(Please insert an screenshot of the Tool) __Type of tool:__ e.g. web application or desktop application __Required skills: __ - Process/material knowledge: e.g. knowledge of material properties - Digitalization knowledge: e.g. no programming __Short description of the tool: __ - Detailed description: Link to the guideline: __Use case/problem:__ Selecting material (recyclate) for specific product requirements __Description of the problem the tools solves:__ [[General] + [[Tool-specific] __Disclaimer:__ __How to use/download/access it:__ e.g. got the the gitup [[link], copy the code into [[XY] and start using\\ __Contact person of the tool: __ Stefan Bloemheuvel __Related tools:__ - Analyse and Visualize your process data with data analytics -> [Data Analytics] - Get guidance to set up a working data infrastucture -> [Data Infrastructure Wiki] - Find the right sensor to survey your process -> [Sensor Tool] - Improve internal information and material flow -> [VSM] - Match material requirements with material properties -> [Matrix] !Tool Description An important step in data analysis is data exploration, to achieve a better understanding of the data. The Exploratory Pattern Analytics (EPA) tool works on prepared/preprocessed tabular data. It provides explanatory patterns, i.e., simple rules between some parameters (e.g., temperature, pressure) that are predictive for a certain target parameter (e.g., scrap rate). This provides important insights enhancing data understanding. For example, it could be used to better understand why certain known outliers occur in process data. !Guidelines Before you get started, take a look at the [guidelines |Exploratory Pattern Analytics/Paper mill EPA tool example updated.odt] and make yourself familiar with how to use the tool. !Getting Started The tool is available through the Data Analytics tool interface at: [https://github.com/cslab-hub/Data_Analytics_DIPLAST/tree/epa]. The python interface for programmers is available at: [https://github.com/cslab-hub/sd4py].