Neural networks and machine learning for data science in business [D]

Submitted by lordgriefter t3_zmpzz2 in MachineLearning

I am currently learning tools for data science, in particular in a business analysis setting for pricing strategy, demand forecast, etc. I am currently reading Géron's Hands on ML and I am fascinated by some of the ML concepts, such as regressions and random forests, and see the potentials of these tools for business data science.

But now when I am reading the neural network part of the book with Keras and TensorFlow, I slowly realised that these tools are used for a really big datasets and features for tasks such as computer vision, voice recognition, etc and not for business analysis applications. Am I right in this feeling that the basic ML tools are enough, or is there real application for neural networks and advanced ML tools for business data analysis?

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fin_quant t1_j0cf0wo wrote

I have seen neural networks being used for agency MBS prepayment models.

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PassionatePossum t1_j0ch2oy wrote

It is not true that neural networks are only suitable for big datasets. It is just that modern deep architectures that are trained from scratch require relatively large datasets.

But there are plenty of alternatives if you want to do ML with small datasets. Use pre-trained networks if possible. Use smaller network architectures. Or don’t use neural networks at all. There are lots of approaches from the era before deep neural networks (e.g. support vector machines, decision trees, probabilistic models, like Bayesian networks) and they are not obsolete.

Choose the right approach for the problem and don’t think that because deep neural networks are the new hotness, that you need a neural network.

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anymorenevermore t1_j0cyo5l wrote

Classic Machine learning tools (let's say what you can get using SciKit learning) will help you with +90% common business analytics problems.

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PredictorX1 t1_j0cz1pr wrote

The term "neural networks" covers a rather wide collection of techniques. While deep learning models consume astronomical amounts of data, older "shallow" neural networks (single hidden layer MLP, for instance) are often used with observations counts in the thousands.

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chief167 t1_j0funra wrote

xgboost or lightgbm are the goto for classic tabular business data

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SnooDogs3089 t1_j0g3jim wrote

As always it depends. They are all tools based on some stats theory that in a spectrum from "perfect case scenario" to "absolutely not feasible" give answers to questions. The choice to use something instead of something else is in the hands of the expert given a great bunch of consideration and educated guesses about the outcome ot the proj. Tools are tools. A craftsman knows when is appropriate an hammer and when is appropriate a precision driller. Same for us.

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Worth-Advance-1232 t1_j0gct0d wrote

Usually it is indeed the case that especially deep learning requires a lot of data. However also with little data neural networks might generate good results using the implementations provided by e. g. scikit learn. Also you might want to look into Boosting Algorithms (specifically XGBoost), as they are usually powerful on tabular data which is a common type of data to have in businesses.

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