PowerOP® Diag
Published by
Dataswati
$75
Features
AI Compatibility score
“AI Compatibility score” (before and after data cleaning – performed automatically for the purpose of analyses only and not for client’s further use). The final Artificial Intelligence Compatibility Score is built based on several sub-scores that measure data completeness, quality and predictability. The sub-scores include:
Individual completeness score: evaluates the availability of data in each column or for each variable
Overall completeness score: evaluates the availability of dataset in global
Regularity score: gives information on sampling frequency of data, a critical indicator for a good prediction
Variability score: gives information about how spread and variations on input data are propagated to the target to be predicted
![AI Compatibility score]()
Indicative prediction scores
Indicative prediction scores - with and without AI based algorithms
The first score indicates the accuracy of a prediction using simple algorithms, while the second score is the reperesentation of a prediction using sophisticated AI based algorithms offered by Dataswati. The scores are supported by graphical demonstrations of the real data versus the predicted values.
![Indicative prediction scores]()
Advanced analysis
You also have access to additional statistical information about your data:
Correlation for continuous and categorical data
Visual display of data availability in a time-frame
Visual display of missing values
![Advanced analysis]()
Chat System
Get instant replies to your questions through live chat with our experts
AI Compatibility score
“AI Compatibility score” (before and after data cleaning – performed automatically for the purpose of analyses only and not for client’s further use). The final Artificial Intelligence Compatibility Score is built based on several sub-scores that measure data completeness, quality and predictability. The sub-scores include:
Individual completeness score: evaluates the availability of data in each column or for each variable
Overall completeness score: evaluates the availability of dataset in global
Regularity score: gives information on sampling frequency of data, a critical indicator for a good prediction
Variability score: gives information about how spread and variations on input data are propagated to the target to be predicted
Indicative prediction scores
Indicative prediction scores - with and without AI based algorithms
The first score indicates the accuracy of a prediction using simple algorithms, while the second score is the reperesentation of a prediction using sophisticated AI based algorithms offered by Dataswati. The scores are supported by graphical demonstrations of the real data versus the predicted values.
Advanced analysis
You also have access to additional statistical information about your data:
Correlation for continuous and categorical data
Visual display of data availability in a time-frame
Visual display of missing values
Chat System
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