diff --git a/final-evaluation.md b/final-evaluation.md
index db7d2b1..6e451bf 100644
--- a/final-evaluation.md
+++ b/final-evaluation.md
@@ -48,13 +48,13 @@ python Main.py
 Since there are three people in our group we have created and combined three subprojects with our main program. Two use **Neural Networks** and one uses **Random Forest Classifier**.
 
 ### First subproject 
-The first subprojects was created by Patryk Krawiec (the raport can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Patryk_Krawiec.md)).
+The first subproject was created by Patryk Krawiec (the raport can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Patryk_Krawiec.md)).
 
 ### Second subproject 
-The first subprojects was created by Kamila Bobkowska (the raport can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Kamila_Bobkowska.md)). This project used **Convolutional Neural Networks** to detect what kind of trash is in the dumpster and sorts it accordingly. It was mainly implemented to to the class `Garbagetruck.py` and `dumpster.py` . It used photos of garbage to determine whether it was sorted correctly. It did so by using CNNs - extracting the most important features from the photos with convolution layers, then using pooling layers and flattening. Along with implementing CNNs the person created a function that assigns trash to dumspters (between 1 to 3 trash correctly sorted and 0 to 2 incorrectly sorted trash). The data set used was from [kaggle](https://www.kaggle.com/asdasdasasdas/garbage-classification).
+The second subproject was created by Kamila Bobkowska (the raport can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Kamila_Bobkowska.md)). This project used **Convolutional Neural Networks** to detect what kind of trash is in the dumpster and sorts it accordingly. It was mainly implemented to to the class `Garbagetruck.py` and `dumpster.py` . It used photos of garbage to determine whether it was sorted correctly. It did so by using CNNs - extracting the most important features from the photos with convolution layers, then using pooling layers and flattening. Along with implementing CNNs the person created a function that assigns trash to dumspters (between 1 to 3 trash correctly sorted and 0 to 2 incorrectly sorted trash). The data set used was from [kaggle](https://www.kaggle.com/asdasdasasdas/garbage-classification).
 
 ### Thrid subproject 
-The first subprojects was created by Klaudia Przybylska (the raport can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Klaudia_Przybylska.md)). This subproject used **Random Forest Classifier** to make sure that when the garbage truck reaches the garbage dump, all the thrash inside is correctly classified before taking it out of the truck and sorting it accordingly. From the pictures of different kinds of waste, it extracts  three  features: Hu moments (shapes), Color Histogram (colors) and Haralick Texture (textures), that are given to the classifier. It is mainly implemented in `garbageDumpSorting.py` class. The data set is the same as in the second project - [kaggle](https://www.kaggle.com/asdasdasasdas/garbage-classification).
+The third subproject was created by Klaudia Przybylska (the raport can be found [here](https://git.wmi.amu.edu.pl/s444517/AI2020_Project/src/master/Report_Klaudia_Przybylska.md)). This subproject used **Random Forest Classifier** to make sure that when the garbage truck reaches the garbage dump, all the thrash inside is correctly classified before taking it out of the truck and sorting it accordingly. From the pictures of different kinds of waste, it extracts  three  features: Hu moments (shapes), Color Histogram (colors) and Haralick Texture (textures), that are given to the classifier. It is mainly implemented in `garbageDumpSorting.py` class. The data set is the same as in the second project - [kaggle](https://www.kaggle.com/asdasdasasdas/garbage-classification).
 
 ## Examples
 How the UI looks like: