Update README.md

README.md

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 ## Main objective
 
+Provide a novel post-hoc ,model-specific, local  XAI solution to enhance the model interpretability of Black-Box models focus on  
+Random Forest, Support Vector Machine, K Nearest Neighbor and Logistic Regression by developing a novel counterfactual rule generation 
+mechanism related to the text classification domain.
+
+
 ## Main Research questions
 
+
+
 ## 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 Logistic Regression 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?
+
+
 ## Individual Objectives
 
-## Other necessary information
\ No newline at end of file
+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.
+
+Providing model specific ,local ,post-hoc explanations using counterfactual mechanisms to improve the interpretability of the system.
+
+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.
+
+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
+
+Frontend:
+- ReactJS 
+- Flask 
+- Boostrap
+
+Backend:
+- Python
+
+Version Control:
+- GitHub
+
+Tools:
+- VS Code
+- Google Colab
+
+Data set
+- IMDB Dataset of 50K Movie Reviews
+