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app (1).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(share=True)

label_encoder_target.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:64021f16ad0ddc7d9e4337b1cf092711d1e5d2c7a2184f1109c757b16797f490
+size 537

label_encoders.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c7924da8ef9cff4754f9ecf4914bb393f76b129807e68eb8fb452e08dbf1090e
+size 1704

logistic_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:80bafde2eb586c26655b50edb2f2d134004785c293b6481e46c902a1bf7aaf18
+size 1135

min_max_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b81e34b8f1602a0940e4f7db4bd99d20a8d4aa163a8270d2bc963df0756a32f
+size 1151

one_hot_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3dd8a114e3d78c0dbdafee4322dc90430441eab7c9c189483128699f76bfcfd6
+size 3901

requirements.txt.txt

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+pandas
+numpy==1.21.5
+scikit-learn==1.2.2
+gradio
+joblib