why should I go for logistic regression?
If you want to know why your advisor advised you with that advise, you should ask your advisor why he advised you thus, as Yuval points out.

If you want to know what makes logistical regression a good method, it is because it takes as input a data set such as the one you have and it outputs a probability (distribution), which is precisely the input-output format you were looking forit; it is simple, widely available, easy to implement, and in practice, it turns out that they do a markedly good job at predicting real-world data. It is also the last step before curve-fitting turns into AI, because neural networks are really just logistic regression on steroids.

There are other methods because world of data analysis is big. You are looking for a classification model. For example, you can use a Bayesian learning algorithm, such as Naive Bayes or Pearl's algorithm. Weka is a freely available software toolkit with 100 (+/- 50) different classification algorithms. It is quite user-friendly. If your data set is large and complex enough, you can consider letting loose a true AI, such as a neural network, or a Deep Learning machine. All these things are freely available.