How to do it...

Here is the code block that plots multiple ROC curves on the same axes:

1. Read the FPR and TPR data for various algorithms from their respective Excel sheets using pandas:

# K-nearest neighbor (KNN)
fpr_KNN = pd.read_excel('ROC_Curves.xlsx', 'fpr_KNN')
tpr_KNN = pd.read_excel('ROC_Curves.xlsx', 'tpr_KNN')

# Multilayer Perceptron(MLP)
fpr_MLP = pd.read_excel('ROC_Curves.xlsx', 'fpr_MLP')
tpr_MLP = pd.read_excel('ROC_Curves.xlsx', 'tpr_MLP')

# Stochastic Gradient Descent (SGD)
fpr_SGD = pd.read_excel('ROC_Curves.xlsx', 'fpr_SGD')
tpr_SGD = pd.read_excel('ROC_Curves.xlsx', 'tpr_SGD')

# Random Forest (RF)
fpr_RF = pd.read_excel('ROC_Curves.xlsx', 'fpr_RF')
tpr_RF = pd.read_excel('ROC_Curves.xlsx', 'tpr_RF')

# Decision Trees (DT)
fpr_DT = pd.read_excel('ROC_Curves.xlsx', 'fpr_DT')
tpr_DT = pd.read_excel('ROC_Curves.xlsx', 'tpr_DT')

2. Plot the line graphs, including the black threshold line, using fpr and tpr data, read for each of the five algorithms in the previous step:

plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr_KNN, tpr_KNN, label='KNN',color='green')
plt.plot(fpr_DT, tpr_DT, label='DecisionTree', color='orange')
plt.plot(fpr_RF, tpr_RF, label='Random Forest',color='purple')
plt.plot(fpr_MLP, tpr_MLP, label='MLP',color='red')
plt.plot(fpr_SGD, tpr_SGD, label='SGD', color='pink')

3. Print the labels, the title, and the legend:

plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC curve')
plt.legend(loc='best')

4. Display the plot on the screen:

plt.show()