File size: 5,596 Bytes
ba27b3c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | import streamlit as st
import pandas as pd
import numpy as np
import pickle
from tensorflow.keras.models import load_model
# load file
with open("./column_transformer.pkl", "rb") as file_1:
column_transformer = pickle.load(file_1)
model_functional = load_model("./functional_model.keras")
def predict():
# form
with st.form("key=churn_prediction"):
st.subheader("Churn Score Prediction")
st.markdown("**Customer Data**")
col1, col2 = st.columns(2, gap="large")
age = col1.number_input(label="Age", help="Customer Age", step=1, value=20)
membership = col2.selectbox(
label="Membership Category",
options=(
"No Membership",
"Basic Membership",
"Premium Membership",
"Silver Membership",
"Gold Membership",
"Platinum Membership",
),
)
st.markdown("---")
col1, col2, col3, col4 = st.columns(4, gap="large")
region = col1.radio(
label="Region",
help="Customer Residence Region",
options=("Town", "City", "Village"),
)
referral = col2.radio(
label="Referral", help="Joined Through Referral?", options=("Yes", "No")
)
device = col3.radio(
label="Device(s)",
help="Device Used",
options=("Smartphone", "Desktop", "Both"),
)
internet = col4.radio(
label="Internet Connection", options=("Wi-Fi", "Fiber_Optic", "Mobile_Data")
)
st.markdown("---")
st.markdown("**Customer Behavior**")
col1, col2, col3, col4, col5 = st.columns(5, gap="large")
last_login = col1.number_input(
label="Last Login", help="Days Since Last Login", step=1, value=6
)
avg_time = col2.number_input(
label="Avg. Usage Time", help="Average Usage Time (Minutes)", value=30
)
avg_login = col3.number_input(
label="Avg. Login Frequency",
help="Average Login Frequency (Days)",
value=14,
)
points = col4.number_input(label="Points in Wallet", value=300)
transaction = col5.number_input(label="Avg. Transaction", value=100, help="USD")
st.markdown("---")
col1, col2, col3 = st.columns(3, gap="large")
offer_pref = col1.selectbox(
label="Preferred Offer Type",
options=(
"Gift Vouchers/Coupons",
"Credit/Debit Card Offers",
"Without Offers",
),
)
used_disc = col2.radio(label="Used Discount Before?", options=("Yes", "No"))
offer_app = col3.radio(
label="Application Preference Offer?", options=("Yes", "No")
)
st.markdown("---")
col1, col2, col3 = st.columns(3, gap="large")
complaints = col1.radio(label="Past Complaint?", options=("Yes", "No"))
complaints_status = col2.selectbox(
label="Complaint Status",
options=(
"Not Appllicable",
"Unsolved",
"Solved",
"Solved in Follow-up",
"No Information Available",
),
)
feedback = col3.selectbox(
label="Feedback Type", options=("Neutral", "Positive", "Negative")
)
submitted = st.form_submit_button("Predict")
# inferencing
data_inf = [
{
"age": age,
"region_category": region,
"membership_category": membership,
"joined_through_referral": referral,
"preferred_offer_types": offer_pref,
"medium_of_operation": device,
"internet_option": internet,
"days_since_last_login": last_login,
"avg_time_spent": avg_time,
"avg_transaction_value": transaction,
"avg_frequency_login_days": avg_login,
"points_in_wallet": points,
"used_special_discount": used_disc,
"offer_application_preference": offer_app,
"past_complaint": complaints,
"complaint_status": complaints_status,
"feedback": feedback,
}
]
data_inf = pd.DataFrame(data_inf)
st.dataframe(data_inf)
data_inf_transform = column_transformer.transform(data_inf)
y_pred_inf = model_functional.predict(data_inf_transform)
y_pred_inf = np.where(y_pred_inf >= 0.65, 1, 0)
st.write("Prediksi Churn Pelanggan Tersebut adalah :")
if y_pred_inf[0] == 1:
html_str = f"""
<style>
p.a {{
font: bold 36px Arial;
color: teal;
}}
</style>
<p class="a">Pelanggan Tidak Berpotensi Churn</p>
"""
st.markdown(html_str, unsafe_allow_html=True)
st.write(
"Dapat menekankan program loyalty agar pelanggan tetap menggunakan layanan"
)
else:
html_str = f"""
<style>
p.a {{
font: bold 36px Arial;
color: red;
}}
</style>
<p class="a">Pelanggan Berpotensi Churn</p>
"""
st.markdown(html_str, unsafe_allow_html=True)
st.write("Dapat diberikan promosi untuk menarik pelanggan kembali")
if __name__ == "__main__":
predict()
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