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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() |