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Cohort Analysis with Python from Scratch | Easy Code 

Absent Data
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Learn how to create cohorts with a real world dataset with Python skills. If you are a marketer, this is an essential skill you should learn.
Find the code and dataset
github.com/Gaelim/Cohort-Anal...
#marketing
#datasciene
#dataanlysis

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3 июл 2024

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Комментарии : 30   
@ronenTheBarbarian
@ronenTheBarbarian 2 года назад
Thanks a lot for this! Really simple code and great explanations throughout. Keep up the great content fellow data person.
@jessicahaire4024
@jessicahaire4024 Год назад
Awesome code along session -- pace was great! Thanks for explaining all the steps and subtleties, and for pointing us to the dataset on the UCI repository.
@absentdata
@absentdata Год назад
You're very welcome! Yes, I tried to keep the pace slow.
@ju-lyndav7087
@ju-lyndav7087 10 месяцев назад
Thank you so much. This has really helped me understand things.
@saya5664
@saya5664 2 года назад
Great video! thank you and looking forward to more videos like this too :)
@absentdata
@absentdata 2 года назад
Thank you! Will do!
@youyou8803
@youyou8803 2 года назад
Amazing channel, would love to watch more videos like this. :)
@absentdata
@absentdata 2 года назад
More to come!
@samfreeman8328
@samfreeman8328 Год назад
Thanks a lot for your heat map tutorial!
@absentdata
@absentdata Год назад
Thanks for watching!
@SanthoshKumar-kx1xh
@SanthoshKumar-kx1xh Год назад
Beautifully explained.
@absentdata
@absentdata Год назад
Thanks for the great compliment. Share the vid if you think if will help someone
@alexkim7270
@alexkim7270 11 месяцев назад
Dear good sir I loved this pace so much. It is much easier to understand everything from top to bottom than asking ChatGPT to spit the code out (was rushing for time hence not much time to digest everything given).
@absentdata
@absentdata 11 месяцев назад
Glad to hear that
@NamPham15019
@NamPham15019 11 месяцев назад
Amazing video, I am a fan of your contents. Would it be possible if you can have some of the intermediate and advanced videos about market segments, revenue and cohort, and predictive analysis please. Truly appreciate it
@djaadiabdellah9081
@djaadiabdellah9081 2 года назад
Great video
@absentdata
@absentdata 2 года назад
Glad you liked it
@leonardofreitas8900
@leonardofreitas8900 Год назад
Could you please provide a more detailed explanation of the outlier displayed at the end. Could it be caused by a problem with the data? Thank you for the video! Great content!
@AffairsBibes
@AffairsBibes 5 месяцев назад
Thanks ❤❤
@ahmadel-ashery8860
@ahmadel-ashery8860 Год назад
Amazing Thank you
@absentdata
@absentdata Год назад
Your Welcome 😊
@akwamfoneventus6124
@akwamfoneventus6124 Год назад
Thank you for this tutorial, this has been most helpful. But I have some things I would love to change - How do I make the values in 100s show as whole numbers rather than the "1.2e+02" format ? - How do I move the cohort index to the top of the chart instead of te botom on the visuals - How can I make the plot interactive such that it will show the customerid that made up a certain cell in the cohorts ? Thank you
@akwamfoneventus6124
@akwamfoneventus6124 Год назад
You can get rid of the scientific notation in the heatmap specifying fmt='.2f' or fmt='.0f'
@ohreinaldo1990
@ohreinaldo1990 Год назад
Thanks for this awesome video and explanation! Just a question... would the same rational work for a dataframe with a single customer ID per month (1 customerID per row, per month) and two columns (activation date / exit date)? Pls take into consideration that null exit date means the customer is still active... The idea is basically the same, which is check for how long my customers are active. Thank you in advance!
@absentdata
@absentdata Год назад
Hi thanks for the comment. Yes you could do that. However, evaluating how a customer is still active would only require grouping customer with a null exit date and comparing the start date to the current date.
@FindingyourKey
@FindingyourKey 2 года назад
Thanks so much for this great video. I had a question about Invoice date. In my use case, We have a purchase date of when the client bought our monthly subscription and cancel date for when they cancelled it. Can I swap Invoice month to cancellation month and use the same method?
@absentdata
@absentdata 2 года назад
If I am understanding the scenario correctly. Then yes you can.
@giandenorte
@giandenorte 2 года назад
First!
@absentdata
@absentdata 2 года назад
Second! :)
@mohamed.montaser
@mohamed.montaser Год назад
why you didn't transform cohort_data to dataframe?
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