Great video. However, I think that the precision/recall explanation at the end should be the other way around.
@CodeEmporium
3 жыл бұрын
Good eye. I'll clear this up in a comment. Thanks for catching this
@Akshaylive
3 жыл бұрын
Came here to say this!
@falak88
3 жыл бұрын
Aye
@bahmanaboulhasanzadeh8512
2 жыл бұрын
It would be great if you add a comment on the video itself or correct that section. Many may not read the comment section and thus not realize it.
@wuneyeproduction
4 ай бұрын
@@bahmanaboulhasanzadeh8512 Exactly!! I almost did not read the comment section.
@ipvikas
3 жыл бұрын
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 |
@snjjain
3 жыл бұрын
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!
@CodeEmporium
3 жыл бұрын
Yep. I saw this in another comment too. Good eye! I'll write a clarifying comment on this soon
@migi7787
Жыл бұрын
@@CodeEmporium I loved this presentation - you're so good at touching the main points (e.g. data leakage) and all in 13 mins!! Exactly what I was looking for, well done!
@19AKS58
Ай бұрын
Great video
@mohamedgaal5340
2 жыл бұрын
Thank you for yet another awesome video! I like how you simplify data science concepts.
@Storieswithsumith
2 жыл бұрын
what is work order ???
@chessplayer0106
2 жыл бұрын
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?
@saikishore1992
Жыл бұрын
I have the same question.
@deepanshudashora5887
3 жыл бұрын
Another masterpiece by you 🙏
@CodeEmporium
3 жыл бұрын
Thanks for the compliments! Means a lot!
@Storieswithsumith
2 жыл бұрын
@@CodeEmporium you please tell me what is work order
@kabeerjaffri4015
3 жыл бұрын
Great video. Love from pakistan
@CodeEmporium
3 жыл бұрын
Awesome! Welcome aboard!
@irfanalfathramadhan4797
10 ай бұрын
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
@snjjain
3 жыл бұрын
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
@RidingWithGerdas
3 жыл бұрын
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 ?
@CodeEmporium
3 жыл бұрын
True. Like if the feature is 89 days since last purchase, chances are they will churn. And now that i think about it, it probably won't be useful making that prediction since acting on it is impossible. We could just exclude that as a feature and say "What is the probability the customer churns 3 months from making this prediction". Good eye
@RidingWithGerdas
3 жыл бұрын
@@CodeEmporium Then this would be related to Survival Analysis?
@dragomir44
3 жыл бұрын
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"?
@rohitsharma0389
3 жыл бұрын
Performing uni-variate analysis of predictor variable's relationship with target variable should answer the "hunches"/hypothesis. For instance, plotting a bar chart of Gender vs Churn will tell us whether males are likely or females are more likely to churn. In this case, we can visualize #WorkOrdersIn6Months against Churn
@munshir.c6161
2 жыл бұрын
Thank you, Could u please help me how can make a mathematical equation for churn model?
@faizaanazam
Жыл бұрын
Love it!
@YeeLeeHaw
3 жыл бұрын
Start porting your videos to LBRY's Odysee as well. You can set that to automatically upload it there whenever it uploads to KZitem 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.
@CodeEmporium
3 жыл бұрын
Thanks for the suggestion Daniel. I def might consider this
@cocoarecords
2 жыл бұрын
amazing
@rohitsharma0389
3 жыл бұрын
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.
@saikishore1992
Жыл бұрын
I have the same questions.
@varunnayyar3138
2 жыл бұрын
Precision recall definition seems to be goofed up.
@CodeEmporium
2 жыл бұрын
Yea. My b. Pinned a previous comment that said the same. Thanks!
@Storieswithsumith
2 жыл бұрын
what is work order ???
@EtienneTremblay
3 жыл бұрын
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.
@CodeEmporium
3 жыл бұрын
Ah. Good call. I think you're right with this
@deadlysk8
3 жыл бұрын
awesome
@CodeEmporium
3 жыл бұрын
Thank youu
@anjaliudoshi5729
2 жыл бұрын
Is this a Hancock 'good job'??
@SMHasan9
Жыл бұрын
I don't agree with the 2nd feature. still great vdo 👍
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