In summary, linear regression seeks the correlation between two numerical variables and logistic regression can have numerical or categorical variables. Logistic regression is used when the answer to your problem can be categorized. Ex: which employees are more likely to leave the company? In this case this question is a binary answer...classifying, either the employee leaves or stays and the model will separate these two blocks and you can evaluate through the confusion matrix. Hope this helps. Helberth  

Hi Kranthi,

I believe that you are just finding information about IRIS because it is something that is "in fashion". Part of Summit was about this, if you look at the developer community, basically just talk about it. In a way, we are almost unconsciously induced to migrate to IRIS. It is a fact that they have differences as friends have already commented, however, in general, it is more of the same.