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@@ -24,10 +24,60 @@ Here we can conclude that the best K is 5, (if we don't use k = 1)  with a perfo
 ### Maths 
 1° 
 
+![knn](mlp_maths/q1.PNG)
+
+2°
+
+![knn](mlp_maths/q2.PNG)
+
+3°
+
+![knn](mlp_maths/q3.PNG)
+
+4°
+
+![knn](mlp_maths/q4.PNG)
+
+5°
+
+![knn](mlp_maths/q5.PNG)
+
+6°
+
+![knn](mlp_maths/q6.PNG)
+
+7°
+
+![knn](mlp_maths/q7.PNG)
+
+8°
+
+![knn](mlp_maths/q8.PNG)
+
+9°
+
+![knn](mlp_maths/q9.PNG)
+
+
+
 
 ​
 
 ### Code
 
+All the code can be found in the Python file mlp.py.
+
+Below, you will find the graph of accuracy as a function of the number of epochs. We used a learning rate of 0.1 and a split ratio of 0.9 between the training and testing datasets.
+
+![mlp](results/mlp.png)
+
+
+
+Firstly, we observe that accuracy increases with each epoch.
+
+However, after 100 epochs, the accuracy is around 16.2%, which is about half the accuracy achieved by the KNN method.
+
+In conclusion, the MLP method is somewhat disappointing. It might be improved by increasing the number of epochs or adjusting the learning rate. I also observed that the MLP method was faster than the KNN method.
+
 
 
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