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| import streamlit as st | |
| import numpy as np | |
| import joblib | |
| model = joblib.load("churn.joblib") | |
| st.title("Credit Card Customer Churn Prediction") | |
| def get_one_hot_encoding(value, categories): | |
| encoding = [False] * len(categories) | |
| if value in categories: | |
| encoding[categories.index(value)] = True | |
| return encoding | |
| def prediction(model,arr): | |
| output = model.predict(arr) | |
| if output == 0: | |
| st.write("# Existing Customer") | |
| elif output==1: | |
| st.write("# Atrrited Customer") | |
| gender_categories = ["Female", "Male"] | |
| education_categories = ["College", "Doctorate","Graduate","High School","Post-Graduate","Uneducated","Unknown"] | |
| marital_categories = ["Divorced", "Married","Single","Unknown"] | |
| income_categories = ["0-40K", "120K Plus","40K-60K","60K-80K","80K-120K","Unknown"] | |
| card_categories = ["Blue", "Gold","Platinum","Silver"] | |
| with st.container(): | |
| st.title("Enter details:") | |
| age = st.number_input("Enter age: ", min_value=1) | |
| dependent = st.number_input("Dependent Count: ", min_value=0) | |
| months = st.number_input("Months (on book): ", min_value = 0) | |
| relations = st.number_input("Total relationship count: ", min_value = 0) | |
| inactive = st.number_input("Inactive Months: ",min_value = 0) | |
| contacts_count = st.number_input("Total contacts (12 months): ",min_value = 0) | |
| revolving_bal = st.number_input("Revolving balance in ₹: ",min_value = 0) | |
| avg_open = st.number_input("Enter Average open to buy (in ₹):", min_value = 0) | |
| amt_change = st.number_input("Total amount change from Q1 to Q4:", min_value = 0) | |
| trans_amt = st.number_input("Total transaction amount :",min_value = 0) | |
| trans_count = st.number_input("Total transactions: ", min_value = 0) | |
| count_change = st.number_input("Total transactions count change from Q1 to Q4: ", min_value = 0) | |
| util_ratio = st.number_input("Average Utilization ratio: ", min_value = 0.0, max_value = 1.0, step=0.1) | |
| gender = st.selectbox("Enter Gender: ",("Female","Male")) | |
| education = st.selectbox("Education Level:",("College", "Doctorate","Graduate","High School","Post-Graduate","Uneducated","Unknown")) | |
| marital_stat = st.selectbox("Marital Status:",("Divorced", "Married","Single","Unknown")) | |
| income = st.selectbox("Income category:",("0-40K", "120K Plus","40K-60K","60K-80K","80K-120K","Unknown")) | |
| card = st.selectbox("Card Category:",("Blue", "Gold","Platinum","Silver")) | |
| with st.container(): | |
| arr = np.zeros((1, 36)) | |
| arr[0, 0] = age | |
| arr[0, 1] = dependent | |
| arr[0, 2] = months | |
| arr[0, 3] = relations | |
| arr[0, 4] = inactive | |
| arr[0, 5] = contacts_count | |
| arr[0, 6] = revolving_bal | |
| arr[0, 7] = avg_open | |
| arr[0, 8] = amt_change | |
| arr[0, 9] = trans_amt | |
| arr[0, 10] = trans_count | |
| arr[0, 11] = count_change | |
| arr[0, 12] = util_ratio | |
| offset = 13 | |
| # Gender | |
| gender_encoding = get_one_hot_encoding(gender, gender_categories) | |
| arr[0, offset:offset+len(gender_categories)] = gender_encoding | |
| offset += len(gender_categories) | |
| # Education | |
| education_encoding = get_one_hot_encoding(education, education_categories) | |
| arr[0, offset:offset+len(education_categories)] = education_encoding | |
| offset += len(education_categories) | |
| # Marital Status | |
| marital_encoding = get_one_hot_encoding(marital_stat, marital_categories) | |
| arr[0, offset:offset+len(marital_categories)] = marital_encoding | |
| offset += len(marital_categories) | |
| # Income | |
| income_encoding = get_one_hot_encoding(income, income_categories) | |
| arr[0, offset:offset+len(income_categories)] = income_encoding | |
| offset += len(income_categories) | |
| # Card | |
| card_encoding = get_one_hot_encoding(card, card_categories) | |
| arr[0, offset:offset+len(card_categories)] = card_encoding | |
| offset += len(card_categories) | |
| if st.button("Calculate"): | |
| prediction(model, arr) | |