Update README.md

Author: LorenzoF6

README.md

@@ -1,32 +1,16 @@
 <h1 align="center">Partial Least Squares Algorithm Implementation</h1>
 
-
 ## Authors
 * **FERRARI Lorenzo**, 1053161, postgraduate in Computer Engineering at University of Bergamo.
 * **LEONI Lorenzo**, 1053379, postgraduate in Computer Engineering at University of Bergamo.
 
 ## Descrption
 Implementation of the PLS algorithm through a MATLAB class which allows:
-* to estimate the classification model using the NIPALS algorithm;
+* to estimate a classification model using the NIPALS algorithm;
 * to validate and cross-validate it by providing some performance metrics;
 * to predict new instances starting from the trained model;
-* to calculate the best reduction order;
+* to compute the best reduction order;
 * to perform a comparison with the PCA technique.
 
-link[Scripts\Data_analysis.mlx] contains an application example of the PLS.m class
-
-## Goals
-- [x] ...
-- [x] ...
-
-## Documentation
-The documentation is aviable at this link []
-
-## Overview
-    .
-    ├── Scripts                 # Algorithm implementation in Matlab
-    ├── Data                    # Dataset used
-    ├── Docs                    # Documentation
-    ├── Images                  # Images 
-    └── README.md
-
+[Data_analysis.mlx](Scripts/Data_analysis.mlx) contains an example of how [PLS.m](Scripts/PLS.m) can be used to classify 
+steel plates faults by using this [dataset](https://www.kaggle.com/datasets/uciml/faulty-steel-plates) available on Kaggle.