00:01 Learn feature engineering for high performance models 02:00 Aggregation is essential for extracting useful information from tables and can be compared to the group-by function in various programming languages. 03:56 Feature engineering involves creating customer-specific features to predict fraud in transactions. 06:01 Feature Engineering is all about aggregation and encoding for capturing patterns and anomalies. 08:00 Feature engineering techniques like lag, difference, rolling, and date components are significant for analyzing time series data. 09:55 Seasonal patterns and time differences for feature engineering 11:55 Reverse engineer feature computation from Kaggle solutions 13:57 Feature engineering can be applied universally in tabular data for extracting features from multiple tables. 15:47 Feature engineering techniques used in data processing 17:41 Utilizing feature engineering to create indicators for bot usage from IP data. 19:22 Geolocation and network features are key for advanced feature engineering. 21:03 Graph features are important for model prediction.
I just found your video and it's great. The reference to FeatureTools was frustrating to say the least. The documentation on the site is not working and the github repo also has examples that just don't work. It's too bad
I am in a Kaggle competition. Learnt a lot from this video!! Thank you so much for uploading this video for us!!
Thank you. You did so very much in such little time in comparison to TWO different bootcamp instructors could in so much time...
00:01 Learn feature engineering for high performance models
02:00 Aggregation is essential for extracting useful information from tables and can be compared to the group-by function in various programming languages.
03:56 Feature engineering involves creating customer-specific features to predict fraud in transactions.
06:01 Feature Engineering is all about aggregation and encoding for capturing patterns and anomalies.
08:00 Feature engineering techniques like lag, difference, rolling, and date components are significant for analyzing time series data.
09:55 Seasonal patterns and time differences for feature engineering
11:55 Reverse engineer feature computation from Kaggle solutions
13:57 Feature engineering can be applied universally in tabular data for extracting features from multiple tables.
15:47 Feature engineering techniques used in data processing
17:41 Utilizing feature engineering to create indicators for bot usage from IP data.
19:22 Geolocation and network features are key for advanced feature engineering.
21:03 Graph features are important for model prediction.
Thank you for such an amazing video!!! It is incredibly useful!!!
It would be great if you could show demo also , thank you for information
Thank you so much! This has been very helpful in getting me to think differently about feature engineering
Glad it was helpful!
Love the videos and blogs- absolute mad content, thank you very much
Fantastic video, so many useful references, I'm glad I watched the entire thing!
Very nicely explained. Your videos are good. Why did you start making them?
Thank you so much man
crazy good explanation
Good One!!!!! Expecting more from You!!!!!!
Should i learn feature Engineering in 2024?
Thank you a lot!
I just found your video and it's great. The reference to FeatureTools was frustrating to say the least. The documentation on the site is not working and the github repo also has examples that just don't work. It's too bad
Try different versions, probably examples for some old versions
Incredible sir
Thank you! :) ♥
It would be better if you use slides with key points. It was distracting by the 'hand-writing' on the screen & hard to read. Anyway, thanks
Difference between time features would lead to negative values. Do we take min max scaler after that?
You would want to apply difference such that future data is subtracted from past so its never negative.
No problem having negative values as features, at all
where are new videos!!!