4.3. Handling Missing Values in Machine Learning | Imputation | Dropping

why inplace is true... what does it mean

Can you plz make a video on handling categorical missing values with lots of unique values . Also how to put different datasets together with common columns that are in the other datasets that needs to be joined together.

Nice explanation but what to do for large dataset plz make a vedio on that

super well designed course

Amazing explaination sir, all my doubts are clear

Siddhardhan, This is a fantastic video. I just want to find out how to go about a dataset with fluctuating densities? How do you go about it? Do you use mean, median, or mode? Thank you so much

Nice explanation. Keep it up.👍

Bro plz make videos on outlier detection nd removal techniques

Hi, First of all the video was really helpful. But I noticed that in the head() function, the salary was NaN when the status was not placed. So I tried this: df[pd.isnull(df.salary)==True]['status'].unique(). The output was such that the salary was NaN only when the status was not placed.. I mean the unique value was just "Not Placed". So I guess it will be correct to fillna with 0 instead of median, mode or mean. I understand that you have made this video for just understanding purpose but just telling what I tried. Thanks for the video, very helpful!

Awesome thank you for all videos

Hi sir,
Sir if are given the regression problem and we have the missing values in the data . Then can we use the regression models like KNN regresor , Random Forest Regressor to find the missing values ?
and then solve our actual problem
Is this the right approach?

Don't we use pandas profiling for EDA

I tried to save the filling datas with median , mean and mode in new variables like this

dataset_median = dataset['salary'].fillna(dataset['salary'].median(), inplace = True)

after checking this below code it was throwing error

dataset_median.isnull().sum()

AttributeError: 'NoneType' object has no attribute 'isnull'

I've watched all ur ML videos ... it was very helpful to me ... damn clr explanation ...keep going sir

Is it a good idea to use imputer from Scikit-learn?

Well Explained!!

Thanks. Very useful for a quick refresher of the techniques to deal with missing values.

Bro, I am not understanding that what arguments we have to choose when we call a method of a library and how we come to know that which argument we have to choose and what is its purpose,i have read the method documentation but I can't understand plz tell me ,i am continuously watching your course and now i am on 4th module Preprocessing topic

siddhardh ji can you please make a series of videos for explaining each machine learning algorithms