Python [Python] finding the correct data mining approach

AI Thread Summary
The discussion focuses on predicting future log-in times for a website based on historical data. The user has cleaned their data to include Date_Time entries for log-ins and seeks to forecast log-ins for the next two weeks, considering daily and hourly trends. They propose clustering the data by day to account for variations between weekdays and using regression analysis for predictions. Additionally, clustering by hour is suggested to analyze hourly trends. The user is looking for resources or guidance on implementing this in Python while aiming for a straightforward approach. A recommendation is made to explore the MLpy library and lowess for potential modeling techniques.
eherrtelle59
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I'm having trouble finding the correct approach to my (fairly simple) example.

Let's say I have months of data for log-in times of a certain website. The data has been selected and cleaned such that I have a list of Date_Time for each log-in.

Now, suppose I wanted to predict the log-ins for the next two weeks by day and hour, based on these past trends.

I imagine I would cluster the data by day (assuming beforehand that there will be different trends with respect to Monday vs. Friday) and make some regression analysis to predict the next two (say) Mondays.

Similarly, I could cluster by the hour and do a regression analysis to extrapolate the trend of log-ins.

Anyone know of a resource which tells you how to do this in Python? I want to keep this example fairly straightforward, but I'm open to any more ideas on how to model this behavior more efficiently.
 
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