Though some of these are actually optimization\u2026<\/p>\n
This came out of at Fast.ai<\/a> ppt that can be found here<\/a>. Granted they only provided the list. You will notice an ethics deck they have uploaded as well, I encourage you to review at it. I have a few ethics slides in my data science talk, but the fast.ai gang hit way harder than I typically do. I admit, shock is a good way to wake people into thinking about what they are doing. <\/p>\n In their ML Applications deck they have a list of applications by industry, below I have them listed out and what I hope is to present either an elevator pitch of what each one is, or and executive overview of each and links to more info. This post will be made of a lot of quotes and references, that is kind of the point, no original content will come from me as I am not the creator of much data science, just a user of it, though i am sure i will add commentary. This will be a series of blogs posts, and clearly each post has the potential for being very long even with just a brief summary and a few links. <\/p>\n The funny thing about solving a data science problem is that their are many ways to solve it, so i don’t expect this to be 100% comprehensive, i try to find what appears to be a canonical solution, though that does not mean you cannot stuff everything into a neural net and close your eyes, which is what everyone appears to be doing these days… <\/p>\n Enjoy <\/p>\n <\/p>\n Horizontal Applications<\/a> \u201cA horizontal application is any software application that targets a large number of users with different knowledge and skill sets. Because these types of applications can extend across markets and be used in a range of industries, they typically do not offer market-specific features.\u201c[1a]<\/a> <\/p>\n \u201cThe goal of predictive CLV is to model the purchasing behavior of customers in order to infer what their future actions will be.\u201d [1]<\/a><\/p>\n \u201cHistorical CLV has several drawbacks, the most important of which being that, since it is the sum of past revenue or profit for a particular customer or group, it only provides insight into what has already occurred, and, thus, sheds little insight into the value of new subscribers. Predictive CLV, however, with its ability to incorporate expected retention, allows marketers to obtain several key insights, including what types of subscribers will be the most profitable over a specific time period\u201d [2]<\/a><\/p>\n Potential Models: More Learning: Implementing and Training Predictive Customer Lifetime Value Models in Python
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\nHorizontal Applications<\/h1>\n<\/div>\n
Marketing Applications<\/h1>\n<\/div>\n
Predictive Lifetime Value LTV\/CLV<\/h2>\n<\/div>\n
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\nhttps:\/\/github.com\/datascienceinc\/pydata-seattle-2017\/tree\/master\/lifetime-value<\/p>\n
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