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import pandas as pd
import numpy as np
import pickle
st.title("Credit Card Customer Segmentation")
st.write(
"Bu uygulama, KMeans kümeleme algoritmasını kullanarak müşteri segmentlerini tahmin eder."
)
# Model ve scaler yükleme
model = pickle.load(open("src/kmeans_model.pkl", "rb"))
scaler = pickle.load(open("src/scaler.pkl", "rb"))
# Kullanıcı girişleri
balance = st.number_input("Balance", value=1000.0)
balance_frequency = st.slider(
"Balance Frequency",
0.0,
1.0,
0.9
)
purchases = st.number_input("Purchases", value=500.0)
oneoff_purchases = st.number_input("Oneoff Purchases", value=200.0)
installments_purchases = st.number_input(
"Installments Purchases",
value=300.0
)
cash_advance = st.number_input("Cash Advance", value=0.0)
purchases_frequency = st.slider(
"Purchases Frequency",
0.0,
1.0,
0.5
)
oneoff_frequency = st.slider(
"Oneoff Purchases Frequency",
0.0,
1.0,
0.3
)
installments_frequency = st.slider(
"Installments Purchases Frequency",
0.0,
1.0,
0.4
)
credit_limit = st.number_input("Credit Limit", value=5000.0)
payments = st.number_input("Payments", value=1000.0)
minimum_payments = st.number_input(
"Minimum Payments",
value=300.0
)
full_payment = st.slider(
"Full Payment Ratio",
0.0,
1.0,
0.3
)
tenure = st.slider(
"Tenure",
1,
12,
12
)
# DataFrame oluşturma
input_data = pd.DataFrame({
"BALANCE":[balance],
"BALANCE_FREQUENCY":[balance_frequency],
"PURCHASES":[purchases],
"ONEOFF_PURCHASES":[oneoff_purchases],
"INSTALLMENTS_PURCHASES":[installments_purchases],
"CASH_ADVANCE":[cash_advance],
"PURCHASES_FREQUENCY":[purchases_frequency],
"ONEOFF_PURCHASES_FREQUENCY":[oneoff_frequency],
"PURCHASES_INSTALLMENTS_FREQUENCY":[installments_frequency],
"CASH_ADVANCE_FREQUENCY":[0],
"CASH_ADVANCE_TRX":[0],
"PURCHASES_TRX":[10],
"CREDIT_LIMIT":[credit_limit],
"PAYMENTS":[payments],
"MINIMUM_PAYMENTS":[minimum_payments],
"PRC_FULL_PAYMENT":[full_payment],
"TENURE":[tenure]
})
# Log dönüşümü
input_data = np.log1p(input_data)
# Scaling
input_scaled = scaler.transform(input_data)
# Tahmin
if st.button("Predict Cluster"):
cluster = model.predict(input_scaled)
st.subheader(f"Predicted Cluster: {cluster[0]}")
if cluster[0] == 0:
st.write("Risky cash advance users")
elif cluster[0] == 1:
st.write("Premium high spending customers")
elif cluster[0] == 2:
st.write("Balanced active customers")
elif cluster[0] == 3:
st.write("Low spending passive customers")
else:
st.write("Low balance customers") |