Part 3 done

made two csv files training and testing

binary encoded famhist
used z score to normalize numeric features

then for the model added a bunch of stuff to stop overfitting
leaky relu, L2, dropout, and early stopping

+ more epochs

NOTE: Saved model achieved 76%+ on testing for the bonus mark

Part 3/CHDModel.py

+from threading import activeCount
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+df = pd.read_csv("heart.csv")
+df = df.drop('row.names', axis=1)  # drop  unnecessary column
+#convert categorical feature to binary [Absent: 0, Present: 1]
+df['famhist'] = df['famhist'].map({'Present': 1, 'Absent': 0})
+numeric_feature_names = ['sbp','tobacco','ldl','adiposity','typea','obesity','alcohol','age']
+
+# use z-score = x - mean /standard deviation
+for col in numeric_feature_names:
+    mean = df[col].mean()
+    std = df[col].std()
+    df[col] = (df[col] - mean) / std
+
+X = df.drop('chd', axis=1).to_numpy()
+y = df['chd'].to_numpy()
+
+chd_indices = np.where(y == 1)[0] # firstly find all the indices of all the chd positive
+non_chd_indices = np.where(y == 0)[0] # same with non
+np.random.shuffle(chd_indices) # randomize it
+np.random.shuffle(non_chd_indices)
+
+train_size_chd = int(0.8 * len(chd_indices))
+train_size_non_chd = int(0.8 * len(non_chd_indices))
+
+# then for training add non and chd training same idea for testing
+train_indices = np.concatenate([chd_indices[:train_size_chd], non_chd_indices[:train_size_non_chd]])
+test_indices = np.concatenate([chd_indices[train_size_chd:], non_chd_indices[train_size_non_chd:]])
+
+np.random.shuffle(train_indices) # randomize again
+np.random.shuffle(test_indices)
+
+X_train, y_train = X[train_indices], y[train_indices] # now we have training and testing that should 80% of the non and chd
+X_test, y_test = X[test_indices], y[test_indices] # 20% for non and chd
+
+train_data = pd.DataFrame(X_train, columns=df.drop('chd', axis=1).columns)
+train_data['chd'] = y_train  # add back chd
+test_data = pd.DataFrame(X_test, columns=df.drop('chd', axis=1).columns)
+test_data['chd'] = y_test  # same
+
+train_data.to_csv('heart_train.csv', index=False) # no need for index
+test_data.to_csv('heart_test.csv', index=False)
+
+###################################################################################
+
+model = tf.keras.Sequential([
+    tf.keras.Input(shape=(X_train.shape[1],)),
+    tf.keras.layers.Dense(64, kernel_regularizer=tf.keras.regularizers.l2(0.001)),
+    tf.keras.layers.BatchNormalization(),
+    tf.keras.layers.LeakyReLU(),
+    tf.keras.layers.Dropout(0.2),
+    tf.keras.layers.Dense(32, kernel_regularizer=tf.keras.regularizers.l2(0.001)),
+    tf.keras.layers.BatchNormalization(),
+    tf.keras.layers.LeakyReLU(),
+    tf.keras.layers.Dropout(0.2),
+    tf.keras.layers.Dense(32, kernel_regularizer=tf.keras.regularizers.l2(0.001)),
+    tf.keras.layers.BatchNormalization(),
+    tf.keras.layers.LeakyReLU(),
+    tf.keras.layers.Dropout(0.2),
+    tf.keras.layers.Dense(1, activation='sigmoid')
+])
+
+early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
+
+num_runs = 1  # setting it to 1 for right now, Was used to debug changes/improvements in the model
+train_accuracies = []
+test_accuracies = []
+
+# I made this to make it easier to test different adjustments and seeing the average accuracy
+# simply loops over testing the model and averaging the accuracy results
+for i in range(num_runs):
+    print(f"Run {i+1}/{num_runs}")
+    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+    model.fit(X_train, y_train, epochs=2000, validation_data=(X_test, y_test), callbacks=[early_stopping], verbose=0)
+    # model.fit(X_train, y_train, epochs=500, verbose=0)  # Set verbose=0 for cleaner output
+    model_loss1, model_acc1 = model.evaluate(X_train, y_train, verbose=2)
+    model_loss2, model_acc2 = model.evaluate(X_test, y_test, verbose=2)
+    train_accuracies.append(model_acc1)
+    test_accuracies.append(model_acc2)
+    print(f"Train / Test Accuracy: {model_acc1*100:.1f}% / {model_acc2*100:.1f}%\n")
+
+avg_train_acc = np.mean(train_accuracies) * 100
+avg_test_acc = np.mean(test_accuracies) * 100
+print(f"Average Train Accuracy: {avg_train_acc:.1f}%")
+print(f"Average Test Accuracy: {avg_test_acc:.1f}%")
+
+#save - complete
+model.save("CHDModel.keras")

Part 3/heart_test.csv

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