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import streamlit as st
import pandas as pd
import numpy as np
import joblib
import tensorflow
with open('full_pipeline.pkl', 'rb') as file_1:
model_pipeline = joblib.load(file_1)
from tensorflow.keras.models import load_model
model_ann = load_model('churn_model.h5')
st.title("Customer Churn Prediction")
membership_category = st.selectbox('Membership Category',('No Membership',
'Basic Membership',
'Silver Membership',
'Premium Membership',
'Gold Membership',
'Platinum Membership'), index=1)
avg_transaction_value = st.number_input('Average Transaction Value :',
min_value = 800.460000,
max_value = 99914.050000,
value = 800.460000)
points_in_wallet = st.number_input('Points In Wallet :',
min_value = 0.000000,
max_value = 2069.069761,
value = 0.000000)
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)
df_inf = pd.DataFrame({
'membership_category' : [membership_category],
'avg_transaction_value' : [avg_transaction_value],
'points_in_wallet' : [points_in_wallet],
'feedback' : [feedback]
})
if st.button('Predict'):
data_inf_transform = model_pipeline.transform(df_inf)
y_pred_inf = model_ann.predict(data_inf_transform)
y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0)
churn_status = np.where(y_pred_inf == 0, "No", "Yes")
if churn_status == "No":
st.success(f"The customer is predicted to `not churn`.")
else:
st.error(f"The customer is predicted to `churn`.") |