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:
- 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
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.