Is a neural network suitable for this application Apologies if this question is not a suitable format. I am a novice in data science.

I have a database of species observation data consisting of ~16 million records.
Each record consists of:


latitude
longitude
date
time
species observed (that's species singular, not plural)


This data has been manually vetted by experts, so there is an additional field for each species in a record that classifies the observation as either valid or invalid (or more accurately speaking, likely correct/likely incorrect)

I am exploring the idea of training a neural network on this data to automatically classify new records as being valid or invalid ("invalid" data will be flagged for manual expert review.)

The vast majority of records are classified as 'valid', so my worry is that there isn't much information to train the model on what constitutes 'invalid'. 

However, a good predictor of whether a record is valid is, informally speaking, "are there other records of this species close by (spatially and/or temporally)"

I'm not sure where to start with formulating a neural network for this problem. E.g.

Inputs: latitude, longitude, date, time, species

Output: validity

OR

Inputs: latitude, longitude, date, time

Outputs: one output for each known species indicating validity

I like the idea of this second model as I can input a time and location and get out a list of likely species.



So my concrete questions are:


Does this sounds like an application suitable for a neural network?
If so, where might I start with formulating a model for my problem? Or can someone point me in a good direction to learn more about this topic.
You may or may not apply neural network to your problem. Neural network will give predictions for sure but it can't be said how much efficient will it be for this problem. You have to code and check it for yourself. Also, if you have all the above data in tabular form and you know the labels(which I guess is valid/inavlid), I will say try it with xgboost. Neural networks are exceptional for unsupervised learning but in the case of supervised learning, there may be a model which may outperform a neural network.