Create app.py

app.py

@@ -0,0 +1,68 @@
+# 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)