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Handling Mixed Variables | Feature Engineering

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When dealing with mixed variables (both numerical and categorical) in feature engineering, strategies involve techniques like one-hot encoding for categorical variables and scaling for numerical ones. These methods ensure compatibility and enhance the effectiveness of mixed variable data in machine learning models.
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Опубликовано:

19 апр 2021

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Комментарии : 29

@shahrukhahmad4127 Месяц назад

I am working a data scientist and I am training one ML algoithm for customer churn prediction, believe me I am facing this issue only. I was so confuted how to tackle these type of column in real time, but trust me you made everything simple..No one in youtube coves these in depth details. I am continously watching this playlist and this is helping me alot in my real time project...Once again thank you nitish sir, love you always. Keep making open source content.

@indiann871 Месяц назад

how you got the job bro? I mean what are the expectations of the company from just an undergraduate student?

@lego-xq4fh 2 года назад

Sir why is your channel so underrated ? I think you are better than most teachers I've seen. Your simplicity to explain is what stands out. Hindi and english both are under your control. Thanks

@MuhammadJunaid-yr8jd Год назад

I think you are better than most teachers I've seen. Your simplicity to explain is what stands out. Hindi and english both are under your control. Thanks

@core4032 2 года назад

Very professional-level way of teaching sir.

@stevegabrial1106 3 года назад

Superb, cant wait for upcoming video, thanks a lot, hats off for your work and make machine learning simple.

@core4032 2 года назад

This course gives me very valuable lessons . Awesome

@lfjv 10 месяцев назад

I enjoy the practical examples which you give in your videos, like Indian railways coach no. is a mixed datatype (in this one).

@sanaayakurup5453 Год назад

your videos are amazing, thankyou so much for providing all of these gems for free!

@vishu8770 2 года назад

this tutorial is my carrier changing tutorial.

@akshaysingh7962 Месяц назад

Thanks for explaining these case scenarios sir. I haven't encountered below scenario but can you tell me how to handle it: Suppose there is a cost column with below values: 4852 5,324 Five Thousand and twenty one Three eight two zero So basically the user has input the cost in different formats. Also how can we prevent such instances to occur. Thanks in advance.

@thebestofbest2626 2 года назад

thank you so much for all your efforts sir ,really so useful

@core4032 2 года назад

I think youtube ke recomender system me apka tag nahi aaya hoga , varna campus x channel boom ML community .

@amansingh-ih2by 2 года назад

use thonny to understand workflow or how the program will be executed

@raghavagrawal6263 3 года назад

Please include one lecture for handling imbalance dataset. Please sir.

@vishu8770 2 года назад

what is imbalance dataset ??

@pritomroy2465 Год назад

But how to work with the missing values. Either one should impute or drop the missing. What should one do here to tackle the missing values that were created after transformation?