diff --git a/src/experiments/knn/knn.py b/src/experiments/knn/knn.py
index 4269d30..c5d4617 100644
--- a/src/experiments/knn/knn.py
+++ b/src/experiments/knn/knn.py
@@ -1,10 +1,12 @@
 import cv2 as cv
 import numpy as np
 import matplotlib.pyplot as plt
+import seaborn as sns
 import csv
 from sklearn.preprocessing import MinMaxScaler
 from enum import Enum
 import random
+from sklearn.metrics import confusion_matrix
 
 class Tree(Enum):
     ACCASIA = 0
@@ -27,17 +29,16 @@ print("CSV tags: ", header)
 
 # Get classifier tags
 tags_int = []
+
 for row in data:
     tree = row.pop(0)
     id = Tree[tree.upper()]
     
     # print("Tree name =", tree, " id =", id.value)
     tags_int.append(id.value)
-    
-# Get a random number for testing
-validateId = random.randint(0, len(tags_int) - 1)
 
 # Make into numpy array cus OpenCV is dumb af
+tags_len = len(tags_int)
 tags_int = np.array(tags_int, dtype=np.int32)
 
 # Transform array for normalisation
@@ -63,22 +64,42 @@ for idx, col in enumerate(data[0]):
     # Replace original data array
     data[:, idx] = column
 
-validateTag = tags_int[validateId]
-validateObj =np.array([data[validateId]])
-np.delete(tags_int, validateId)
-np.delete(data, validateTag)
+# # Get a random number for testing
+# validateId = random.randint(0, tags_len - 1)
+tag_true = []
+tag_predict = []
 
-print(validateTag, validateObj)
+print(tags_len)
 
-knn = cv.ml.KNearest_create()
-knn.train(data, cv.ml.ROW_SAMPLE, tags_int)
+for validateId in range(0, tags_len - 1):
+    # Remove object from train set
+    validateTag = tags_int[validateId]
+    validateObj =np.array([data[validateId]])
+    np.delete(tags_int, validateId)
+    np.delete(data, validateTag)
 
-# print (data)
-# print('--------------------')
-# print (validateObj)
+    tag_true.append(validateTag)
 
-ret, results, neighbours ,dist = knn.findNearest(validateObj, 3)
+    # print(validateTag, validateObj)
 
-print( "result: {}\n".format(results) )
-print( "neighbours: {}\n".format(neighbours) )
-print( "distance: {}\n".format(dist) )
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+    knn = cv.ml.KNearest_create()
+    print(tags_int)
+    print(data.dtype, type(data), tags_int.dtype, type(tags_int))
+    knn.train(data, cv.ml.ROW_SAMPLE, tags_int)
+
+    # print (data)
+    # print('--------------------')
+    # print (validateObj)
+
+    ret, results, neighbours ,dist = knn.findNearest(validateObj, 3)
+    tag_predict.append(results[0][0])
+    
+    # print( "result: {}\n".format(results) )
+    # print( "neighbours: {}\n".format(neighbours) )
+    # print( "distance: {}\n".format(dist) )
+    
+
+# Create a heatmap
+sns.heatmap(confusion_matrix(tag_true, tag_predict), annot=True)
+plt.title( "Confusion Matrix KNN" )
+plt.show()
\ No newline at end of file