Machine learning and time-based data I want to predict conversion rates for an eCommerce store. I have data from Google Analytics with features like averageSessionDuration, bounceRate, numberOfVisitorsBySource etc. and the corresponding conversion rate.
That's the specific background for a generic question: I am not sure how I should select the time units for my data. Should I export the data by day, by week, by month or by year?

When I have one row per day, I have a lot of data (a lot of rows),
but also a lot of noise, because the conversion data for one day is
sparse.
When I have one row per year, I have less noise, but also less data
(less rows).

In general, what's the right approach to select the right time frame per row in such a case?
I think you should first export it with a timestamp and then add a day, month ... other features to the data. This might help in better accuracy.
For more details, you can refer: This article