Update churn_analysis.py

churn_analysis.py

@@ -0,0 +1,76 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib, os
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+pipeline_path = os.path.join(script_dir, 'toolkit', 'pipeline.joblib')
+model_path = os.path.join(script_dir, 'toolkit', 'Random Forest Classifier.joblib')
+
+# Load transformation pipeline and model
+pipeline = joblib.load(pipeline_path)
+model = joblib.load(model_path)
+
+# Create a function to calculate TotalCharges
+def calculate_total_charges(tenure, monthly_charges):
+    return tenure * monthly_charges
+
+# Create a function that applies the ML pipeline and makes predictions
+def predict(SeniorCitizen, Partner, Dependents, tenure,
+            InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
+            StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod,
+            MonthlyCharges):
+    
+    # Calculate TotalCharges
+    TotalCharges = calculate_total_charges(tenure, MonthlyCharges)
+
+    # Create a dataframe with the input data
+    input_df = pd.DataFrame({
+        'SeniorCitizen': [SeniorCitizen],
+        'Partner': [Partner],
+        'Dependents': [Dependents],
+        'tenure': [tenure],
+        'InternetService': [InternetService],
+        'OnlineSecurity': [OnlineSecurity],
+        'OnlineBackup': [OnlineBackup],
+        'DeviceProtection': [DeviceProtection],
+        'TechSupport': [TechSupport],
+        'StreamingTV': [StreamingTV],
+        'StreamingMovies': [StreamingMovies],
+        'Contract': [Contract],
+        'PaperlessBilling': [PaperlessBilling],
+        'PaymentMethod': [PaymentMethod],
+        'MonthlyCharges': [MonthlyCharges],
+        'TotalCharges': [TotalCharges]
+    })
+
+    # Selecting categorical and numerical columns separately
+    cat_cols = [col for col in input_df.columns if input_df[col].dtype == 'object']
+    num_cols = [col for col in input_df.columns if input_df[col].dtype != 'object']
+    
+    X_processed = pipeline.transform(input_df)
+
+    # Extracting feature names for categorical columns after one-hot encoding
+    cat_encoder = pipeline.named_steps['preprocessor'].named_transformers_['cat'].named_steps['onehot']
+    cat_feature_names = cat_encoder.get_feature_names_out(cat_cols)
+
+    # Concatenating numerical and categorical feature names
+    feature_names = num_cols + list(cat_feature_names)
+
+    # Convert X_processed to DataFrame
+    final_df = pd.DataFrame(X_processed, columns=feature_names)
+
+    # Extract the first three columns and remaining columns, then merge
+    first_three_columns = final_df.iloc[:, :3]
+    remaining_columns = final_df.iloc[:, 3:]
+    final_df = pd.concat([remaining_columns, first_three_columns], axis=1)
+
+    # Make predictions using the model
+    prediction_probs = model.predict_proba(final_df)[0]
+    prediction_label = {
+        "Prediction: CHURN 🔴": prediction_probs[1],
+        "Prediction: STAY ✅": prediction_probs[0]
+    }
+
+    return prediction_label
+