Update README.md

README.md

@@ -15,39 +15,31 @@ mechanism related to the text classification domain.
 ## Individual research question
 
 - #### IT20097660 | Sashini Devindi
-How to get a counterfactual rule generation-based explanation for the Support Vector Machine classifier, when it handle  
-non-linear separable data in text classification?
+How to get a counterfactual rule generation-based explanation for the Support Vector Machine classifier, when it handle non-linear separable data in text classification?
 
 - #### IT18161298 | Srinidee
-How to get a counterfactual rule generation-based explanation for the Logistic Regression classifier when it becomes black box 
-in text classification?
+How to get a counterfactual rule generation-based explanation for the Logistic Regression classifier when it becomes black box in text classification?
 
 - #### IT20100698 | Thilini Anjalika
-How to get a counterfactual rule generation-based explanation for the Random Forest classifier, when it becomes black box in 
-text classification?
+How to get a counterfactual rule generation-based explanation for the Random Forest classifier, when it becomes black box in text classification?
 
 - #### IT20013950 | Lakshani N.V.M.
-How to get a counterfactual rule generation-based explanation for the k-NN classifier, when it handle  Curse of Dimensionality 
-problem in text classification?
+How to get a counterfactual rule generation-based explanation for the k-NN classifier, when it handle  Curse of Dimensionality problem in text classification?
 
 
 ## Individual Objectives
 
-#### IT20097660 | Sashini Devindi
-Provide a novel post-hoc ,model-specific, local  XAI solution to enhance the model interpretability of function-based 
-classification models focus on  SVM by developing a novel counterfactual rule generation mechanism related to the text 
-classification domain.
+- #### IT20097660 | Sashini Devindi
+Provide a novel post-hoc ,model-specific, local  XAI solution to enhance the model interpretability of function-based classification models focus on  SVM by developing a novel counterfactual rule generation mechanism related to the text classification domain.
 
-#### IT18161298 | Srinidee
+- #### IT18161298 | Srinidee
 Providing model specific ,local ,post-hoc explanations using counterfactual mechanisms to improve the interpretability of the system.
 
-#### IT20100698 | Thilini Anjalika
-Provide a novel post-hoc ,model-specific, local  XAI solution to enhance the model interpretability of ensemble models focus 
-on  Random forest by developing a novel counterfactual rule generation mechanism related to the text classification domain.
+- #### IT20100698 | Thilini Anjalika
+Provide a novel post-hoc ,model-specific, local  XAI solution to enhance the model interpretability of ensemble models focus on Random forest by developing a novel counterfactual rule generation mechanism related to the text classification domain.
 
-#### IT20013950 | Lakshani N.V.M.
-Provide a novel post-hoc ,model-specific, local  XAI solution to enhance the model interpretability of distance-based classification 
-models focus on  k-NN by developing a novel counterfactual rule generation mechanism related to the text classification domain.
+- #### IT20013950 | Lakshani N.V.M.
+Provide a novel post-hoc ,model-specific, local  XAI solution to enhance the model interpretability of distance-based classification models focus on  k-NN by developing a novel counterfactual rule generation mechanism related to the text classification domain.
 
 
 ## Other necessary information