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app.py

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+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()

label_encoder_target.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:abaa19d805b61f0a5c204049ef0179bf2d9dc8f69678ae91fcd2549afecf1612
+size 537

label_encoders.pkl

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+oid sha256:048b8994247d9d5a7d4a37ddf59b9f625d85b891697128597273f5929b02865d
+size 1704

logistic_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e527cc822e0015fc69d2b8a90b29b77a3578c24e60924754800a7a4beda0c42
+size 1183

min_max_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:791ce9f20bba7e9a60dd5172153c08554f989addc99b732a6fc9b0096d1e5f67
+size 1151

one_hot_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:19bb9638a3d7cd799d3c49cf305eb088b50d7509ee6093aba241efc75ecbeb5d
+size 3922