Do you need a neural network for calculation or not?

Question

Python

As far as I understood from various lectures, a neural network is used to recognize belonging, we enter characteristics and it gives out a boy or a girl (conditionally), but my task is a little different.
Conventionally, I have a table of statistics for weight, gender, age now, etc. with the results, how long a person lived and based on these statistics, I need to calculate / predict the result (how long he lived) do I need a neural network for this at all? Can you suggest how to solve this kind of problem?

Answer 1

You cannot predict in any way until you collect all the factors of death. And sometimes doctors can not always provide them.

Answer 2

No.

Answer 3

Alternatively, you can try to build a model on the NS.
For example, one of the first training tasks on Kaggle is to predict whether the passenger survived or not based on data from the passengers of the Titanic .
It differs from your task only in the final layer, where with the Titanic they get a binary answer yes / no, and you need a certain coefficient indicating age.
For a more or less reliable model, a lot of data will be needed, and there should really be a meaningful dependence in them.

Answer 4

For this task, a neural network is not required because, in principle, you have no indicators responsible for the lifespan. the probability of error will be incredibly high. Even with statistical analysis, you are almost 100% wrong with the result.
For understanding - ML and neural networks are designed to predict the result based on relevant validated data that has been verified and prepared in a special way to create an appropriate model. If we now take the data you indicated, we find out that people in the average weight of the same age of average height die in the range of 1 to 120 years. The dispersion will be huge, the neural network will not be able to build an adequate model.
PS. The numbers are exaggerated just to show meaninglessness

Answer 5

Yes, no, maybe. (C)
A good selection turned out, just like the slogan from one movie