| |
| import pickle |
| import streamlit as st |
| import pandas as pd |
| import sklearn |
| import numpy |
|
|
| |
| model = pickle.load(open("model-3.pkl", "rb")) |
|
|
| |
| 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) |
|
|
