File size: 4,996 Bytes
80d1913 | 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 | # import pandas as pd
# import numpuy as np
# import joblip
# import gradio as gr
# importing the neccesary libs
import pandas as pd
import numpy as np
import joblib
import gradio as gr
# Load the preprocessing steps and the model
label_encoders = joblib.load('label_encoders.pkl')
one_hot_encoder = joblib.load('one_hot_encoder.pkl')
min_max_scaler = joblib.load('min_max_scaler.pkl')
model = joblib.load('logistic_regression_model.pkl')
le_target = joblib.load('label_encoder_target.pkl')
def preprocess_data(data):
df = pd.DataFrame([data])
label_encode_cols = ["Partner", "Dependents", "PhoneService", "PaperlessBilling", "gender"]
one_hot_encode_cols = ["MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup",
"DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies",
"Contract", "PaymentMethod"]
min_max_scale_cols = ["tenure", "MonthlyCharges", "TotalCharges"]
# Strip leading and trailing spaces from string inputs
for col in label_encode_cols + one_hot_encode_cols:
df[col] = df[col].str.strip()
# Convert non-numeric values to NaN and fill them with the mean of the column
df[min_max_scale_cols] = df[min_max_scale_cols].replace(' ', np.nan).astype(float)
df[min_max_scale_cols] = df[min_max_scale_cols].fillna(df[min_max_scale_cols].mean())
# Label encode specified columns
for col in label_encode_cols:
le = label_encoders[col]
df[col] = le.transform(df[col])
# One-hot encode specified columns
one_hot_encoded = one_hot_encoder.transform(df[one_hot_encode_cols])
# Min-max scale specified columns
scaled_numerical = min_max_scaler.transform(df[min_max_scale_cols])
# Combine processed columns into one DataFrame
X_processed = np.hstack((df[label_encode_cols].values, scaled_numerical, one_hot_encoded))
return X_processed
def predict(gender, senior_citizen, partner, dependents, tenure, phone_service, multiple_lines, internet_service,
online_security, online_backup, device_protection, tech_support, streaming_tv, streaming_movies,
contract, paperless_billing, payment_method, monthly_charges, total_charges):
data = {
"gender": gender,
"SeniorCitizen": senior_citizen,
"Partner": partner,
"Dependents": dependents,
"tenure": tenure,
"PhoneService": phone_service,
"MultipleLines": multiple_lines,
"InternetService": internet_service,
"OnlineSecurity": online_security,
"OnlineBackup": online_backup,
"DeviceProtection": device_protection,
"TechSupport": tech_support,
"StreamingTV": streaming_tv,
"StreamingMovies": streaming_movies,
"Contract": contract,
"PaperlessBilling": paperless_billing,
"PaymentMethod": payment_method,
"MonthlyCharges": monthly_charges,
"TotalCharges": total_charges
}
try:
X_new = preprocess_data(data)
prediction = model.predict(X_new)
prediction = le_target.inverse_transform(prediction)
return "Churn" if prediction[0] == 'Yes' else "No Churn"
except Exception as e:
print("Error during prediction:", e)
return str(e)
# Define the Gradio interface
inputs = [
gr.Radio(label="Gender", choices=["Female", "Male"]),
gr.Number(label="Senior Citizen (0 or 1)"),
gr.Radio(label="Partner", choices=["Yes", "No"]),
gr.Radio(label="Dependents", choices=["Yes", "No"]),
gr.Number(label="Tenure (integer)"),
gr.Radio(label="Phone Service", choices=["Yes", "No"]),
gr.Radio(label="Multiple Lines", choices=["Yes", "No", "No phone service"]),
gr.Radio(label="Internet Service", choices=["DSL", "Fiber optic", "No"]),
gr.Radio(label="Online Security", choices=["Yes", "No", "No internet service"]),
gr.Radio(label="Online Backup", choices=["Yes", "No", "No internet service"]),
gr.Radio(label="Device Protection", choices=["Yes", "No", "No internet service"]),
gr.Radio(label="Tech Support", choices=["Yes", "No", "No internet service"]),
gr.Radio(label="Streaming TV", choices=["Yes", "No", "No internet service"]),
gr.Radio(label="Streaming Movies", choices=["Yes", "No", "No internet service"]),
gr.Radio(label="Contract", choices=["Month-to-month", "One year", "Two year"]),
gr.Radio(label="Paperless Billing", choices=["Yes", "No"]),
gr.Radio(label="Payment Method", choices=["Electronic check", "Mailed check", "Bank transfer (automatic)", "Credit card (automatic)"]),
gr.Number(label="Monthly Charges (float)"),
gr.Number(label="Total Charges (float)")
]
outputs = gr.Textbox(label="Prediction")
# Create the Gradio interface
gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title="Churn Prediction Model").launch() |