First of all - I'm new to all of this.

I'm trying to create a model to predict the release of ios 12 based on previous years. I've got an excel that has a format like this: ios version | release name | date

Each version consists of about 5 betas + general release. I've set up the import like this: File import

Secondly I've set up flow like this: Flow diagram

First step selects all the rows that have [date] param - those go to either kNN or Linear Regression, everything else (1 row with ios 12 release date) goes to prediction and then to a table.

Cool, now depending on the modeling I get different results (as expected). It's either 2015-09-17 (kNN) or 2018-09-10 (LR). I've used the Test & Score, which gives me:

Test & Score results

If I'm reading this properly - Linear Regression is more accurate (R2 column), meaning that iOS 12 will release on 10th of Sept 2018 (DUH! not 2015).

But if I look at release dates up to now, this might be wrong, because no other release happened so soon:

iOS release dates

So I also did an exercise where I've adjusted the dates, so that they are all in 2018 (so that the year is less important, and I'd care more about relation within each year, given the features) - here are predicted results:


and Test & Score for the offsets: enter image description here

Now... I would welcome all comments. Am I using the wrong modeling? Something other than kNN & LR? Should I be using offsets? Am I using the tool completely incorrectly?

I'd really like some comments still and just so you know, I got the prediction right. I assumed that since it's either 10th of 16th and 16th is Sunday, I said it would be either 10th or 17th:

iOS 12 release date

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