aaronayitey
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fc4d334
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a87966b
Upload 2 files
Browse files- app.py +79 -0
- rf_model.joblib +3 -0
app.py
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import gradio as gr
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import joblib
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import pandas as pd
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import numpy as np
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# Define prediction function
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def make_prediction(
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gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService,
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MultipleLines, InternetService, OnlineSecurity, OnlineBackup,
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DeviceProtection, TechSupport, StreamingTV, StreamingMovies,
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Contract, PaperlessBilling, PaymentMethod,
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MonthlyCharges, TotalCharges):
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# Make a dataframe from input data
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input_data = pd.DataFrame({
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'gender': [gender], 'SeniorCitizen': [SeniorCitizen], 'Partner': [Partner],
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'Dependents': [Dependents], 'tenure': [tenure], 'PhoneService': [PhoneService],
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'MultipleLines': [MultipleLines], 'InternetService': [InternetService],
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'OnlineSecurity': [OnlineSecurity], 'OnlineBackup': [OnlineBackup],
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'DeviceProtection': [DeviceProtection], 'TechSupport': [TechSupport],
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'StreamingTV': [StreamingTV], 'StreamingMovies': [StreamingMovies],
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'Contract': [Contract], 'PaperlessBilling': [PaperlessBilling],
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'PaymentMethod': [PaymentMethod], 'MonthlyCharges': [MonthlyCharges],
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'TotalCharges': [TotalCharges]
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})
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# Load already saved pipeline and make predictions
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with open("preprocessor.joblib", "rb") as p:
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preprocessor = joblib.load(p)
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input_data = preprocessor.transform(input_data)
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# You may need to update this part to drop the relevant columns for your model
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input_data = input_data.drop(['encode__PaperlessBilling_No', 'encode__MultipleLines_No', 'encode__InternetService_Fiber optic', 'encode__StreamingMovies_No internet service', 'encode__InternetService_No', 'encode__OnlineBackup_No internet service', 'encode__StreamingTV_No internet service'], axis=1)
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# Load already saved pipeline and make predictions
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with open("rf_model.joblib", "rb") as f:
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model = joblib.load(f)
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predt = model.predict(input_data)
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# Return prediction
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if np.any(predt == 1):
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return 'Customer Will Churn'
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else:
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return 'Customer Will Not Churn'
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# Create the input components for Gradio with labels
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gender_input = gr.Dropdown(choices=['Female', 'Male'], label='Select gender')
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SeniorCitizen_input = gr.Dropdown(choices=['Yes', 'No'], label='Is the customer a senior citizen?')
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Partner_input = gr.Dropdown(choices=['Yes', 'No'], label='Has the customer a partner?')
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Dependents_input = gr.Dropdown(choices=['Yes', 'No'], label='Does the customer have dependents?')
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tenure_input = gr.Number(label='Number of months the customer has stayed with the company')
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PhoneService_input = gr.Dropdown(choices=['Yes', 'No'], label='Does the customer have phone service?')
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MultipleLines_input = gr.Dropdown(choices=['No phone service', 'No', 'Yes'], label='Does the customer have multiple phone lines?')
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InternetService_input = gr.Dropdown(choices=['DSL', 'Fiber optic', 'No'], label='Type of Internet service')
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OnlineSecurity_input = gr.Dropdown(choices=['No', 'Yes', 'No internet service'], label='Does the customer have online security?')
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OnlineBackup_input = gr.Dropdown(choices=['Yes', 'No', 'No internet service'], label='Does the customer have online backup?')
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DeviceProtection_input = gr.Dropdown(choices=['No', 'Yes', 'No internet service'], label='Does the customer have device protection?')
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TechSupport_input = gr.Dropdown(choices=['No', 'Yes', 'No internet service'], label='Does the customer have tech support?')
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StreamingTV_input = gr.Dropdown(choices=['No', 'Yes', 'No internet service'], label='Does the customer have streaming TV?')
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StreamingMovies_input = gr.Dropdown(choices=['No', 'Yes', 'No internet service'], label='Does the customer have streaming movies?')
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Contract_input = gr.Dropdown(choices=['Month-to-month', 'One year', 'Two year'], label='Type of contract')
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PaperlessBilling_input = gr.Dropdown(choices=['Yes', 'No'], label='Is the customer using paperless billing?')
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PaymentMethod_input = gr.Dropdown(choices=['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)'], label='Payment method')
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MonthlyCharges_input = gr.Number(label='Monthly charges')
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TotalCharges_input = gr.Number(label='Total charges')
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output = gr.Textbox(label='Prediction')
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# Set the title of the Gradio app
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app = gr.Interface(fn=make_prediction, inputs=[
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gender_input, SeniorCitizen_input, Partner_input, Dependents_input, tenure_input,
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PhoneService_input, MultipleLines_input, InternetService_input, OnlineSecurity_input,
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OnlineBackup_input, DeviceProtection_input, TechSupport_input, StreamingTV_input,
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StreamingMovies_input, Contract_input, PaperlessBilling_input, PaymentMethod_input,
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MonthlyCharges_input, TotalCharges_input], outputs=output, title='Customer Churn Prediction App')
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app.launch(share=True, debug=True)
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rf_model.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:2caf44776f121e5e584125d07ccc7e8853ee30a26a6b3f255542daf3a0341a6b
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size 46576698
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