Machine Learning with Python video 6 : Handling missing term in dataset using SimpleImputer

In this video i will show you How do I handle missing values in pandas?.Most datasets contain "missing values", meaning that the data is incomplete. Deciding how to handle missing values can be challenging! In this video, I'll cover all of the basics: how missing values are represented in sklearn, how to locate them, and options for how to drop them or fill them in.

realted video title :
Python for Machine Learning - Part 15 - Handling Missing Values Using Imputer
Missing Value - Simple Imputation

realted tags :
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tags : #SimpleImputer #Data_Preprocessing #machine_learning #i_know_python

[bradleyli]

You are an amazing instructor! Your explanation is clear, concise and hits the point! Hope that you can continue posting more quality videos!

[striders]

I am not understanding your row and column explanation. I will suggest you write the script before making video.... Very helpful video btw. Keep it up.

[brunomartel]

awesome, recommendations: make code bigger, use fullscreen to reduce distracting stuff, love!

[chiomaobiajulu]

nice video but you did not explain the indexing in clear terms. You kept on mixing the rows and columns in your speech (while explaining). Therefore, it is difficult to understand which slicing is for the row and which is for the column in this statement: df[1:, 1:8]

[mohammeddanishreza]

9:02...in python slicing the first term is excluded??? srsly?? so far i have read that the first term of slicing is included and the last term is exluded....

[NiketVania]

At 9:23 I am getting error and I searched for it and found iloc is missing. So, I added it now it's showing SettingWithCopyWarning "A value is trying to be set on a copy of a slice from a DataFrame"

[jaypatil]

nice video but please make sure you use a little zoom onto code, it is hardly visible

[yogeshbharadwaj]

Hello sir, in your example the String or categorical column was present as first and last column so it was excluded easily, but if its present in diff position like 5th column then 9th column then 13th column then how to pass only numerical columns to Imputer ? Please suggest. tks

[kilipsbyhades]

the .csv file you have shared is allready being solved for empty values..share the file before doing the operation.

[adelinejohn]

Hi, May i know how to do if I'm getting this error - 'numpy.ndarray' object has no attribute 'dropna' ?

[nehasinghjaswal]

Can we save the dataset after filling in these missing values? If yes then how?
Next, can we do it in one go for a dataset having 5826 columns and 339 rows?
Can you share your contact details as I really need some help?
Thanks in anticipation.

[datascientist]

Can you make video on multiple imputation predictive mean matching

[svitirur]

Kinldly help It gives me " TypeError: float() argument must be a string or a number, not 'SimpleImputer' " This my code :- from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values = np.nan, strategy='mean')
imputer = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])

[xeniavotsidi]

you should have define first how to install the libraries

[chandrap]

Thanks for the explanation. I am trying to run the same code, but getting an error. Please help. Thanks and regards.

ModuleNotFoundError Traceback (most recent call last)
Cell In [3], line 1
----> 1 from sklearn.input import SimpleImputer
ModuleNotFoundError: No module named 'sklearn.input'