add cm plot

scripts/logistic.py

@@ -2,7 +2,7 @@ import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
 from sklearn.preprocessing import LabelEncoder
-from sklearn.metrics import accuracy_score, classification_report
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
 import pickle
 import statsmodels.api as sm
 import matplotlib.pyplot as plt
@@ -38,6 +38,18 @@ accuracy = accuracy_score(y_test, y_pred)
 classification_report_result = classification_report(y_test, y_pred)
 #sns.heatmap(pd.DataFrame(classification_report_result).iloc[:-1, :].T, annot=True)
 
+
+# Calculate the confusion matrix
+confusion = confusion_matrix(y_test, y_pred)
+
+# Plot the confusion matrix using Seaborn
+plt.figure(figsize=(8, 6))
+sns.heatmap(confusion, annot=True, fmt='d', cmap='Blues', linewidths=0.5)
+plt.xlabel('Predicted')
+plt.ylabel('Actual')
+plt.title('Confusion Matrix')
+plt.show()
+
 print(f"Accuracy: {accuracy}")
 print("Classification Report:")
 print(classification_report_result)