_score | |
} | |
data_inf = pd.DataFrame([data_inf]) | |
data_inf_transform = model_pipeline.transform(data_inf) | |
a = st.dataframe(data_inf_transform) | |
b = '' | |
if len(data_inf_transform) == 0: | |
b = 'Not Churn' | |
else: | |
# Predict using ANN: Sequential API | |
y_pred_inf = model_ann.predict(data_inf_transform) | |
y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0) | |
if y_pred_inf == 0: | |
b = 'Not Churn' | |
else: | |
b = 'Churn' | |
if submitted: | |
st.write('# Prediction : ', b) | |
if __name__ == '__main__': | |
run() |