diff --git a/scripts/Confusion_Matrix/README.md b/scripts/Confusion_Matrix/README.md
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+# CODE BY ZeroToMastery TensorFlow course.
+# The function makes a labelled confusion matrix comparing predictions and ground truth labels.
+  
+# If classes is passed, confusion matrix will be labelled, if not, integer class values
+# will be used.
+
+## Args:
+
+`y_true`: Array of truth labels (must be same shape as y_pred).
+`y_pred`: Array of predicted labels (must be same shape as y_true).
+`classes`: Array of class labels (e.g. string form). If `None`, integer labels are used.
+`figsize`: Size of output figure (default=(10, 10)).
+`text_size`: Size of output figure text (default=15).
+`norm`: normalize values or not (default=False).
+`savefig`: save confusion matrix to file (default=False).
+
+## Returns: A labelled confusion matrix plot comparing y_true and y_pred.
+### Example usage:
+
+"""make_confusion_matrix(y_true=test_labels, # ground truth test labels
+                        y_pred=y_preds, # predicted labels
+                        classes=class_names, # array of class label names
+                        figsize=(15, 15),
+                        text_size=10)"""
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diff --git a/scripts/Confusion_Matrix/make_confusion_matrix.py b/scripts/Confusion_Matrix/make_confusion_matrix.py
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+import itertools
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import confusion_matrix
+
+def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15, norm=False, savefig=False): 
+  # Create the confustion matrix
+  cm = confusion_matrix(y_true, y_pred)
+  cm_norm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] # normalize it
+  n_classes = cm.shape[0] # find the number of classes we're dealing with
+
+  # Plot the figure and make it pretty
+  fig, ax = plt.subplots(figsize=figsize)
+  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better
+  fig.colorbar(cax)
+
+  # Are there a list of classes?
+  if classes:
+    labels = classes
+  else:
+    labels = np.arange(cm.shape[0])
+  
+  # Label the axes
+  ax.set(title="Confusion Matrix",
+         xlabel="Predicted label",
+         ylabel="True label",
+         xticks=np.arange(n_classes), # create enough axis slots for each class
+         yticks=np.arange(n_classes), 
+         xticklabels=labels, # axes will labeled with class names (if they exist) or ints
+         yticklabels=labels)
+  
+  # Make x-axis labels appear on bottom
+  ax.xaxis.set_label_position("bottom")
+  ax.xaxis.tick_bottom()
+
+  # Set the threshold for different colors
+  threshold = (cm.max() + cm.min()) / 2.
+
+  # Plot the text on each cell
+  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+    if norm:
+      plt.text(j, i, f"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)",
+              horizontalalignment="center",
+              color="white" if cm[i, j] > threshold else "black",
+              size=text_size)
+    else:
+      plt.text(j, i, f"{cm[i, j]}",
+              horizontalalignment="center",
+              color="white" if cm[i, j] > threshold else "black",
+              size=text_size)
+
+  # Save the figure to the current working directory
+  if savefig:
+    fig.savefig("confusion_matrix.png")
+
+y_true = [0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0]
+y_pred = [0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0]
+
+make_confusion_matrix(y_true, y_pred)
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