Feature Engineering Secret From A Kaggle Grandmaster
Ғылым және технология
Learn how to do feature engineering for tabular data like a Kaggle Grandmaster and get high-performance machine learning models.
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0:00 Intro
1:38 The One Question To Ask Yourself
2:40 Credit Card Fraud Examples
6:34 Brief Info On Categorical Features
7:23 Time Series Feature Engineering
11:53 An Extremely Valuable Exercise To Improve Feature Engineering Skills
18:18 An Useful Feature Engineering Guide and Library in Python
19:21 Spatial Feature Engineering
20:12 Graph-based Feature Engineering
Links:
Facebook Competition: www.kaggle.com/c/facebook-rec...
Small Yellow Duck Solution: small-yellow-duck.github.io/au...
www.kaggle.com/c/facebook-rec...
FeatureTools: featuretools.alteryx.com/en/s...
Kazuki Onodera Instacart Solution: github.com/KazukiOnodera/Inst...
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Пікірлер: 18
Thank you. You did so very much in such little time in comparison to TWO different bootcamp instructors could in so much time...
Love the videos and blogs- absolute mad content, thank you very much
I am in a Kaggle competition. Learnt a lot from this video!! Thank you so much for uploading this video for us!!
Fantastic video, so many useful references, I'm glad I watched the entire thing!
Thank you so much man crazy good explanation
It would be great if you could show demo also , thank you for information
Thank you! :) ♥
Good One!!!!! Expecting more from You!!!!!!
Incredible sir
Thank you so much! This has been very helpful in getting me to think differently about feature engineering
@Forecastegy
2 жыл бұрын
Glad it was helpful!
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.
Difference between time features would lead to negative values. Do we take min max scaler after that?
@ozan4702
Жыл бұрын
You would want to apply difference such that future data is subtracted from past so its never negative.
@darkchoco7407
11 ай бұрын
No problem having negative values as features, at all
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
@dimka11ggg
Жыл бұрын
Try different versions, probably examples for some old versions