Great video. However, I think that the precision/recall explanation at the end should be the other way around.

Thank you for yet another awesome video! I like how you simplify data science concepts.

Great video

Love it!

amazing

how should we treat the test dataset? Does that also have to be randomly sampled? How would you present recall and precision? at the user level or daily observation level? Thanks

Another masterpiece by you 🙏

Precision: Of the users who churned, how many did the model predict would churn Precision: छोड़ कर गए लोगों में से कितनों के बारे में model ने बताया था कि ये छोड़ के जा सकते हैं | Recall: Of the users the model said would churn, how many users actually churned? Recall: Model ने जिनको बताया था कि ये जा सकते हैं , उनमे से कितने वाकई में छोड़ कर चले गए |

Thank you, Could u please help me how can make a mathematical equation for churn model?

Can you provide examples of practices in tools related to churn, for example in Orange Data Maining? I'm still learning to churn, thank you

awesome

Start porting your videos to LBRY's Odysee as well. You can set that to automatically upload it there whenever it uploads to KZfaq and can be activated when you create your Odysee account. You deserve way more exposure, maybe that can help a little since there is far less competition there.

Awesome video! The step at 9:05 went a bit too fast in my opinion. Could you show us, perhaps with made up data, how you verify it using SQL? What goes into the process of defining a "Correct" and an "incorrect"?

There are two points that you mentioned in the model training slides 1. Create snapshots randomly for active users at a given time 2. Only train on data up to 3 months in the past Does that mean that when I want to create a random snapshot, let's say of 18th Mar 2021, I only include customers who have purchased at least once in the last 3 months as anyone whose last purchase was 4 months prior to 18th Mar is already a churned and hence inactive violating the first point. Also of those users who are considered active, can we look back at data prior to 3 months or not. I am guessing not because that would violate rule 2 but then you are using a feature #work orders in last 6 months. So I am a little confused here.

I think you got the precision and recall swapped, precision = Tp/ tp + fp , Recall = Tp/ Tp + Fn , The false negatives + true positives in recall stands for the users who actually churned and the true positives + false positives in precisions are the predictions by the model!

Great video. Love from pakistan

Want to ask about perhpas too bias features. Isn't something like "days from last orders" or "total order count" would be too bias to predict churn. I mean it's common sense that if the customer hasn't ordered for a while or in a long time ordered once or twice, he is SUPER likely to churn when the churn period arrives. Wouldn't such features somehow hide or overshadow other features which perhaps could indicate us which features make negative churn predicitons ?

Great video! I don’t think you could use regression though because of right censoring. You don’t know if some of those customers will churn and when. Survival analysis models deal with this kind of censoring.

Wait why can we only train on data up to 3 months in the past? If you have the same features for customers from last year and also see their usage history last year, couldn't you use that historical data to predict churn as well?

what is work order ???

Is this a Hancock 'good job'??

Precision recall definition seems to be goofed up.

I don't agree with the 2nd feature. still great vdo 👍