Your video is very good and I am very helpful. but I want to ask, what if you want to predict house prices using skewness transformation as explained? because after doing skewness transformation, the data will have a different range from the original data. then if you want to predict house prices, it must have a different value from the market price. so, can you predict house prices with skewness transformation and a different range from the original house price data?
Thank you for making this video! I was just curious about why we use only Logr, square or cubic functions to deal with skewed data. are there other ways to deal with skewness?
There's pandas replace function to replace values in any column, you just need to replace 0 with a smaller number so that the log and reciprocal transformations don't fail 💪
Hi Satyajit, to remove skewness of the dataset do we need to do this for every column in the data of the dataset? Is there any process that can remove skewness of the dataset?
![USEFUL OR TRASH? Kurtosis and Skewness [WATCH THIS, BEFORE YOU USE]](http://i.ytimg.com/vi/Nhpe0uSf0Ts/mqdefault.jpg)
Clear explanation with actual practical tips...
Watched many but got clear vision towards skewness by your video only. Thanks for the video
😍😍
Very informative.. on Skewness.... Request you to upload more n more video on stats topics.
Thanks you so much Satyajit Sir
Ofcourse Nishiraj
Awesome. Very good content🔥
Thanks, pls share it among friends too.
Amazing
One more Informative video 💯🙏
Thanks Vishal 😎
you have good video
Solved my assignment from this video..
lecture madhe laksha dila pahije XD
easy hota
Superb
💪💪
That was really helpful thank you
came from binks...love your channel
Thanks bro ❤️
Good one 👍
thanks.
Teach us which model to apply on a given dataset
Your video is very good and I am very helpful. but I want to ask, what if you want to predict house prices using skewness transformation as explained? because after doing skewness transformation, the data will have a different range from the original data. then if you want to predict house prices, it must have a different value from the market price. so, can you predict house prices with skewness transformation and a different range from the original house price data?
If you transform your data to get rid of your skewness then for new data where you want to predict, you need to do similar transformations too
Came here from binks channel
Thank you for making this video! I was just curious about why we use only Logr, square or cubic functions to deal with skewed data. are there other ways to deal with skewness?
very informative.. but how to replace values 0 and 0.01?
There's pandas replace function to replace values in any column, you just need to replace 0 with a smaller number so that the log and reciprocal transformations don't fail 💪
Is it mandatory that for training of every machine learning model, data must be normally distributed?
not mandatory
First we should normalize the sat then handle the skewness or for first we should handle skewness then normalize the data ?
If there are missing values and the data is right skewed we fill missing data using median and not mean? Can you do a video why this happens?
Hi Satyajit, to remove skewness of the dataset do we need to do this for every column in the data of the dataset? Is there any process that can remove skewness of the dataset?
There's a long discussion going on this over LinkedIn, I am in touch with a statistician, will revert back with the best answer soon..
@@SatyajitPattnaik Thanks
Can you make a video on hypothesis testing and its p-value with a dataset, how it is tested and used
To stock market ko captured par liya jayega python lagakar 🤣🤣🤣🤣
To some extent, definitely not perfectly because there are many factors that drive stock market rates
@@SatyajitPattnaik to merey reserch karwavo jo sarey factor ka use karkey ho jayega
So simple 🤣🤣🤣🤣