I want to build a predictive model to predict the dropout rate of students based on their age, gender, and family income. I am a beginner with machine learning and want help. Please let me know how to proceed with this?
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If you have previous dataset that comprises of age, gender, family income and dropout, you can do this using sklearn.. There are lot of tutorials available in internet.. for snippets you can use https://github.com/gklc811/Python3.6/tree/master/ML – Gokul C Feb 09 '19 at 15:19
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What you are describing is essentially Churnn prediction.
Before you even begin thinking of building a predictive model you need to make sure you have a lot of labeled data. In your case you have to have many records with students labeled with Y/N (0/1) whether they have dropped out and not.
Student ID, Age, Gender, Family Income, Dropped Out
1, 15, M, 10000, Y
2, 16, F, 20000, N
3, 14, M, 13000, Y
..
10000, 15, M, 30000, N
If you do have such data, you can go ahead and start building your model.
Here is a blog post explaining the general process of building your model with Scikit-Learn, and here is another blog post for more details on Churnn prediction with some python code.
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Hi Konstantinos, I have labeled data with dropped out results as True and False. Can we connect over email ? – Ashish Krishan Feb 11 '19 at 04:53
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I highly prefer we continue on the site. You can read the material that I have sent you and if you have more question go ahead and ask me here or create another question. – Konstantinos Charalampous Feb 12 '19 at 07:55