Which ML algo can be used in Loan prediction system other than these 3 (
Logistic Regression, Decision Tree , Random Forest). I am in search of a thesis topic for MS there are many research papers that are published using these 3 techniques. I want to apply someother algio
Hello Hamza,
You can use SVM ( Suport Vector Machines), K-NN ( k-nearest neighbors) or NN (Neural Networks) if you want to use ML algorithms, but I think that in most cases you will have similar results to when you use RF (Random Forest) but with higher execution times, that is the reason why RF is often used, and because is more simple use RF than the others ML algorithms.
If you want to change, maybe with DL algorithms (Deep Learning) you can have better results, for exampo using LSTM algorithms (Long Short-Term Memory) but this type of algorithms is more complicated to program and the execution time are bigger than RF
I hope I have been useful, good luck!
Hamza Mehmood, it depends on your interest, you can go for deep learning-based algorithms and will be more suitable for your prediction system than traditional ML algorithms.
Have a look at these : https://github.com/Architectshwet/Loan-prediction-using-Machine-Learning-and-Python
https://copycoding.com/d/machine-learning-project-in-python-to-predict-loan-approval-prediction-part-6-of-6-
https://www.kaggle.com/jlrsource/predicting-loan-status-with-python
Dear Hamza Mehmood,
You can choose either SVM, K-NN, or LSTM for this. Because these are the three popular leading ML models used in various applications nowadays.
If you required more accuracy then use the hybridization of these models.
Thanks
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