Spaces:
Sleeping
Sleeping
File size: 5,395 Bytes
f2db62e ac59594 f2db62e ac59594 f2db62e ac59594 f2db62e ac59594 beb1407 ac59594 f2db62e beb1407 f2db62e |
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 |
# Importations
import pandas as pd
import gradio as gr
import os
import pickle
# Creating key List
expected_inputs = ['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges',
'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',
'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',
'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',
'PaperlessBilling', 'PaymentMethod']
# Function to load machine learning components
def load_components_func(fp):
# To load the machine learning components saved to re-use in the app
with open(fp, "rb") as f:
object = pickle.load(f)
return object
# Loading the machine learning components
DIRPATH = os.path.dirname(os.path.realpath(__file__))
ml_core_fp = os.path.join(DIRPATH,"ML_Model.pkl")
ml_components_dict = load_components_func(fp=ml_core_fp)
# Defining the variables for each component
label_encoder = ml_components_dict['label_encoder']
encoder = ml_components_dict['encoder']
imputer = ml_components_dict['imputer']
scaler = ml_components_dict['scaler']
balance = ml_components_dict['imbalance']
model = ml_components_dict['model']
def predict_churn(*args, scaler=scaler, model=model, imputer=imputer, encoder=encoder):
input_data = pd.DataFrame([args], columns=expected_inputs)
# Encode the data
num_col = input_data[['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges']]
cat_col = input_data[['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',
'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',
'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',
'PaperlessBilling', 'PaymentMethod']]
cat_col = cat_col.astype(str)
encoded_data = encoder.transform(cat_col)
encoded_df = pd.concat([num_col, encoded_data], axis=1)
# Imputing missing values
imputed_df = imputer.transform(encoded_df)
# Scaling
scaled_df = scaler.transform(encoded_df)
# Prediction
model_output = model.predict_proba(scaled_df)
#Probability of Churn(Positive class)
prob_Churn = float(model_output[0][1])
#Probability of staying(Negative Class)
prob_Stay = 1 - prob_Churn
return {"Prediction Churn": prob_Churn,
"Prediction Not Churn": prob_Stay}
# We define our inputs
Gender = gr.Radio(choices=['Male', 'Female'], label="Gender : Gender of the customer")
Partner = gr.Radio(choices=['Yes', 'No'], label="Partner : Whether the customer has a partner.")
Dependents = gr.Radio(choices=['Yes', 'No'], label="Dependents : Whether the customer has dependents.")
Tenure = gr.Number(label="Tenure : The Number of months the customer has been with the company.")
InternetService = gr.Radio(choices=['DSL', 'Fiber optic', 'No'], label="Internet Service : Type of internet service.")
PhoneService = gr.Radio(choices=['Yes', 'No'], label="Phone Service : Whether the customer has phone service.")
MultipleLines = gr.Radio(choices=['Yes', 'No'], label="Multiple Lines : Whether the customer has multiple phone lines.")
Contract = gr.Radio(choices=['Month-to-month', 'One year', 'Two year'], label="Contract : Type of contract the customer has.")
MonthlyCharges = gr.Number(label="Monthly Charges : Amount of monthly charges.")
TotalCharges = gr.Number(label="Total Charges : Total amount charged to the customer.")
PaperlessBilling = gr.Radio(choices=['Yes', 'No'], label='Paperless Billing : Whether the customer uses paperless billing.')
PaymentMethod = gr.Radio(choices=['Electronic check', 'Mailed check', 'Bank transfer (automatic)',
'Credit card (automatic)'], label="Payment Method : Payment method used by the customer.")
OnlineSecurity = gr.Radio(choices=['Yes', 'No'], label="Online Security : Whether the customer has online security service.")
OnlineBackup = gr.Radio(choices=['Yes', 'No', 'None'], label="Online Backup : Whether the customer has online backup service.")
DeviceProtection = gr.Radio(choices=['Yes', 'No'], label="Device Protection : Whether the customer has device protection service.")
TechSupport = gr.Radio(choices=['Yes', 'No'], label="Tech Support : Whether the customer has tech support service.")
StreamingTV = gr.Radio(choices=['Yes', 'No'], label="Streaming TV : Whether the customer uses streaming TV service.")
SeniorCitizen = gr.Radio(choices=[0, 1], label='Senior Citizen : Whether the customer is a senior citizen(0 for No and 1 For Yes).')
StreamingMovies = gr.Radio(choices=['Yes', 'No'], label="Streaming Movies : Whether the customer uses streaming movies service.")
#
gr.Interface(inputs=[SeniorCitizen, Tenure, MonthlyCharges, TotalCharges,
Gender, Partner, Dependents, PhoneService, MultipleLines,
InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,
TechSupport, StreamingTV, StreamingMovies, Contract,
PaperlessBilling, PaymentMethod],
outputs=gr.Label("Awaiting Submission...."),
fn=predict_churn,
title=" Teleco Services Customer Churn Prediction",
description="This model predicts whether a customer will churn or stay with the telecom service based on various input features",
).launch(inbrowser=True, show_error=True)
|