XML, HTML and less formal languages all respond quite nicely to being transformed or interrogated within a graph framework. XML and HTML are particularly useful in that they conform strictly to a tree-structure. That means that any good data components can be measured in terms of tree-distance to any other "good" data components.
If you extract your regex-...
I suggest you should use AI Regression Model for future predictions for an attendance of students. Because of this technique or model design for future predictions.
Follow this to get more information about regression type and methodology
I can't speak to wit.ai specifically, but I can tell you a little bit about how similar applications work. Specifically, I can talk a bit about Apache Stanbol which also converts free text into structured data. That said, I should prefix this by saying there isn't just one way to "get there from here." Many techniques could be part of a stack for ...
Assuming your three columns data (data label included) is stored in array "a" (numpy).
1) extract data values only, removing the data label, into new array b:
2) classify "b" using KMeans/kmeans.predict/... . Result is an array of class labels, it keeps original order of samples.
3) rejoin original data and ...
scikit-learn has a small data sets API http://scikit-learn.org/stable/datasets/index.html
I imagine one can add more data sets locally.
Some data sets are for classification, other for regressions.
This is the only one I know about.
Is your question about storing, writing, or reading/processing huge data? I'm not an expert in this topic, but I know a couple of possible ways to handle huge datasets:
If the data is too big to be fully uploaded to RAM, you can iterate over it in Pandas. You can find a brief explanation in the article Why and How to Use Pandas with Large Data, section 1. ...
@SmallChess's answer is a good start, but there are some additional parts to the question.
binary variables or binary data consist of data with the values 0 or 1, and no other values. We usually don't talk about "binary distributions", because it's only data, variables, or outcomes that can be binary. A distribution might produce binary data, but is not ...
You would need to define 'frames', 'templates', or sets of data belonging together to form an address or other kind of data, with typical labels. So phone or tel etc would indicate a phone number, provided that their content also looks like a phone number. That's how you as a human recognise it. So you encode your domain knowledge as entities with possible ...
Because you have a small number of students (30), and a short time (one week), the number of absences is likely to be best modelled as a Poisson distribution.
The average number of absences within a given time period is μ (use your data to estimate this).
Then, the Poisson probability of x absences is:
P(x; μ) = (e-μ) (μx) / x!
where e ...