Search Results for "dataset"

Kaggle CLI is Kaggle’s official command-line interface for interacting with the Kaggle platform from a terminal. It lets users authenticate, search resources, download files, submit competition entries, manage datasets, work with models, run notebooks, and read discussion content without relying only on the web interface. The tool is useful for data scientists who want to automate Kaggle workflows inside scripts, CI jobs, notebooks, or reproducible local environments. It supports both...

...It uses YAML-based contract files to define the structure, meaning, quality rules, service levels, and connection details for a data product. The tool can connect to real data sources and check whether the actual dataset matches the schema, constraints, and quality expectations described in the contract. It supports both the Data Contract Specification and the Open Data Contract Standard, making it useful for teams standardizing data governance across different platforms. It can run locally, inside CI/CD pipelines, through Docker, or directly from Python code. ...

Input 1 - A training dataset (multiple observations) of up to four variables (predictors) against one (response variable) Input 2 - A test dataset (multiple observations) of the same four variables with unknown response variable Output - Calculation of the response variable for each test observation using fuzzy logic or fuzzy rule-based Bayesian algorithms HABFUZZ is a habitat model, which can be used in ecohydraulic modelling applications for the calculation of the instream habitat suitability in various discharge scenarios in a simulated river reach. ...

...Now, it is possible to sequence all organisms present in a metagenomic community. However, a critical problem is to determine the host organism for each genomic fragment present in the dataset. This project provides the computational tools for assigning the organismal identity for each genomic scaffold in the given dataset. For more detailed documentation please refer to the link below: https://sourceforge.net/p/ngsscaffolds/wiki/Documentation/

...Malware Classifier uses machine learning algorithms to classify Win32 binaries – EXEs and DLLs – into three classes: 0 for “clean,” 1 for “malicious,” or “UNKNOWN.” The tool was developed using models resultant from running the J48, J48 Graft, PART, and Ridor machine-learning algorithms on a dataset of approximately 100,000 malicious programs and 16,000 clean programs. The tool extracts seven key features from an unknown binary, feeds them to one of the four classifiers or all of them, and presents its classification of the unknown binary.