Which algorithm should I use for this use cases?

Question

I got data like:

1. Student age
2. Student study subject
3. Student gender
4. Student academic result
5. Student joined actives

and I got the student graduation income.

So, I would like to train a model, so that I can guest a student with some background and find back his / her projected income. If I would like to use machine learning to deal with this case, any ideas on how to implement? Please advise.

Answer 1

Features: age, gender and academic result: take as numeric values

study subject : encode the subjects like 0 for maths, 1 for physics etc..

for joined actives encode and use features like number of activities plus other columns as highly important activities having values encoded as 0 and 1.

Example: age, gender, academic result, study subject, total_actives, debate, music`` 24, 0, , 90, , 3 , 4 , 1, 0

Train your XGBoost regressor using the above features and voila!