Hi Ben,

I have high expectation on your book and am very excited to learn. However, the codes published on Github is incomplete. For example, in chapter 2, where are the codes from page 52 to 53 especially page 53? I am very interested to know how you did the code in page 53. Also some codes in jupyter notebook are not explained either in notebook and book. I am kind of disappointed. Could you please republish your complete jupyter notebook? Thanks

Hello, In the Chapter 3 Preprocessing notebook there seem to be several errors in the code.

When installing 'featuretools[tsfresh]' in colab the following error occurs: WARNING: featuretools 0.27.1 does not provide the extra 'tsfresh'

When running the first code bracket using feature tools in colab the following error occurs: TypeError: import_optional_dependency() got an unexpected keyword argument 'errors'

It appears colab installs featuretools version 0.27.1 If I install the most recent version of featuretoolls (i.e., 1.3.0) and run the code bracket below Automated Feature Extraction I get the following error: AttributeError: 'EntitySet' object has no attribute 'entity_from_dataframe'

If it is versioning that is an issue, please let me know what version of featuretools the code in the book was based on. If it is not versioning, would you be able to advise what the issue is?

Let me know if you have any questions. Thanks!

Best, Nils

Hi Ben!

Not a book issue, but one with the readme associated with this repo. The Key Features section starts fine but in the middle it accidentally switches into a description of another Pack book on Power BI. Looking forward to reading your book!

Alex

1. Change the order for chapter-6 as per the book.
2. issue in the notebook for line intrusion_outliers = od.predict(intrusions["data"][:,0])
3. Few minor modifications for the chapter 5 notebook.

An integer is being used instead of the yearly_periods parameter in the get_season() function. The function works as is, but is not generalizable to seasonality of other yearly_periods values.

This is from the documentation of ARCH package "The null hypothesis of the Augmented Dickey-Fuller is that there is a unit root, with the alternative that there is no unit root. If the p-value is above a critical size, then the null cannot be rejected that there and the series appears to be a unit root."

on p. 152 a p-value of 0.997 is reported (in a figure which references KPSS test while the table shown clearly says that it is an ADF test) and the following conclusion is made "Given the p-value of 0.997, we can reject our null hypothesis of the unit root, and we conclude that our process is weakly stationary."

In a couple of pages "Please note that we need to set trend="t" here so that the model includes a constant. If not, we would get a spurious regression." In the statsmodels' ARIMA class documentation, it is stated clearly that trend is "Parameter controlling the deterministic trend. Can be specified as a string where ‘c’ indicates a constant term, ‘t’ indicates a linear trend in time, and ‘ct’ includes both."

It's quite doubtful that the author of this book understands what he is writing about. But at least he could have checked the documentation of the packages used in it.

The coverage of time series clustering is poor. Actually does not exist beyond referencing a few libraries which people can find themselves. Any follow up with examples here would help I think.