Excellent work! I have been working on it for two days, but I did not grasp the main concept. However, after watching the video, I now understand the whole concept. Is my use of 'dose' correct? Also, please check the entire sentence
Hey! Great video as always. I have a question for you. In the end you're doing fit_transform with for loop. How can I do it with map, list ? When I do list(map(le.fit_transform(df_cat),df_cat)) it gives this error : y should be a 1d array, got an array of shape (513, 2) instead. How would you do map,list as an alternative to for loop ?
Great explanation! I have a question though, When we apply label encoder and the categorical column has more than 3 unique values it assigns the value as 1,2,3,4 etc. Are there any chances that our model prioritizes the category which has a higher number compared to others?
Superb and simplified video presentation...Thank you.
Thanq very much sir
Thank you so much.
These concepts were not that easy for me as you made these now.Any average learner can understand and implement these concept.
Glad that I could help you 🙂👍
super sir ,thank you soo much
Can you please give a piece of code from which we can get the number of categorical columns and numeric columns in the whole dataset
i have been working on it for long time , you made it simple .. thank you
Excellent work! I have been working on it for two days, but I did not grasp the main concept. However, after watching the video, I now understand the whole concept. Is my use of 'dose' correct? Also, please check the entire sentence
I'm getting true or false instead of 0 and 1 after applying the dummies. Why is that?
Time saver ,thank you so much🙏
Hey! Great video as always. I have a question for you. In the end you're doing fit_transform with for loop. How can I do it with map, list ? When I do list(map(le.fit_transform(df_cat),df_cat)) it gives this error :
y should be a 1d array, got an array of shape (513, 2) instead.
How would you do map,list as an alternative to for loop ?
You can also use this technique -
df_cat = df_cat.apply(lambda x : LabelEncoder().fit_transform(x))
@@sukamal_das Oh, thanks! That works and surely is an easy way to work things. Wish you good luck, thanks again !
Thanks :)
Great explanation!
I have a question though,
When we apply label encoder and the categorical column has more than 3 unique values it assigns the value as 1,2,3,4 etc. Are there any chances that our model prioritizes the category which has a higher number compared to others?
Yes you are right. To avoid this problem we can go for One Hot Encoding technique.
@@sukamal_das but what if we have 100 categories? Then it would create 99 extra columns right?
How do we handle this?
Im having error even after converting categorical values df value still shows object type
Can you share your code via github ?
@@sukamal_das yes sure can u share the link
Thankyou❤