CODEWITHSUNDEEP
pythonscikit-learn
Dillion Ecmark
Dillion Ecmark

Reputation: 724

Machine learning classification dataset setup

I am very sorry if this question violates SO's question guidelines but I am stuck and I cannot find anywhere else to ask this type of questions. Suppose I have a dataset containing three experimental data that were obtained in three different conditions (hot, cold, comfortable). The data is arranged in three columns in a pandas dataframe consisting of 4 columns (time, cold, comfortable and hot). When I plot the data, I can visually see the separation of the three experiments, but I would like to do it automatically with machine learning. The x-axis represents the time and the y-axis represents the magnitude of the data. I have read about different machine learning classification techniquesbut I do not understand how to set up my data so that I can 'feed' it into the classification algorithm. Namely, my questions are:

  1. Is this programmatically feasible?
  2. How can I set up (arrange my data) so that it can be easily fed into the classification algorithm? From what I read so far, it seems, for the algorithm to work, the data has to be in a certain order (see for example the iris dataset where the data is nicely labeled. How can I customize the algorithms to fit my needs? NOTE: Ideally, I would like the program that, given a magnitude value, it would classify the value as hot, comfortable or cold. The time series is not much of relevance in my case

Upvotes: 1

Views: 452

Answers (1)

boot-scootin
boot-scootin

Reputation: 12515

Of course this is feasible.

It's not entirely clear from the original post exactly what variables/features you have available for your model, but here is a bit of general guidance. All of these machine learning problems, from classification to regression, rely on the same core assumption that you are trying to predict some outcome based on a bunch of inputs. Usually this relationship is modeled like this: y ~ X1 + X2 + X3 ..., where y is your outcome ("dependent") variable, and X1, X2, etc. are features ("explanatory" variables). More simply, we can say that using our entire feature-set matrix X (i.e. the matrix containing all of our x-variables), we can predict some outcome variable y using a variety of ML techniques.

So in your case, you'd try to predict whether it's Cold, Comfortable, or Hot based on time. This is really more of a forecasting problem than it is a ML problem, since you have a time component that looks to be one of the most important (if not the only) features in your dataset. You may want to look at some simpler time-series forecasting methods (e.g. ARIMA) instead of ML algorithms, as some of the time-series ML approaches may not be well-suited for a beginner.

In any case, this should get you started, I think.

Upvotes: 1

Related Questions