File size: 2,788 Bytes
0997dbc |
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 |
import streamlit as st
import pandas as pd
import requests
# Streamlit UI for Customer Churn Prediction
st.title("Telecom Customer Churn Prediction App")
st.write("This tool predicts customer churn risk based on their details. Enter the required information below.")
# Collect user input based on dataset columns
CustomerID = st.number_input("Customer ID", min_value=10000000, max_value=99999999)
SeniorCitizen = st.selectbox("Senior citizen", ["Yes", "No"])
Partner = st.selectbox("Does the customer have a partner?", ["Yes", "No"])
Dependents = st.selectbox("Does the customer have dependents?", ["Yes", "No"])
PhoneService = st.selectbox("Does the customer have phone service?", ["Yes", "No"])
InternetService = st.selectbox("Type of Internet Service", ["DSL", "Fiber optic", "No"])
Contract = st.selectbox("Type of Contract", ["Month-to-month", "One year", "Two year"])
PaymentMethod = st.selectbox("Payment Method", ["Electronic check", "Mailed check", "Bank transfer", "Credit card"])
tenure = st.number_input("Tenure (Months with the company)", min_value=0, value=12)
MonthlyCharges = st.number_input("Monthly Charges", min_value=0.0, value=50.0)
TotalCharges = st.number_input("Total Charges", min_value=0.0, value=600.0)
# Convert categorical inputs to match model training
customer_data = {
'SeniorCitizen': 1 if SeniorCitizen == "Yes" else 0,
'Partner':Partner,
'Dependents': Dependents,
'tenure': tenure,
'PhoneService': PhoneService,
'InternetService': InternetService,
'Contract': Contract,
'PaymentMethod': PaymentMethod,
'MonthlyCharges': MonthlyCharges,
'TotalCharges': TotalCharges
}
if st.button("Predict", type='primary'):
response = requests.post("https://RedRooster99-week2guidedhandsonbackendspace.hf.space/v1/customer", json=customer_data) # enter user name and space name before running the cell
if response.status_code == 200:
result = response.json()
churn_prediction = result["Prediction"] # Extract only the value
st.write(f"Based on the information provided, the customer with ID {CustomerID} is likely to {churn_prediction}.")
else:
st.error("Error in API request")
# Batch Prediction
st.subheader("Batch Prediction")
file = st.file_uploader("Upload CSV file", type=["csv"])
if file is not None:
if st.button("Predict for Batch", type='primary'):
response = requests.post("https://RedRooster99-week2guidedhandsonbackendspace.hf.space/v1/customerbatch", files={"file": file}) # enter user name and space name before running the cell
if response.status_code == 200:
result = response.json()
st.header("Batch Prediction Results")
st.write(result)
else:
st.error("Error in API request")
|