diff --git a/Report_Klaudia_Przybylska.md b/Report_Klaudia_Przybylska.md
index f769860..d5a3dfb 100644
--- a/Report_Klaudia_Przybylska.md
+++ b/Report_Klaudia_Przybylska.md
@@ -13,6 +13,7 @@ Before running the program it is obligatory to unpack "Garbage classifier.rar" a
 ## Extracting information from images
 In order to use Random Forest Classifier to classify pictures, I used three global feature descriptors:
 * Hu Moments - responsible for capturing information about shapes because they have information about intensity and position of pixels. They are invariant to image transformations (unlike moments or central moments).
+
 ```
 def hu_moments(image):
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
@@ -20,7 +21,9 @@ def hu_moments(image):
     huMoments = cv2.HuMoments(moments).flatten()
     return huMoments
 ```
+
 * Color histogram - representation of the distribution of colors in an image.
+
 ```
 def histogram(image, mask=None):
     image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
@@ -30,15 +33,19 @@ def histogram(image, mask=None):
     return histogram
 ```
 * Haralick Texture is used to quantify an image based on texture (the consistency of patterns and colors in an image).
+
 ```
 def haralick(image):
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     haralick = mahotas.features.haralick(gray).mean(axis=0)
     return haralick
+
 ```
 * All three features are then stacked into one matrix and used in training the classifier, and in the same way for testing it.
+
 ```
 allFeatures = np.hstack([histo, hara, huMoments])
+
 ```
 ## Creating test and training sets
 Data is divided between two sets, where training set contains 80% of all data and test set only 20%. Images are randomly shuffled.