app.py

import streamlit as st
import pandas as pd
import joblib

# =========================
# LOAD MODEL AND COLUMNS
# =========================

model = joblib.load("churn_model.pkl")
model_columns = joblib.load("model_columns.pkl")

# =========================
# PAGE CONFIG
# =========================

st.set_page_config(
    page_title="Customer Churn Prediction",
    page_icon="📉",
    layout="centered"
)

# =========================
# TITLE
# =========================

st.title("📉 Customer Churn Prediction")

st.write(
    """
    Predict whether a telecom customer is likely to churn
    based on customer profile and subscription details.
    """
)

# =========================
# USER INPUTS
# =========================

st.header("Enter Customer Details")

# Basic Info

gender = st.selectbox(
    "Gender",
    ["Male", "Female"]
)

senior_citizen = st.selectbox(
    "Senior Citizen",
    ["Yes", "No"]
)

partner = st.selectbox(
    "Partner",
    ["Yes", "No"]
)

dependents = st.selectbox(
    "Dependents",
    ["Yes", "No"]
)

# Tenure

tenure_months = st.slider(
    "Tenure Months",
    min_value=1,
    max_value=72,
    value=12
)

# Services

phone_service = st.selectbox(
    "Phone Service",
    ["Yes", "No"]
)

multiple_lines = st.selectbox(
    "Multiple Lines",
    ["Yes", "No", "No phone service"]
)

internet_service = st.selectbox(
    "Internet Service",
    ["DSL", "Fiber optic", "No"]
)

online_security = st.selectbox(
    "Online Security",
    ["Yes", "No", "No internet service"]
)

online_backup = st.selectbox(
    "Online Backup",
    ["Yes", "No", "No internet service"]
)

device_protection = st.selectbox(
    "Device Protection",
    ["Yes", "No", "No internet service"]
)

tech_support = st.selectbox(
    "Tech Support",
    ["Yes", "No", "No internet service"]
)

streaming_tv = st.selectbox(
    "Streaming TV",
    ["Yes", "No", "No internet service"]
)

streaming_movies = st.selectbox(
    "Streaming Movies",
    ["Yes", "No", "No internet service"]
)

# Contract

contract = st.selectbox(
    "Contract Type",
    ["Month-to-month", "One year", "Two year"]
)

paperless_billing = st.selectbox(
    "Paperless Billing",
    ["Yes", "No"]
)

payment_method = st.selectbox(
    "Payment Method",
    [
        "Electronic check",
        "Mailed check",
        "Bank transfer (automatic)",
        "Credit card (automatic)"
    ]
)

# Charges

monthly_charges = st.number_input(
    "Monthly Charges",
    min_value=0.0,
    max_value=200.0,
    value=70.0
)

total_charges = st.number_input(
    "Total Charges",
    min_value=0.0,
    max_value=10000.0,
    value=1000.0
)

cltv = st.number_input(
    "Customer Lifetime Value (CLTV)",
    min_value=0,
    max_value=10000,
    value=3000
)

# =========================
# CREATE INPUT DATA
# =========================

input_dict = {
    'Senior Citizen': senior_citizen,
    'Tenure Months': tenure_months,
    'Monthly Charges': monthly_charges,
    'Total Charges': total_charges,
    'CLTV': cltv
}

# =========================
# MANUAL ENCODING
# =========================

# Gender

input_dict['Gender_Male'] = 1 if gender == "Male" else 0

# Partner

input_dict['Partner_Yes'] = 1 if partner == "Yes" else 0

# Dependents

input_dict['Dependents_Yes'] = 1 if dependents == "Yes" else 0

# Phone Service

input_dict['Phone Service_Yes'] = 1 if phone_service == "Yes" else 0

# Multiple Lines

input_dict['Multiple Lines_Yes'] = 1 if multiple_lines == "Yes" else 0

input_dict['Multiple Lines_No phone service'] = (
    1 if multiple_lines == "No phone service" else 0
)

# Internet Service

input_dict['Internet Service_Fiber optic'] = (
    1 if internet_service == "Fiber optic" else 0
)

input_dict['Internet Service_No'] = (
    1 if internet_service == "No" else 0
)

# Online Security

input_dict['Online Security_Yes'] = (
    1 if online_security == "Yes" else 0
)

input_dict['Online Security_No internet service'] = (
    1 if online_security == "No internet service" else 0
)

# Online Backup

input_dict['Online Backup_Yes'] = (
    1 if online_backup == "Yes" else 0
)

input_dict['Online Backup_No internet service'] = (
    1 if online_backup == "No internet service" else 0
)

# Device Protection

input_dict['Device Protection_Yes'] = (
    1 if device_protection == "Yes" else 0
)

input_dict['Device Protection_No internet service'] = (
    1 if device_protection == "No internet service" else 0
)

# Tech Support

input_dict['Tech Support_Yes'] = (
    1 if tech_support == "Yes" else 0
)

input_dict['Tech Support_No internet service'] = (
    1 if tech_support == "No internet service" else 0
)

# Streaming TV

input_dict['Streaming TV_Yes'] = (
    1 if streaming_tv == "Yes" else 0
)

input_dict['Streaming TV_No internet service'] = (
    1 if streaming_tv == "No internet service" else 0
)

# Streaming Movies

input_dict['Streaming Movies_Yes'] = (
    1 if streaming_movies == "Yes" else 0
)

input_dict['Streaming Movies_No internet service'] = (
    1 if streaming_movies == "No internet service" else 0
)

# Contract

input_dict['Contract_One year'] = (
    1 if contract == "One year" else 0
)

input_dict['Contract_Two year'] = (
    1 if contract == "Two year" else 0
)

# Paperless Billing

input_dict['Paperless Billing_Yes'] = (
    1 if paperless_billing == "Yes" else 0
)

# Payment Method

input_dict['Payment Method_Credit card (automatic)'] = (
    1 if payment_method == "Credit card (automatic)" else 0
)

input_dict['Payment Method_Electronic check'] = (
    1 if payment_method == "Electronic check" else 0
)

input_dict['Payment Method_Mailed check'] = (
    1 if payment_method == "Mailed check" else 0
)

# =========================
# TENURE BUCKETS
# =========================

input_dict['Tenure Group_New'] = (
    1 if tenure_months <= 12 else 0
)

input_dict['Tenure Group_Regular'] = (
    1 if 12 < tenure_months <= 36 else 0
)

input_dict['Tenure Group_Loyal'] = (
    1 if 36 < tenure_months <= 60 else 0
)

input_dict['Tenure Group_Very Loyal'] = (
    1 if tenure_months > 60 else 0
)

# =========================
# DATAFRAME
# =========================

input_df = pd.DataFrame([input_dict])

# Match training columns

input_df = input_df.reindex(
    columns=model_columns,
    fill_value=0
)

# =========================
# PREDICTION
# =========================

if st.button("Predict Churn"):

    probability = model.predict_proba(input_df)[0][1]

    prediction = model.predict(input_df)[0]

    st.subheader("Prediction Result")

    st.write(
        f"### Churn Probability: {probability:.2%}"
    )

    if prediction == 1:

        st.error(
            "⚠️ High Risk of Churn"
        )

    else:

        st.success(
            "✅ Low Risk of Churn"
        )

    # Risk Meter

    st.progress(float(probability))

# =========================
# FOOTER
# =========================

st.markdown("---")

st.caption(
    "Built using Machine Learning, Streamlit, and Logistic Regression"
)