AdvancedMiner Professional

The AdvancedMiner Professional System developed by StatConsulting is a modern and advanced analytical software. It provides a wide range of tools for data transformations, construction of Data Mining models, advanced data analysis and reporting.

Besides the AdvancedMiner Professional, StatConsulting also offers training and other support services oriented toward increasing the effectiveness of working with the system.

AdvancedMiner Professional is:

  • a combination of over 9 years of experience gathered during analytical projects conducted by StatConsulting with modern IT technologies and developments in Data Mining and statistics,
  • a valued tool which meets the requirements of even the most demanding users, thanks to a versatile scripting language Gython and an advanced editor with functionality that is not available in other tools of this kind,
  • a tool for complex data exploration in an interactive graphical environment,
  • a highly efficient database engine,
  • a database engine without any restrictions on the number of columns in data tables,
  • the capability of working with huge databases (even with 5 billion records) located on a dedicated server or PC-class workstations.
  • extracting and saving data from/to different database systems and files,
  • performing a wide range of operations on data, such as sampling, joining datasets, dividing into testing/training/validating sets, assigning roles to attributes,
  • graphical and interactive data exploration,
  • outlier filtering, supplying missing values, PCA, various data transformations, etc.,
  • building association models, clustering analyses, variable importance analyses, etc.,
  • constructing various analytical models with the use of diverse Data Mining and statistical algorithms (such as classification trees, neuron networks, linear and logistic regression, K-means, association rules),
  • creation of scoring code so that the models can be integrated with other IT applications (scoring code may include the models as well as data transformations),
  • model quality evaluation and comparison of Data Mining models (LIFT, ROK, K-S, Confusion Matrix),
  • generation of model quality reports (MS Office, OpenOffice).
  • transformations of customer data from various kinds of sources and described on different levels of detail,
  • construction of data marts for analytical and reporting purposes,
  • Credit Scoring - evaluation of credibility of customers who are applying for a credit,
  • marketing campaign targeting - calculating the probability of customer response to a marketing offer,
  • customer segmentation and profiling,
  • optimization of Cross Selling and Up Selling offers,
  • market basket analysis,
  • Customer Lifetime Value analysis - estimating the expected customer value based on the profit he/she is likely to generate in the future,
  • Churn analysis – calculating the probability that a customer will stop using company's services,
  • Fraud Detection.