| import gradio as gr |
| import main |
| import numpy as np |
| import pandas as pd |
| from main import clf_rf, clf_log, accuracy_score_rf, accuracy_score_lr, brier_score_rf, brier_score_lr, roc_rf, roc_lr, logistic, rf_clf, encoder, scaler |
|
|
| def eda(Graphs): |
| match Graphs: |
| case "Customer Churn": |
| return gr.Image("graphs/EDAGraphs/Churn.png") |
| case "Contract": |
| return gr.Image("graphs/EDAGraphs/Contract.png") |
| case "Dependents": |
| return gr.Image("graphs/EDAGraphs/Dependents.png") |
| case "Device Protection": |
| return gr.Image("graphs/EDAGraphs/DeviceProtection.png") |
| case "Heatmap": |
| return gr.Image("graphs/EDAGraphs/Heatmap.png") |
| case "Monthly Charges": |
| return gr.Image("graphs/EDAGraphs/MonthlyCharges.png") |
| case "Online Backup": |
| return gr.Image("graphs/EDAGraphs/OnlineBackup.png") |
| case "Online Security": |
| return gr.Image("graphs/EDAGraphs/OnlineSecurity.png") |
| case "Paperless Billing": |
| return gr.Image("graphs/EDAGraphs/PaperlessBilling.png") |
| case "Partner": |
| return gr.Image("graphs/EDAGraphs/Partner.png") |
| case "Payment Method": |
| return gr.Image("graphs/EDAGraphs/PaymentMethod.png") |
| case "Senior Citizen": |
| return gr.Image("graphs/EDAGraphs/SeniorCitizen.png") |
| case "Tech Support": |
| return gr.Image("graphs/EDAGraphs/TechSupport.png") |
| case "Tenure": |
| return gr.Image("graphs/EDAGraphs/tenure.png") |
|
|
| def result(Graphs): |
| match Graphs: |
| case "Correlation": |
| inf = """ |
| Inferences: |
| |
| - Churn is perfectly correlated with itself (1.0). |
| |
| - Tenure and Contract have strong negative correlation with Churn. |
| |
| - MonthlyCharges and PaperlessBilling have moderate positive correlation with Churn. |
| |
| - Gender, PhoneService, and MultipleLines have near-zero correlation. |
| |
| - OnlineSecurity, TechSupport, and DeviceProtection have moderate negative correlation. |
| """ |
| return [gr.Image("graphs/EDAGraphs/Correlation.png"), inf] |
| case "Confusion Matrix for Random Forest": |
| inf = """ |
| Inferences: |
| |
| - True Positives (181) are lower than Logistic Regression’s (215), indicating worse recall for churn. |
| |
| - False Negatives (192) are higher – more churners are missed. |
| |
| - False Positives (104) are slightly higher than logistic regression’s — marginally more false alarms. |
| |
| - True Negatives (932) are comparable – performs similarly for non-churn cases. |
| |
| - Overall: Slightly poorer at identifying churn compared to Logistic Regression in this case. |
| |
| """ |
| return [gr.Image("graphs/OutputGraphs/ConfusionMatrixRandomForest.png"), inf] |
| case "Confusion Matrix for Logistic regression": |
| inf = """ |
| Inferences: |
| |
| - True Negatives (936) and True Positives (215) indicate good performance in correctly identifying both classes. |
| |
| - False Negatives (158): A moderate number of actual churns are missed, which could be critical in business decisions. |
| |
| - False Positives (100): Some non-churning customers are predicted to churn — might lead to unnecessary retention efforts. |
| |
| - Model Bias: Slight bias towards predicting the majority class (non-churn). |
| |
| - Overall: Reasonable balance, but recall for churn could be improved. |
| |
| """ |
| return [gr.Image("graphs/OutputGraphs/ConfusionMatrixLogistic.png"), inf] |
| case "SHAP analysis for Random Forest": |
| inf = """ |
| Inferences: |
| |
| - Bimodal Distribution: Indicates two clear groups — likely senior citizens and non-seniors with distinct behavior patterns. |
| |
| - Near-Zero SHAP values: SeniorCitizen has limited standalone predictive power. |
| |
| - Interaction: The variable may influence predictions when combined with other features (e.g., internet service or contract). |
| |
| - Red vs Blue Dots: Represents SHAP values across classes — they’re symmetric, confirming weak influence. |
| |
| - Overall: SeniorCitizen is not a key predictor by itself in this model. |
| """ |
| return [gr.Image("graphs/OutputGraphs/SHAP_RandomForest_Summary.png"), inf] |
| case "SHAP analysis for Logistic Regression": |
| inf = """ |
| Inferences: |
| |
| - Top Feature: tenure is the most influential in predicting churn — lower tenure likely increases churn risk. |
| |
| - MonthlyCharges & Contract also have strong effects — customers on monthly or expensive plans may churn more. |
| |
| - Security-related services (e.g., OnlineSecurity, TechSupport) have moderate influence — presence may reduce churn. |
| |
| - PaperlessBilling and OnlineBackup show notable contributions, possibly associated with digital-savvy customers. |
| |
| - Features like DeviceProtection, Partner, and PaymentMethod have minimal impact. |
| """ |
| return [gr.Image("graphs/OutputGraphs/SHAP_Logistic_Summary.png"), inf] |
|
|
| def metrics(Algorithms): |
| match Algorithms: |
| case "Random Forest": |
| df = pd.DataFrame(clf_rf) |
| df = df.drop(columns = ['accuracy', 'macro avg', 'weighted avg']).T |
| df = df.reset_index().rename(columns={'index': 'class'}) |
| df_clf = gr.DataFrame( |
| value = df |
| ) |
| df_acc = gr.DataFrame( |
| headers = ['Accuracy Score', 'Brier Score', 'ROC Score'], |
| value = [list([accuracy_score_rf, brier_score_rf, roc_rf])], |
| ) |
| return df_clf, df_acc |
| |
| case "Logistic Regression": |
| df = pd.DataFrame(clf_log) |
| df = df.drop(columns = ['accuracy', 'macro avg', 'weighted avg']).T |
| df = df.reset_index().rename(columns={'index': 'class'}) |
| df_clf = gr.DataFrame( |
| value = df |
| ) |
| df_acc = gr.DataFrame( |
| headers = ['Accuracy Score', 'Brier Score', 'ROC Score'], |
| value = [list([accuracy_score_lr, brier_score_lr, roc_lr])], |
| ) |
| return df_clf, df_acc |
|
|
| def predictChurn(model, filename): |
| dataset = pd.read_csv(filename) |
|
|
| customers = dataset["customerID"] |
| dataset.drop(columns=['customerID'], inplace=True) |
|
|
| for column in dataset.select_dtypes(include=['int64', 'float64']).columns: |
| dataset[column] = scaler.fit_transform(dataset[column].values.reshape(-1, 1)) |
| for column in dataset.select_dtypes(include=['object']).columns: |
| dataset[column] = encoder.fit_transform(dataset[column]) |
|
|
| dataset.drop(columns=['gender', 'PhoneService', 'MultipleLines', 'InternetService', 'StreamingTV', 'StreamingMovies', 'TotalCharges'], inplace=True) |
|
|
| match model: |
| case "Logistic Regression": |
| model = logistic |
| case "Random Forest": |
| model = rf_clf |
|
|
| y_predictions = model.predict(dataset) |
| print(y_predictions) |
| d = {0: "No Churn", 1: "Churn"} |
|
|
| l = zip(customers, y_predictions) |
|
|
| df = pd.DataFrame(l, columns= ["Customer ID", "Output"]) |
|
|
| for i in range(len(df["Customer ID"])): |
| output = d[y_predictions[i]] |
| df["Output"][i] = output |
|
|
| return gr.DataFrame(value = df) |
|
|
| with gr.Blocks() as Output: |
| gr.Markdown("View Exploratory data Analysis and Output") |
| with gr.Tab("EDA Graphs"): |
| eda_input = gr.Radio(["Customer Churn", "Contract", "Dependents", "Device Protection", "Heatmap", "Monthly Charges", "Online Backup", "Online Security", "Paperless Billing", "Partner", "Payment Method", "Senior Citizen", "Tech Support", "Tenure"], show_label= False) |
| eda_output = gr.Image() |
|
|
| eda_input.change(fn = eda, inputs= eda_input, outputs= eda_output) |
|
|
| with gr.Tab("Output Graphs"): |
| result_input = gr.Radio(["Correlation", "Confusion Matrix for Random Forest", "Confusion Matrix for Logistic regression", "SHAP analysis for Random Forest", "SHAP analysis for Logistic Regression"], show_label = False) |
|
|
| result_output = [gr.Image(), gr.Markdown()] |
|
|
| result_input.change(fn = result, inputs=result_input, outputs = result_output) |
|
|
| with gr.Tab("Performance Metrics"): |
| algorithm = gr.Radio(["Random Forest", "Logistic Regression"], show_label= False) |
|
|
| metrics_output = [gr.DataFrame(), gr.DataFrame()] |
|
|
| algorithm.change(fn = metrics, inputs = algorithm, outputs = metrics_output) |
|
|
| with gr.Tab("Predict Live"): |
| gr.Markdown("# Predict Churn") |
| model = gr.Radio(["Logistic Regression", "Random Forest"], show_label = False) |
| file = gr.File() |
| dataset = gr.UploadButton("Upload Dataset(as CSV file)", file_count = "single") |
| predict = gr.Button("Predict") |
|
|
| op_df = gr.DataFrame(headers = ["Customer ID", "Output"]) |
| op_md = gr.Markdown("# Predicted Churns") |
|
|
| clear = gr.ClearButton(components = [model, file, dataset, op_df]) |
|
|
| dataset.upload(lambda file: file, dataset, file) |
| predict.click(fn = predictChurn, inputs = [model, dataset], outputs = op_df) |
| clear.click() |
|
|
| Output.launch() |
