What started with a team of ten people with a...
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Areteans Descriptive Analytics Framework in Pega(aDAFP) in the space Artificial Intelligence, powered by Inferential Statistics, is a prototype solution, refers to analyze, summarize, visualize business Data and represent the historical discoveries and outcomes of Pega customer dataset, derived from a sample or population, in Feature Engineering and Preprocessing phrase of Data Science project Life cycle. This framework will also facilitates to explore the statistical information and pattern recognition from customer behavioral , demographic or transactional past data and to built the state-of-the-art Statistical AI Model.
Unique features
Key differentiators
Heatmap Analysis
Provides AI Heatmap Plot of data to check the statistical interrelation among explanatory variables with response variables where color intensity represents the depth of the relationship
Box and Whisker Analysis
Histogram Analysis
A histogram plot of predictors is to check the spread, dispersion, imbalance, and skewness of data and hence can easily observe the pattern of the distribution i.e. Gaussian-Normal Distribution, or exponential Distribution or Bi-modal Distribution, etc.
Basic Feature Engineering
Statistical Data Preprocessing Analytics for feature engineering with detailed
analysis, for Numerical Predictors, of
Imbalance and Bios effect of Target Variable
Detect Class Imbalance of the target Class(for Classification Type of Problem) and hence need to take appropriate transformations or measures to remove Biasing of target
Heatmap Analysis
Provides AI Heatmap Plot of data to check the statistical interrelation among
explanatory variables with response variables where color intensity
represents the depth of the relationship
Box and Whisker Analysis
Histogram Analysis
A histogram plot of predictors is to check the spread, dispersion, imbalance, and skewness of data and hence can easily observe the pattern of the distribution i.e. Gaussian-Normal Distribution, or exponential Distribution or Bi-modal Distribution, etc.
Basic Feature Engineering
Statistical Data Preprocessing Analytics for feature engineering with detailed
analysis, for Numerical Predictors, of
Imbalance and Bios effect of Target Variable
Detect Class Imbalance of the target Class(for Classification Type of Problem)
and hence need to take appropriate transformations or measures to remove
Biasing of target
Provides AI Heatmap Plot of data to check the statistical interrelation among explanatory variables with response variables where color intensity represents the depth of the relationship
A histogram plot of predictors is to check the spread, dispersion, imbalance, and skewness of data and hence can easily observe the pattern of the distribution i.e. Gaussian-Normal Distribution, or exponential Distribution or Bi-modal Distribution, etc.
Statistical Data Preprocessing Analytics for feature engineering with detailed
analysis, for Numerical Predictors, of
Detect Class Imbalance of the target Class(for Classification Type of Problem)
and hence need to take appropriate transformations or measures to remove
Biasing of target
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Read MoreAreteans Descriptive Analytics Framework in Pega(aDAFP) in the space Artificial Intelligence, powered by Inferential Statistics, is a prototype solution, refers to analyze, summarize, visualize business Data and represent the historical discoveries and outcomes of Pega customer dataset, derived from a sample or population, in Feature Engineering and Preprocessing phrase of Data Science project Life cycle.
Reach out to [email protected] to know more.
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