Spaces:
Sleeping
Sleeping
File size: 3,899 Bytes
c1cacde 8243175 c1cacde 8243175 c1cacde |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
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)
|