Final Update readme

README.md

@@ -51,16 +51,16 @@ The effectiveness of the KNN algorithm was evaluated based on the number of neig
 
 ### Running the Code
 To execute the models, follow these steps in the terminal:
-bash
+
 # Ensure requirements are installed before running KNN or MLP
 pip install -r requirements.txt
 
 1. KNN Model:
-bash
+
 python knn.py
 
 2. MLP Model:
-bash
+
 python mlp.py 
 
 ## Results :
@@ -79,7 +79,7 @@ A graph showing the accuracy variation with the number of epochs was generated u
 ![Semantic description of image](Results/mlp.png)
 
 ## Analysis of KNN Results
-Unfortunately, the performance of the KNN algorithm was disappointing, with accuracy ranging between 33% and 36% for different values of k (up to k=20). Several reasons may explain these mixed results:
+Unfortunately, the performance of the KNN algorithm was disappointing, with accuracy ranging between 33% and 34% for different values of k (up to k=20). Several reasons may explain these mixed results:
 
 1. *High Dimensionality of Data*: CIFAR-10 dataset images are 32x32 pixels, resulting in high-dimensional data. This can make Euclidean distance less discriminative, affecting KNN's performance.
 
@@ -95,7 +95,7 @@ The deep learning algorithm (ANN) used for our dataset has relatively low perfor
 These results suggest that adjustments to certain aspects of the model, such as complexity, hyperparameters, or weight initialization, may be necessary to improve its ability to generalize to new data. Further exploration of these aspects could be beneficial in optimizing model performance.
 
  ## Conculsion 
- The best accuracy is achieved with the KNN model, reaching 36%. However, it could be further improved by using Convolutional Neural Networks (CNN) instead of Artificial Neural Networks (ANN). CNNs are particularly recognized for their effectiveness in image recognition, analysis, and classification of images and videos.
+ The best accuracy is achieved with the KNN model, reaching 34%. However, it could be further improved by using Convolutional Neural Networks (CNN) instead of Artificial Neural Networks (ANN). CNNs are particularly recognized for their effectiveness in image recognition, analysis, and classification of images and videos.
 
 ## Author
 Sara EL ALIMI