A space for data analytics & AI related technical resources and discussions.
Unfortunately we are not able to publish the detailed list of incremental fixes for our daily Maintenance Builds (MB) of Altair SLC. We have the internal records of course, but they are in programmer abbreviations and not fit for public reading. Since we aim to only include bug fixes in the MB versions of SLC, we expect…
In this video we demonstrate how to already apply changes to multiple data sources and then group (summarize). Demo 3: Inventory Master Maintenance * De-duplicate the Inventory Master file of Classic Items * Identify Missing Items not on the Master File
In this video we demonstrate how to already apply changes to multiple data sources and then group (summarize). Demo 2: Cleanse & Enrich the January - Apr Sales Data File based on Demo 1 * Append the 4 tabs for Jan - Apr * Apply the Change List to the Appended Tables * Summarize Total Sales by Month
In this video we demonstrate how to import an Excel file, and how to perform a few data preparation operations to cleanse the data and group and summarize it. Demo 1: Cleanse & Enrich the January Sales Data File * Format unit price and amount as currency * Format Date as Short Date * Create a calculated field for Discounts…
The Logistic Regression block enables you to apply a logistic regression predictive model to a dataset. The following demonstrates how to use the Logistic Regression block to create a logistic regression model for an input dataset ExamResults.csv (which contains observations that describe a range of test scores from a…
The Linear Regression block enables you to apply a linear regression predictive model to a dataset. The following demonstrates how to use the Linear Regression block to predict the mass of a banana based on the other variables used to describe a banana in the input bananas.csv dataset. * Import the bananas.csv dataset onto…
The K-Means Clustering block enables you to apply a K-Means clustering model to a dataset. The following demonstrates how the K-Means Clustering block is used to split the input dataset lib_books.csv (containing observations that describe a range of books available from a lending library) into a specified number of…
The Join block enables you to combine observations from two datasets into a single working dataset. The following demonstrates how to use the Join block to link information in two input datasets * lib_books.csv, which contains observations that describe a range of books available from a lending library. * ddn_subjects.csv,…
The Impute block enables you to replace missing values in a dataset variable based on other values for that variable. The block is used to replace the missing values in the Price variable in an input dataset lib_books.csv (which contains observations that describe a range of books available from a lending library) based on…
The Hierarchical Clustering block enables you to apply a hierarchical clustering model to a dataset. The following demonstrates how to use the Hierarchical Clustering to split the input basketball_players.csv dataset (containing observations that describe baskteball players in a national league) and assign observations to…
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