Data Preprocessing 01: StandardScaler Machine Learning | Scikit Learn | Sklearn | Python |

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Data Preprocessing 01: StandardScaler Machine Learning | Scikit Learn | Sklearn | Python |

About this video: In this video, you will learn about StandardScaler in Python

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Thanks. It will be helpful for beginners to let them know why/the purpose standardizing the features

dagma

Thank you! This explains things much more clearly than my textbook.

RhoChalmers

thanks for sharing,
I want to ask if there is a manual calculation of the numbers formed from standardScaler processing?

zain

is that to say that the approximate values of the standard scalar mean of displacement and weight is zero?

otekanonso

Great video! Do you know if when we implement StandardScaler through the Pipeline we are doing it this way or if we are doing a fit_transform? How would it be done this way? Thanks

daniela.lapena

Thank you for ur teaching. Just i don't understand what the ''axis = 0' means.

youyangpeng

Do we ever need to standardize the dependent variable "y"?

lolikpof

now thats its scaled, now you just train model in this transformed data?

BibleSamurai

hi sir, how can I calculate the standardized value from init value by mean and scale. I want apply for my program on my MCU. Hope your answer. Thanks

thaivuo

Very helpful tutorial, but I have a small problem. What to do if df.shape() returns an error : tuple object is not callable? Should I modify data type?

mp

00:02 Standardization makes features look like a standard normally distributed data with mean 0 and unit variables.
01:40 Applying standardization on specific integer and float number variables.
03:22 Standardize variables using StandardScaler from the pre-processing library.
05:17 Using StandardScaler for data preprocessing
06:50 StandardScaler transforms data to standardized values
08:35 StandardScaler transforms data to have mean 0 and variance 1
10:12 StandardScaler transformation on test data and analysis of mean and variance.
11:56 Using StandardScaler for data standardization in Python
Crafted by Merlin AI.

AnhQuan

For the test data, we should be re-using the scaler object resulting from fitting only the train data, right?
something like...
ss = StandardScalar()
ss.fit(X_train)
ss.transform(X_train)
ss.transform(X_test)

_seeker

Thanks for your effort. I really appreciate it.

ettavictor

this is same as z-score normalization?

Sinsanevlog

what is the meaning of random state parameter while splitting the data?

mayankbaber

In this tutorial should we also transform mpg and acceleration columns?

sherin

Great Video! Do you know where I can get the data set?

maxmacken

You did't explain, what exactly StandardScaler did behind the scene. you just explained how to do it.

GridoWit

it was a very helpful video but why do we need to standardize the data ??

sheetalkumari

Just wanted to know how to get the mean easily.... Thanks

Hard_Online

Data Preprocessing 01: StandardScaler Machine Learning | Scikit Learn | Sklearn | Python |

Data Preprocessing 01: StandardScaler Machine Learning | Scikit Learn | Sklearn | Python |

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