Hello! previously I was using Minitab and obtaining the factor scoring coefficients and then graphing. I don't know how to get that with python, I hope you can help me please. Saludos
Could you explain for me why I have different results while implementing Factor Analysis in Python and STATA? There are differences in both scree plot and the value of loading factors :(( Don't know which is the correct one now :( Thank you for your support!
If you have trouble w/factor analyzer: 1. Open cmd and paste: pip install factor_analyzer 2. Once it is successfully installed, paste the command again and get the file path it is downloaded in (first line after the command). 3. Follow the rest of the steps in the video.
Any ideas on how one could go about getting goodness of fit indices? All my FAs are done in R purely because of these fit indices but I really want to completely move over to Python.
When I type fa.fit(df), it tells me there is an error "ValueError: Found array with 0 sample(s) (shape=(0, 38)) while a minimum of 1 is required by FactorAnalyzer." but when I check my document there is not ligne with 0 values, what can I do please?
Hi, I have one question. if we want to use FA or PCA for feature selection of all types of data(without dropping string type of data) how can we do that? Is there any alternative to apply?
Nice question but the explanation is not trivial. We consider the factor loading of each variable which tells us the entent to which the variable relates with the factor. We then select factors with highest loadings. I'll recomment you read up Principal Components Analysis(PCA) as it helps you understand FA. www.kindsonthegenius.com/pca-tutorial-1-how-to-perform-principal-components-analysis-pca/ www.kindsonthegenius.com/principal-components-analysispca-in-python-step-by-step/ www.kindsonthegenius.com/basics-of-factor-analysis-for-data-scientists/ Question, What is Factor Analysis ruclips.net/video/s2ffkELXsHc/видео.html
Hi, just finding this great video on FA, thanks for posting it. When I use the varimax orthogonal rotation and then use transform() to 'score' the dataframe with the factor model, the resulting factors do not have 0 correlation as expected from an orthogonal transformation. I'd like to take those factors as inputs into a clustering exercise but want non correlated factors (similar to PCA). Thanks in advance for any thoughts on where I might be going awry.
Answers to a questionnaire about personality (Big-Five traits). A1 through 5 represent questions about agreeableness, N is neuroticism, C is conscientiousness, E is extraversion and O is openness to experience. 1 represents an answer of -2 and 5 is actually +2. It does not really matter though, because they are all getting standardized by the standard variation and the mean. For example one question might be "I am interested in talking about abstract topics". -2 (in the matrix: 1.0) would mean that the statement does not describe you at all, while +2 (in the matrix: 5.0) means, it describes you perfectly. etc. etc.
Can someone please help me figure out the name of the test I need to perform to see if my data is suitable for factor analysis? I can not seem to hear the name. Thank you in advance.
the correct column name is "Unnamed: 0". df.drop(['Unnamed: 0'], axis = 1, inplace = True) works...
Great video, helped out a lot it processing my data using factor analysis for my data science class.
df = drop(['unnamed:0'], axis=1, inplace = True) This one works.
This is by far the best video on the internet to explain factor analysis and all the steps necessary! Great work!
Glad it was helpful!
Wow, this is great. i wish i can like this video multiple times
Hi, that is great work.
Hi, that is an amazing video.
This video litterally saved me and my Master's Dissertation. Great explanations, easy to understand and follow. thank you very much!!
Hello! previously I was using Minitab and obtaining the factor scoring coefficients and then graphing.
I don't know how to get that with python, I hope you can help me please.
Saludos
Thank you very much, final year computer science but very little experience in statistics etc. this was a massive help
You're very welcome!
Could you explain for me why I have different results while implementing Factor Analysis in Python and STATA? There are differences in both scree plot and the value of loading factors :(( Don't know which is the correct one now :( Thank you for your support!
If you have trouble w/factor analyzer:
1. Open cmd and paste: pip install factor_analyzer
2. Once it is successfully installed, paste the command again and get the file path it is downloaded in (first line after the command).
3. Follow the rest of the steps in the video.
Thanks for the observation!
Helpfull video, thanks!
very useful as it is!!! thanks a ton!
Thanks for this excellent video
So nice of you
Thanks!! this video was very helpful to me :D
Thank you for your sharing and fantastic guiding
Any ideas on how one could go about getting goodness of fit indices? All my FAs are done in R purely because of these fit indices but I really want to completely move over to Python.
When I type fa.fit(df), it tells me there is an error "ValueError: Found array with 0 sample(s) (shape=(0, 38)) while a minimum of 1 is required by FactorAnalyzer." but when I check my document there is not ligne with 0 values, what can I do please?
Hi, I have one question. if we want to use FA or PCA for feature selection of all types of data(without dropping string type of data) how can we do that? Is there any alternative to apply?
Good work!
where can I find your Jupyter notebook?
Would you like to join and 6 Weeks Intensive Data Science Course that begins this week? Let me know on mail@kindsonthegenius.com.
Even after appending the site package directory the factor_analyzer is not importing. Can you help?
Hi, I am trying to analyse data with multiple factors over a series of time. Is there a way I can consult you off youtube?
Could you explain me on what basis the factors were selected in FactorAnalyzer
Nice question but the explanation is not trivial. We consider the factor loading of each variable which tells us the entent to which the variable relates with the factor. We then select factors with highest loadings. I'll recomment you read up Principal Components Analysis(PCA) as it helps you understand FA.
www.kindsonthegenius.com/pca-tutorial-1-how-to-perform-principal-components-analysis-pca/
www.kindsonthegenius.com/principal-components-analysispca-in-python-step-by-step/
www.kindsonthegenius.com/basics-of-factor-analysis-for-data-scientists/
Question, What is Factor Analysis ruclips.net/video/s2ffkELXsHc/видео.html
Hi, just finding this great video on FA, thanks for posting it. When I use the varimax orthogonal rotation and then use transform() to 'score' the dataframe with the factor model, the resulting factors do not have 0 correlation as expected from an orthogonal transformation. I'd like to take those factors as inputs into a clustering exercise but want non correlated factors (similar to PCA). Thanks in advance for any thoughts on where I might be going awry.
I had an ModuleNotFoundError for factor_analyzer in the first step. How i solve this error? Please help me
i cant see the link to the dataset please
This is such a great video.. Thank you so much
You're welcome! And do remember to subscribe 😃
do you know of algorithms to use other criteria to select factors? like cng package in R? Horn's PA, etc.?
I’ll try to make a video on this
good factor analysis for python
Is there something called "Factor Scores" as well?
Thank you, bro.
Please, tell me what the values in the matrix signify.
Answers to a questionnaire about personality (Big-Five traits). A1 through 5 represent questions about agreeableness, N is neuroticism, C is conscientiousness, E is extraversion and O is openness to experience. 1 represents an answer of -2 and 5 is actually +2. It does not really matter though, because they are all getting standardized by the standard variation and the mean. For example one question might be "I am interested in talking about abstract topics". -2 (in the matrix: 1.0) would mean that the statement does not describe you at all, while +2 (in the matrix: 5.0) means, it describes you perfectly. etc. etc.
thank you very much
How does one load the factor analyzer package? Where does one find the package?
pip install factor_analyzer
In 15:51-16:07 you said ev=eigenvectors and v=eigenvalues.
But during the scree plot, you said ev = eigenvalues.
Which is correct?
ev is the eigenvectors.
Can someone please help me figure out the name of the test I need to perform to see if my data is suitable for factor analysis? I can not seem to hear the name. Thank you in advance.
KMO and Bartlett's test of sphericity can help you in knowing if data is suitable for factor analysis or not.....
You just copied datacamp's tutorial, including comments