File size: 5,424 Bytes
047d6a6 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | import streamlit as st
import numpy as np
import pickle # Change: 'pickle' imported instead of 'joblib'
from tensorflow.keras.models import load_model
# --- Load Model and Scaler ---
@st.cache_resource
def load_keras_model():
"""Load the saved Keras model."""
try:
model = load_model("model_churn.keras")
return model
except Exception as e:
st.error(f"Error loading Keras model (model_churn.keras): {e}")
return None
# ----- START: UPDATED SECTION -----
@st.cache_resource
def load_scaler_model():
"""Load the saved Scaler (pickle)."""
try:
# Pickle files must be opened in 'rb' (read binary) mode
with open("scaler_churn", "rb") as f:
scaler = pickle.load(f)
return scaler
except FileNotFoundError:
st.error("Error: 'scaler_churn' file not found. Please make sure the file is uploaded.")
return None
except Exception as e:
st.error(f"Error loading Scaler (scaler_churn): {e}")
return None
# ----- END: UPDATED SECTION -----
# Load models
model = load_keras_model()
scaler = load_scaler_model()
# Order of the 11 features expected by your model/scaler
MODEL_INPUT_COLUMNS_ORDER = [
'CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',
'IsActiveMember', 'EstimatedSalary', 'Geography_Germany', 'Geography_Spain',
'Gender_Male'
]
# --- User Interface (UI) ---
st.set_page_config(page_title="Customer Churn Prediction", layout="wide")
st.title("🏦 Customer Churn Prediction Model")
col1, col2 = st.columns([1, 1])
with col1:
st.header("Customer Information")
st.caption("Please enter customer information.")
geography = st.selectbox("Country", ("France", "Spain", "Germany"), index=0)
gender = st.radio("Gender", ("Female", "Male"), index=0)
has_cr_card = st.radio("Has Credit Card?", ("Yes", "No"), index=0)
is_active_member = st.radio("Active Member?", ("Yes", "No"), index=0)
credit_score = st.number_input("Credit Score", min_value=300, max_value=900, value=619)
age = st.number_input("Age", min_value=18, max_value=100, value=42)
tenure = st.number_input("Tenure (Years)", min_value=0, max_value=10, value=2)
balance = st.number_input("Balance", value=0.00, format="%.2f")
num_of_products = st.number_input("Number of Products", min_value=1, max_value=4, value=1)
estimated_salary = st.number_input("Estimated Salary", value=101348.88, format="%.2f")
predict_button = st.button("Calculate Risk", type="primary")
with col2:
st.header("Prediction Result")
st.caption("Model's churn probability prediction.")
if predict_button:
if model is None or scaler is None:
st.error("Models could not be loaded. Please contact administrator.")
else:
try:
# --- Step 1: Collect All Raw Inputs in a Dictionary ---
raw_data_dict = {
'CreditScore': credit_score,
'Age': age,
'Tenure': tenure,
'Balance': balance,
'NumOfProducts': float(num_of_products),
'HasCrCard': 1.0 if has_cr_card == "Yes" else 0.0,
'IsActiveMember': 1.0 if is_active_member == "Yes" else 0.0,
'EstimatedSalary': estimated_salary,
'Geography_Germany': 1.0 if geography == "Germany" else 0.0,
'Geography_Spain': 1.0 if geography == "Spain" else 0.0,
'Gender_Male': 1.0 if gender == "Male" else 0.0
}
# --- Step 2: Order Data According to Model's Expected Order ---
raw_input_features = [raw_data_dict[col] for col in MODEL_INPUT_COLUMNS_ORDER]
raw_input_array = np.array(raw_input_features).reshape(1, -1)
# --- Step 3: Scaling ---
scaled_input_array = scaler.transform(raw_input_array)
# --- Step 4: Make Prediction ---
with st.spinner("Model running, making prediction..."):
prediction_proba = model.predict(scaled_input_array)[0][0]
churn_probability_percent = prediction_proba * 100
threshold = 50.0
# --- Step 5: Display Result ---
if churn_probability_percent > threshold:
st.error(f"Customer Churn Probability: {churn_probability_percent:.2f}%")
st.warning("This customer has HIGH churn risk. 🚨")
else:
st.success(f"Customer Churn Probability: {churn_probability_percent:.2f}%")
st.info("This customer has LOW churn risk. ✅")
with st.expander("Processed (Scaled) Data Seen by Model"):
scaled_features_list = scaled_input_array.flatten().tolist()
st.json({col: val for col, val in zip(MODEL_INPUT_COLUMNS_ORDER, scaled_features_list)})
with st.expander("Raw Data Entered to Scaler (for verification)"):
st.json({col: val for col, val in zip(MODEL_INPUT_COLUMNS_ORDER, raw_input_features)})
except Exception as e:
st.error(f"An error occurred during prediction: {e}")
|