Update file README.md

README.md

@@ -34,7 +34,7 @@ The project is organized into the following key components:
 - Other parameters include test set size and the number of recommendations to consider.
 
 2. ***constants.py***
-    - This code defines a Constant class that stores paths to datasets and column names for content and evidence data. Paths to content, evidence, and evaluation directories are defined based on the data directory path. File names and column names for article and rating data are specified, along with the rating scale.
+- This code defines a Constant class that stores paths to datasets and column names for content and evidence data. Paths to content, evidence, and evaluation directories are defined based on the data directory path. File names and column names for article and rating data are specified, along with the rating scale.
 
 ### Data Loaders
 3. ***loaders.py***
@@ -54,7 +54,7 @@ The project is organized into the following key components:
     
 - The `get_top n` function takes a list of predictions and returns the top recommendations for each user.
 
-- Recommendation algorithms are defined as classes inheriting from Surprise's `AlgoBase` class, each implementing an =`estimate`  method to predict user ratings for items.
+- Recommendation algorithms are defined as classes inheriting from Surprise's `AlgoBase` class, each implementing an `estimate`  method to predict user ratings for items.
 
 ### Analytics and Evaluation 
 5. ***analytics_ui.ipynb***
@@ -110,15 +110,7 @@ The project is organized into the following key components:
 
 We begin by loading a dataset using the `load_ratings` function, which converts the data into a format suitable for the `Surprise library`. This formatted data, referred to as `surprise_data`, is then used to create a training set (`trainset`) for our collaborative filtering models. The training set contains user-item ratings that will be used to train our recommendation algorithms.
 =======
-Le **small dataset** de données contient les données traditionnelles de **MovieLens** et est celui que nous devons utiliser lors de l'**évaluation** des modèles et de la construction de notre **système de recommandation**.
 
-### Test
-
-Le **test dataset** est une version encore plus petite **(6 utilisateurs et 10 articles)** et contient l'exemple utilisé dans les diapositives de la conférence. Nous utilisons ce dataset afin de **mieux comprendre** le fonctionnement des algorithmes lors du développement de nos modèles.
-
-### Tiny
-
-Le **tiny dataset** est une version plus petite du small dataset et est utilisé pour **déboguer** notre code. Cela est utile lorsque certains modèles prennent beaucoup de temps à s'exécuter et que nous voulons **accélérer** le temps de calcul.
 >>>>>>> Evaluator
 >>>>>>> Adrien
 
@@ -202,19 +194,17 @@ Le Jupyter Notebook peut être lancé indépendemment de tout autre code.
 
 
 ## Contact
-For any questions, suggestions, or collaboration requests regarding this project, feel free to contact us. For in-depth discussions about our research and methodology, you can reach out to:
 
-- Audrey Ghilain à audrey.ghilain@student.uclouvain.be
-- Nathanaël Kindidi à nathanael.kindidi@student.uclouvain.be
-- Charles Addae à charles.addae@student.uclouvain.be
-- Adrien Payen à adrien.payen@student.uclouvain.be 
+For questions or collaboration requests, please contact:
 
-## Remerciements
-We would like to express our gratitude to several parties who played a crucial role in the realization of this project:
+- Audrey Ghilain: audrey.ghilain@student.uclouvain.be
+- Nathanaël Kindidi: nathanael.kindidi@student.uclouvain.be
+- Charles Addae: charles.addae@student.uclouvain.be
+- Adrien Payen: adrien.payen@student.uclouvain.be
 
-- **Professor Vande Kerckhove Corentin**: For his teaching on recommender systems. His support and guidance were essential for adapting and improving our codes in our project.
+## Acknowledgements
 
-- **Else...**
+We thank Professor Vande Kerckhove Corentin for his guidance on recommender systems, which was crucial in adapting and improving our project.
 
 
 ## Authors 
@@ -226,6 +216,10 @@ We would like to express our gratitude to several parties who played a crucial r
 
 ## References
 
+
+Surprise Documentation
+Pandas Documentation
+Numpy Documentation
  **User_based**
 
 <<<<<<< HEAD
@@ -267,3 +261,4 @@ We would like to express our gratitude to several parties who played a crucial r
     - https://surprise.readthedocs.io/en/stable/ 
     
 >>>>>>> Evaluator
+