Visualizing Machine Learning

I started the tensorflow skillpath in codecademy and i just finished the project dealing with the cardiovascular stuff and after finishing the project i feel that it lacks visual representation, at the end of the project it just shows me loss and accuracy of the model but i don’t see how that will help predict patients with high risk.

project link:
my code:

import pandas as pd from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.model_selection import train_test_split from collections import Counter from sklearn.compose import ColumnTransformer from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, InputLayer from sklearn.metrics import classification_report from tensorflow.keras.utils import to_categorical import numpy as np data = pd.read_csv('heart_failure.csv') #print( print(Counter(data['death_event'])) y = data.iloc[:,-1:] print( x = data.iloc[:, 1:-2] print( x = pd.get_dummies(x) X_train, X_test, Y_train, Y_test = train_test_split(x,y,test_size = 0.25, random_state = 57) ct = ColumnTransformer([('numeric', StandardScaler(), ['age','creatinine_phosphokinase','ejection_fraction','platelets','serum_creatinine','serum_sodium','time'])]) X_train = ct.fit_transform(X_train) X_test = ct.transform(X_test) le = LabelEncoder() Y_train = le.fit_transform(Y_train.astype(str)) Y_test = le.transform(Y_test.astype(str)) Y_train = to_categorical(Y_train) Y_test = to_categorical(Y_test) model = Sequential() model.add(InputLayer(input_shape = (X_train.shape[1],))) model.add(Dense(12, activation = 'relu')) model.add(Dense(2, activation = 'softmax')) model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy']), Y_train, epochs = 100, batch_size = 16, verbose = 1) loss, acc = model.evaluate(X_test, Y_test, verbose=1) print("Loss", loss, "Accuracy:", acc) y_estimate = model.predict(X_test, verbose = 0) y_estimate = np.argmax(y_estimate, axis=1) y_true = np.argmax(Y_test, axis=1) print(classification_report(y_true, y_estimate))