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First time using PDFfiller. When I copy and past to make a document I would like to change the font color from black and blue or another color. Other than that, like the service
2017-05-31
Great, this website has allowed me to be able to document forms I needed in my current court case and save me tons of money from hiring a lawyer. Thank you!!!!
2017-07-10
The outputs of the system are great ... just your opening page is a bit confusing. I just wanted to look convert a document but couldn't see conversion icons - finally worked out that I have to use Add New button - remember people are coming to your website from other portals they have used before - and like me they look for a document conversion tool!
2019-11-19
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PDFfiller actually has lots of online tools that can help with many general document editing tasks. We can convert PDF files to lot of different document formats such as DOC files, which are files I use often at work. For document signing, editing and adding fillable fields is not complicated. The toolbar has tools that are neatly laid out and easy to access. There are lots of click-and-drag features that make using PDFfiller comfortable, even with hours of editing. There are also helpful collaboration options that let internal staff communicate about document edits.
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PDFfiller can be a valuable solution if you need to make lots of edits to PDF files and don't have a much experience with more advanced software like Adobe Acrobat. It seems to be designed for beginner users, but can also work for more expert document editors.
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Even though I know how to use various popular PDF and document editing software, I often turn to PDFfiller to allow for more intuitive editing features and its abilities to work with just about any PDF we load into it. It's a versatile editing solution that lets us quickly format our documents to our specifications.
PDFfiller actually has lots of online tools that can help with many general document editing tasks. We can convert PDF files to lot of different document formats such as DOC files, which are files I use often at work. For document signing, editing and adding fillable fields is not complicated. The toolbar has tools that are neatly laid out and easy to access. There are lots of click-and-drag features that make using PDFfiller comfortable, even with hours of editing. There are also helpful collaboration options that let internal staff communicate about document edits.
What do you dislike?
From an admin perspective, it's been a delight working with PDFfiller, but some people that receive our digital documents can sometimes have trouble accessing the files and knowing how to sign them.
Recommendations to others considering the product:
PDFfiller can be a valuable solution if you need to make lots of edits to PDF files and don't have a much experience with more advanced software like Adobe Acrobat. It seems to be designed for beginner users, but can also work for more expert document editors.
What problems are you solving with the product? What benefits have you realized?
Even though I know how to use various popular PDF and document editing software, I often turn to PDFfiller to allow for more intuitive editing features and its abilities to work with just about any PDF we load into it. It's a versatile editing solution that lets us quickly format our documents to our specifications.
2019-11-17
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Sometimes it lags and it can be a pain, but overall this is a great software to use and I don't have many cons.
2018-03-27
Their customer service is great
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PDF filler is easy to use and a great tool for me who is using it constantly to merge files and reduce them to a smaller size. Especially useful for anyone applying for jobs and need to have everything in one document.
2023-03-16
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2020-11-14
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2020-11-12
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Should you standardize data before applying PCA?
Yes, it is necessary to normalize data before performing PCA. The PCA calculates a new projection of your data set. If you normalize your data, all variables have the same standard deviation, thus all variables have the same weight and your PCA calculates relevant axis.
Should I standardize before PCA?
Hello ! Yes, it is necessary to normalize data before performing PCA. The PCA calculates a new projection of your data set. And the new axis are based on the standard deviation of your variables.
Why do we standardize data for PCA?
2 Answers. Normalization is important in PCA since it is a variance maximizing exercise. It projects your original data onto directions which maximize the variance. The first plot below shows the amount of total variance explained in the different principal components her we have not normalized the data.
Do you need to standardize data for PCA?
Yes, it is necessary to normalize data before performing PCA. If you normalize your data, all variables have the same standard deviation, thus all variables have the same weight and your PCA calculates relevant axis.
Why do we need to standardize data?
Data standardization is about making sure that data is internally consistent. That is, each data type has the same content and format. Standardized values are useful for tracking data that isn't easy to compare otherwise. For example, suppose you and your friend went to different universities.
What is the goal of PCA?
PCA is a widely used statistical tool for dimension reduction. The objective of PCA is to find common factors, the so called principal components, in form of linear combinations of the variables under investigation, and to rank them according to their importance.
How do you standardize data?
Subtract mean and divide by standard deviation: Center the data and change the units to standard deviations. Subtract mean: Center the data. Divide by standard deviation: Standardize the scale for each variable that you specify, so that you can compare them on a similar scale.
What type of data should be used for PCA?
PCA works best on data set having 3 or higher dimensions. Because, with higher dimensions, it becomes increasingly difficult to make interpretations from the resultant cloud of data. PCA is applied on a data set with numeric variables.
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