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# importing the libraries
import pickle
import streamlit as st
import pandas as pd
import sklearn
import numpy
# calling our pickle file
model = pickle.load(open("model-3.pkl", "rb"))
# creating a title for website
st.title("Customer Churn Prediction for Banks")
min_max_values = {
'credit_score': {'min': 350, 'max': 850},
'age': {'min': 18, 'max': 92},
'tenure': {'min': 0, 'max': 20},
'balance': {'min': 0, 'max': 250000},
'num_of_products': {'min': 1, 'max': 4},
'estimated_salary': {'min': 10000, 'max': 200000}
}
def min_max_scale(value, feature_name):
min_val = min_max_values[feature_name]['min']
max_val = min_max_values[feature_name]['max']
return (value - min_val) / (max_val - min_val)
credit_score = min_max_scale(
st.number_input("Credit Score:", min_value=350, max_value=850, help="Enter a value between 350 and 850"),
'credit_score'
)
gender = st.number_input("Gender (1 for Male, 0 for Female):", min_value=0, max_value=1)
age = min_max_scale(
st.number_input("Age:", min_value=18, max_value=92),
'age'
)
tenure = min_max_scale(
st.number_input("Tenure (years):", min_value=0, max_value=20),
'tenure'
)
balance = min_max_scale(
st.number_input("Account Balance:", help="Enter your account balance"),
'balance'
)
num_of_products = min_max_scale(
st.number_input("Number of Products:", min_value=1, max_value=4),
'num_of_products'
)
has_credit_card = st.number_input("Do you have a Credit Card? (1 for Yes, 0 for No)")
is_active_member = st.number_input("Are you an Active Member? (1 for Yes, 0 for No)")
estimated_salary = min_max_scale(
st.number_input("Estimated Salary:", help="Enter your estimated annual salary"),
'estimated_salary'
)
country_options = {"France": 1, "Spain": 2, "Germany": 3}
country = st.radio("Choose your country:", list(country_options.keys()))
country_code = country_options[country]
user_input_scaled = pd.DataFrame([[
credit_score, gender, age, tenure, balance, num_of_products,
has_credit_card, is_active_member, estimated_salary, country_code
]])
if st.button("Predict Churn"):
prediction = model.predict(user_input_scaled)[0]
message = "The customer is most likely to churn." if prediction == 1 else "The customer is not likely to churn."
st.write(message)
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