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| # 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) | |