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Cracking Data Science Interview Is Easy By This Approach!! Solve this Problem
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- čas přidán 18. 08. 2024
- In this video we will discuss about the recent interview experience of one of my subscriber and student of ineuron whi cracked a job in product based company and yes he cracked it.
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This interview cource are came up with job assistance or job guarantee??
Is there job quarantee ...?
Is there personal mock test conducted or not....if yes then personal feedback is given to candidate?
2 scenarios:
1) If there are only 2 Pin code data available and the rest are missing. Then replace the Pin code column with A Pin code dummy variable with missing as 1 and non-missing as 0.
2) If we know that multiple Pin codes are missing, if a substantial number like 90% of the Pin codes are missing, drop that column. If less, say 27% of Pin discreet categorically column and apply one hot encoding with multiple levels, wherein, look at the frequency distribution of the pin code and take a call. If the distribution is random, then push the business to provide data for these, if no data is available for this then ask the business whether this column is very important if not remove the column.
Looking forward to learning all of these soon from you Krish..super excited to see you in class..
Features to be dropped
CustomerID - It is just an id column
Pin Code - Too many data is missing
We can use a variance threshold to remove low variance features as well
City Tier has low variance
Missing values
For numerical features such as age, we can use univariate imputation techniques such as mean/median or we could use random imputation as well.
If distribution of age is normal, I would use mean
If distribution of age is skewed, I would use median
But if there are many missing values in the age column, mean/median would change the shape of the distribution of the age column
we could then use random imputation.
These are univariate techniques
But we could also use multivariate techniques such as knn imputation or mice.
For categorical features, we can use most frequent/mode or we can use random imputation
We can also use knn imputation or mice.
For pin code encoding, we can use target encoding
(but as we are already missing lots of values, I don’t think it would be necessary)
Derived features - This one would require domain knowledge
I was thinking of converting the age into a categorical feature using numerical encoding techniques like discretization/binning.
Creating categories like
30-40(Young),40-50(Mid),50+(Old)
But if we do not have domain knowledge, we could be using pca techniques to transform the high dimensional data into low dimensional and at the same time keeping the essence of the data.
In terms of feature scaling, it all depends what model we are using.
If we are using models that require the dependent variables to be normally distributed, then we can apply log transformation,box-cox transformation to convert into normal distribution
I don't get why we would want to keep pin code column? It doesn't contribute to this data because it doesn't have a pattern that may or may affect other variables let alone our decisions?it's not like we can ask this data a question of "what's a pincode pattern that can affect our target?" It doesn't make sense to me, there's a lot of missing values that evidence of even a slight correlation is nil.
1:12-Feature engineering and E.D.A takes around 30% of the project time
Really helpful... Before watching videos
Sir please upload a full interview video please sir 🙏
Happy Birthday sir
Hi Krish,
Thanks for the video,, few months over i have completed ML Course... I am from Commerce background, Feeling so much difficulty to crack 'Data Science' interviews.
Your guidance will be very helpful
Happy belated birthday Krish sir
Found an article
Leveraging Value from Postal Codes, NAICS Codes, Area Codes and Other Funky-Arse Categorical Variables in Machine Learning Models
Hi Krish, When will you take NLP live session ?
Hi Krish... thank you for the video...just wanted to know if I'm still learning DS now...can i join your stated course regarding the interview... because it's a lifetime access...but registration time may be limited right?? Just let me know please
❤️❤️ awesome sir
Please make this Data science Interview preparation course available to tech neuron also
Pincode encoding = p(class|pincode)/len(pincode column)
Big fan sir
Hi sir - If someone is enrolled for One-Neuron, will he get access to this interview course videos as well? Or he still needs to take this course separately.
+1
+1
It may/may not be available as of now, but in future many courses will be added....I bought day itself when it was announced...that time it didn't had much courses....but now too many courses including data science, data engineering, SAP , cloud, C++...etc, etc....There is no loss in One-Neuron platform...Its worth buying it
Hi sir,
Instead of Smote technique wich technique will be use for getting better results I hope you will be answering my question
cannot drop customer id if you need to make some recommendation per customer
for pincode encoding I think min-max scaling technique is a better option because using this range will be between 0 to 1.
We have city feature as tier i ii III then why do we need pincode?
Because pin code gives exact location within city
Sir, is this included in Tech Neuron also ?
@krish its showing coupon code is invalid