Machine Learning with Python video 7:How to Handle Categorical Data||OneHotEncoding||ColumnTransform
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- Опубліковано 15 бер 2020
- In this video i will show you how you can handle categorical data . it will be done in two steps
1) LabelEncder to give numerical value to each category
2) one hot encoding and column transform to give each category a separate column
How do I encode categorical features using scikit-learn?
source code : github.com/harshitroy2605/han...
link to machine learning playlist : • Machine learning with ...
related video title:
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thanks man! helped a lot! All old tutorials show old sklearn libraries. Very tough to excute when you are a beginner in ML
sir, I need your help I am working on English premier league prediction so my target variable is Full-time result(FTR) and FTR have a categorical value that is H, A, and D so how to handle that kind of problem if our target variable is a categorical value like H, A and D, H means Home and A means Away and D means Draw
Hi , may i know how we can use columntransformer if we need to apply the one hot encoding multiple columns at once
Why do we need to do Label Encoding before the column transformer step? Can't we just use ColumnTransformer with OneHotENcoder only? without using Label Encoder?
I paid an Udemy course that couldn't explain this topic, you help me a lot man I'll follow your course for sure
what course?
Very great and clear work you have done. i have a question please, i am working on my raster dataset for prediction like ANN, RF and CNN, i have converted the rasters into numeric and then train and test the data and got very good accuracy. Now i need to convert my test data into raster again as final prediction map but i don't know how to do this, please guide me thanks.
did you find the solution? if yes please share i am having the same problem
how to decode ? I stucked in such a situation where I need decoding after encoding
Very nice but what if I have 10 or more categorical features and each feature with 20-30 categories in it. How should I tackle that kind of data?
let me know how you solved it please
Great video
How do we retrieve column names for the final dataframe
ct.get_feature_names()
y.columns = ct.get_feature_names()
@@vishalrao4817 does not work, can you explain??
Good job !
Hey bro,keep it up
Please do video explaining KNN and random forest is possible as well
Yup bro i will
I know python thanks, do you mind if u make it ur next upload, cus currently doin an assignment and still can’t get my head around the concepts fully and it would really help me out
Voice sound is very low☹️
yes...
Kindly make loud voice
sir tumara sound chimani gat hai thoda kavleki tara karo means bada
please use good microphone .. otherwise all good
yes sound is too low. I had to use headphones.
Yup on that day i was recording in a very noisy background soo i have to eliminate all of them while editing hence noise was also decreased 😊😊
awaaz badha le bhai full volume pe bhi nhi aa rhi awaax tumhari
Instead of df[:,8] = couldn't we just state df['origin'] ? When we want to transform/work directly on the dataframe, not the array. Actually could you redo this video working directly on dataframes NOT arrays...
I actually stopped watching when I noticed that he is doing things manually and in the wrong way. Why did he count columns? Why did he not use the dataframe as it is?
Madame ji thoda tez nahi bol Sakti kya app
Nhi bhabhi jii
O bhai your voice sound is too low.. Seems like you are making video under pressure of talibanis
not funny