initial commit

app.py

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+import streamlit as st
+import joblib
+import numpy as np
+
+# Load the trained model
+model = joblib.load("bank_churn_model.pkl")
+
+# Define categorical mappings
+gender_map = {'Male': 0, 'Female': 1}
+education_level_map = {'High School': 0, 'HS Graduate': 1, 'Uneducated': 2, 'Unknown': 3, 'College': 4, 'College Graduate': 5, 'Doctorate': 6}
+marital_status_map = {'Married': 0, 'Single': 1, 'Unknown': 2, 'Divorced': 3}
+card_category_map = {'Blue': 0, 'Gold': 1, 'Silver': 2, 'Platinum': 3}
+income_category_map = {'Less than 40k': 0, '40-60k': 1, '60-80k': 2, '80-120k': 3, 'Unknown': 4}
+
+# Streamlit UI
+st.title("Customer Churn Prediction")
+
+st.header("Enter Customer Details")
+
+# User Inputs
+
+customer_age = st.number_input("Enter Customer Age", min_value=1, max_value=100)
+gender = st.selectbox("Gender", list(gender_map.keys()))
+dependent_count = st.number_input("Number of Dependents (e.g children, spouse, or other family members) (0-5)", min_value=0, max_value=5)
+education_level = st.selectbox("Education Level", list(education_level_map.keys()))
+marital_status = st.selectbox("Marital Status", list(marital_status_map.keys()))
+income_category = st.selectbox("Income Category", list(income_category_map.keys()))
+card_category = st.selectbox("Card Category", list(card_category_map.keys()))
+months_on_book = st.number_input("Enter Account Total of Months Active", min_value=0, max_value=100)
+total_relationship_count = st.number_input("Total number of accounts or financial products the customer has with the bank ", min_value=0, max_value=100)
+months_inactive_12_mon = st.number_input("Enter number of Years account is Inactive", min_value=0, max_value=100)
+contacts_count_12_mon = st.number_input("Enter Account Total of Years Inactive", min_value=0, max_value=100)
+credit_limit = st.number_input("Enter Maximum Credit Limit", min_value=1, max_value=100000000)
+total_revolving_bal = st.number_input("Enter Total Revolving Balance in Account", min_value=0, max_value=100000000)
+avg_open_to_buy = st.number_input("Enter Average amount of credit available to the customer on a revolving credit account", min_value=0, max_value=100000000)
+total_amt_chng_q4_q1 = st.number_input("Enter change in total transaction amount from Quarter 4 to Quarter 1", min_value=0, max_value=100000000)
+total_trans_amt = st.number_input("Enter Total Transaction Amount", min_value=0, max_value=100000000)
+total_trans_ct = st.number_input("Enter Total Transaction Count", min_value=0, max_value=100000000)
+total_ct_chng_q4_q1 = st.number_input("Enter change in total transaction Count from Quarter 4 to Quarter 1", min_value=0, max_value=100000000)
+avg_utilization_ratio = st.number_input("Enter average ratio of credit card balance to credit limit over a period", min_value=0, max_value=100000000)
+
+
+# Convert inputs to numerical values
+gender_encoded = gender_map[gender]
+education_encoded = education_level_map[education_level]
+marital_status_encoded = marital_status_map[marital_status]
+income_encoded = income_category_map[income_category]
+card_encoded = card_category_map[card_category]
+
+
+
+# Prepare data for prediction
+user_input = np.array([[customer_age, gender_encoded, dependent_count, education_encoded, marital_status_encoded, income_encoded, card_encoded, months_on_book, total_relationship_count, months_inactive_12_mon, contacts_count_12_mon, credit_limit, total_revolving_bal, avg_open_to_buy, total_amt_chng_q4_q1 ,total_trans_amt, total_trans_ct, total_ct_chng_q4_q1, avg_utilization_ratio]])
+
+# Predict button
+if st.button("Predict Churn"):
+    prediction = model.predict(user_input)
+    if prediction[0] == 1:
+        st.error("🔴 This customer is likely to churn.")
+    else:
+        st.success("🟢 This customer is not likely to churn.")

bank_churn_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:01bba94b3ec1db720e23331e50707d1c4e8c0b5409660ddf5c7f5ee66e3e5a1d
+size 1759

requirements.txt

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+streamlit
+numpy
+pandas
+matplotlib.pyplot
+seaborn