diff --git a/Models/NeuralNetworks/custom_neural_network.py b/Models/NeuralNetworks/custom_neural_network.py
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+++ b/Models/NeuralNetworks/custom_neural_network.py
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+
+#%% packages
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+
+#%% data prep
+# source: https://www.kaggle.com/datasets/rashikrahmanpritom/heart-attack-analysis-prediction-dataset
+df = pd.read_csv('heart.csv')
+df.head()
+
+#%% separate independent / dependent features
+X = np.array(df.loc[ :, df.columns != 'output'])
+y = np.array(df['output'])
+
+print(f"X: {X.shape}, y: {y.shape}")
+
+#%% Train / Test Split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12)
+
+#%% scale the data
+scaler = StandardScaler()
+X_train_scale = scaler.fit_transform(X_train)
+X_test_scale = scaler.transform(X_test)
+
+#%% network class
+class CustomNeuralNetwork:
+    def __init__(self, learning_rate, X_train, y_train, X_test, y_test):
+        self.weights = np.random.randn(X_train.shape[1])
+        self.biases = np.random.randn()
+        self.learning_rate = learning_rate
+        self.X_train = X_train
+        self.y_train = y_train
+        self.X_test = X_test
+        self.y_test = y_test
+        self.learning_losses_train = []
+        self.learning_losses_test = []
+
+    def activationFunction(self, x):
+        # sigmoid
+        return 1 / (1 + np.exp(-x))
+    
+    def activationDerivative(self, x):
+        # sigmoid derivative
+        return self.activationFunction(x) * (1 - self.activationFunction(x))
+
+    def forwardPass(self, derivative_x):
+        hidden_layer_1 = np.dot(derivative_x, self.weights) + self.biases
+        activate_layer = self.activationFunction(hidden_layer_1)
+
+        return activate_layer
+
+    def backwardPass(self, independent_features, y_true_values):
+        # calculcate gradients1
+        hidden_layer_1 = np.dot(independent_features, self.weights) + self.biases
+        y_predictions = self.forwardPass(independent_features)
+
+        derivative_of_losses_to_predictions = 2 * (y_predictions - y_true_values)
+        derivative_of_predictions_to_hidden_layer = self.activationDerivative(hidden_layer_1)
+        derivative_of_hidden_layer_biases = 1
+        derivative_of_hidden_layer_weights = independent_features
+
+        derivatives_biases = derivative_of_losses_to_predictions * derivative_of_predictions_to_hidden_layer * derivative_of_hidden_layer_biases
+        derivatives_weights = derivative_of_losses_to_predictions * derivative_of_predictions_to_hidden_layer * derivative_of_hidden_layer_weights
+
+        return derivatives_biases, derivatives_weights
+    
+    def optimiser(self, derivatives_biases, derivatives_weights):
+        # update weights
+        self.biases = self.biases - derivatives_biases * self.learning_rate
+        self.weights = self.weights - derivatives_weights * self.learning_rate
+
+    def train(self, iterations):
+        for iteration in range(iterations):
+            # get random position
+            random_position = np.random.randint(len(self.X_train))
+
+            # forward pass
+            y_train_true = self.y_train[random_position]
+            y_train_predictictions = self.forwardPass(self.X_train[random_position])
+
+            # calculate training losses
+            losses = np.sum(np.square(y_train_predictictions - y_train_true))
+            self.learning_losses_train.append(losses)
+
+            # calculate gradients
+            derivatives_biases, derivatives_weights = self.backwardPass(self.X_train[random_position], self.y_train[random_position])
+
+            # update rates
+            self.optimiser(derivatives_biases, derivatives_weights)
+
+            # calculate errors for test data
+            losses_sum = 0
+            for i in range(len(self.X_test)):
+                y_true_value = self.y_test[i]
+                y_predictions = self.forwardPass(self.X_test[i])
+
+                losses_sum += np.square(y_predictions - y_true_value)
+            self.learning_losses_test.append(losses_sum)
+        return 'training done'
+
+#%% Hyper parameters
+learning_rate = 0.2
+iterations = 1000
+
+#%% model instance and training
+neural_network = CustomNeuralNetwork(learning_rate, X_train_scale, y_train, X_test_scale, y_test)
+
+neural_network.train(iterations)
+
+# %% check losses
+sns.lineplot(x=list(range(len(neural_network.learning_losses_test))), y=neural_network.learning_losses_test)
+
+# %% iterate over test data
+total_observations = X_test_scale.shape[0]
+correct_predictions = 0
+y_preditctions = []
+for i in range(total_observations):
+    y_true = y_test[i]
+    y_preditction = np.round(neural_network.forwardPass(X_test_scale[i]))
+    y_preditctions.append(y_preditction)
+    correct_predictions += 1 if y_true == y_preditction else 0
+
+# %% Calculate Accuracy
+correct_predictions / total_observations
+
+# %% Baseline Classifier
+from collections import Counter
+Counter(y_test)
+
+# %% Confusion Matrix
+cm = confusion_matrix(y_true=y_test, y_pred=y_preditctions)
+
+
+# %%
+import matplotlib.pyplot as plt
+disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['true', 'false'])
+
+disp.plot()
+plt.show()
+
+# %%