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3809050 80f6825 3125dec 3809050 | 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 | import gradio as gr
import joblib
import pandas as pd
from PIL import Image
best_model = joblib.load("best_model.pkl")
roc_img = Image.open("roc_curve_rf_tuned.png")
def churn_prediction(age, gender, tenure, usage_frequency, support_calls, payment_delay, last_interaction, total_spend, subscription_type, contract_length):
try:
input_data = {
"Age": age,
"Gender_Male": 1 if gender == "Male" else 0,
"Gender_Female": 1 if gender == "Female" else 0,
"Usage Frequency": usage_frequency,
"Support Calls": support_calls,
"Contract Length_Monthly": 1 if contract_length == "Monthly" else 0,
"Contract Length_Quarterly": 1 if contract_length == "Quarterly" else 0,
"Contract Length_Annual": 1 if contract_length == "Annual" else 0,
"Payment Delay": payment_delay,
"Last Interaction": last_interaction,
"Total Spend": total_spend,
"Tenure": tenure,
"Subscription Type_Basic": 1 if subscription_type == "Basic" else 0,
"Subscription Type_Premium": 1 if subscription_type == "Premium" else 0,
"Subscription Type_Standard": 1 if subscription_type == "Standard" else 0,
}
input_df = pd.DataFrame([input_data])
# Predict churn and probability
prediction = best_model.predict(input_df)
prediction_proba = best_model.predict_proba(input_df)[:, 1]
churn_probability = f"{prediction_proba[0]:.2f}"
if prediction_proba[0] < 0.8:
churn_result = "No"
else:
churn_result = "Yes" if prediction[0] == 1 else "No"
return churn_result, churn_probability, roc_img
except Exception as e:
return f"Error: {str(e)}", "N/A", None
inputs = [
gr.Slider(18, 100, value=40, label="Age"),
gr.Dropdown(["Female", "Male"], value="Male", label="Gender"),
gr.Slider(1, 60, value=30, label="Tenure (months)"),
gr.Slider(1, 30, value=15, label="Usage Frequency"),
gr.Slider(0, 10, value=4, label="Support Calls"),
gr.Slider(0, 30, value=15, label="Payment Delay"),
gr.Slider(1, 30, value=15, label="Last Interaction (days ago)"),
gr.Slider(100, 1000, value=620, label="Total Spend"),
gr.Dropdown(["Premium", "Standard", "Basic"], value="Standard", label="Subscription Type"),
gr.Dropdown(["Monthly", "Quarterly", "Annual"], value="Annual", label="Contract Length")
]
outputs = [
gr.Textbox(label="Churn Prediction"),
gr.Textbox(label="Churn Probability"),
gr.Image(label="ROC Curve")
]
gr.Interface(
fn=churn_prediction,
inputs=inputs,
outputs=outputs,
title="Customer Churn Prediction"
).launch() |