How come this code is not presenting any errors, but also fails to print the desired code or display the graph

import pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn import metricsairline_data = pd.read_csv('airlines-corgis.csv')airline_data['CarrierNames'] = airline_data['CarrierNames'].str.split(',')airline_data['TotalCarriers'] = airline_data['CarrierNames'].apply(len)airline_data = airline_data.explode('CarrierNames')airline_data['Time'] = pd.to_numeric(airline_data['Time'], errors='coerce')airline_data_encoded = pd.get_dummies(airline_data, columns=          ['CarrierNames','Code','Name','MonthName'])X = airline_data_encoded.drop(['Delays-Carrier', 'Delays-LateAircraft', 'Delays-   NAS', 'Delays-Security', 'Delays-Weather'], axis=1)y = airline_data_encoded['Delayed']X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,   random_state=16)my_forest = RandomForestClassifier()my_forest.fit(X_train, y_train)y_pred = my_forest.predict(X_test)cnf_matrix = metrics.confusion_matrix(y_test, y_pred, normalize='true')plt.figure(figsize=(8, 6))sns.heatmap(cnf_matrix, annot=True, fmt='.2f',        xticklabels=my_forest.classes_,        yticklabels=my_forest.classes_)plt.title('Normalized Confusion Matrix')plt.xlabel('Predicted Value')plt.ylabel('True Value')plt.show()print('Length of X_train:', len(X_train))print('Feature Importances:', my_forest.feature_importances_)

I have been trying to create a random forest classifier, heat map, and determine the most important features impacting delays (which delay type), but when I run the code, nothing appears.