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					@ -161,7 +161,7 @@ def preprocess(type_of_model,  type_of_activity):
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def draw_confusion_matrix(y_test, y_pred, model):
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					def draw_confusion_matrix(y_test, y_pred, model):
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    conf_matrix = confusion_matrix(y_test, y_pred)
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					    conf_matrix = confusion_matrix(y_test, y_pred)
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    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])
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					    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'], annot_kws={"size": 14})
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    plt.xlabel('Predicted')
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					    plt.xlabel('Predicted')
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    plt.ylabel('Actual')
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					    plt.ylabel('Actual')
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    plt.title('Confusion Matrix')
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					    plt.title('Confusion Matrix')
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					@ -180,10 +180,10 @@ def draw_roc_curve(X_test, y_pred_prob, model):
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    plt.plot(fpr, tpr, label="ROC curve(area = %0.3f)" % roc_auc)
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					    plt.plot(fpr, tpr, label="ROC curve(area = %0.3f)" % roc_auc)
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    plt.plot([0, 1], [0, 1], color="red",label="Random Baseline", linestyle="--")
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					    plt.plot([0, 1], [0, 1], color="red",label="Random Baseline", linestyle="--")
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    plt.grid(color='gray', linestyle='--', linewidth=0.5)
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					    plt.grid(color='gray', linestyle='--', linewidth=0.5)
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    plt.xlabel("False Positive Rate")
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					    plt.xlabel("False Positive Rate", fontsize=14)
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    plt.ylabel("True Positive Rate")
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					    plt.ylabel("True Positive Rate", fontsize=14)
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    plt.title("ROC Curve", size=18)
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					    plt.title("ROC Curve", size=18)
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    plt.legend(loc="lower right")
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					    plt.legend(loc="lower right", fontsize=14)
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    plt.show()
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					    plt.show()
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    save_file_s3("Roc_curve_", type_of_activity, type_of_model, model)
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					    save_file_s3("Roc_curve_", type_of_activity, type_of_model, model)
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