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Search Preprocessing , found 13 results

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Automated Data Preprocessing

preprocessing-data-with-automated-pipelines

jeremylongshore/claude-code-plugins-plus-skills

This skill builds and executes automated pipelines to clean, validate, and transform data for machine learning, covering ETL workflows, error handling, and quality reporting so Claude can deliver analysis-ready datasets.

Ray Data Processing

Orchestra-Research/AI-Research-SKILLs

Ray Data offers scalable distributed data processing for ML workloads, streaming across CPU/GPU clusters and integrating with PyTorch, TensorFlow, and Ray Train. Use it for batch inference, ETL, and multi-modal preprocessing from a laptop up to hundreds of nodes.

Clustering Analysis Assistant

running-clustering-algorithms

jeremylongshore/claude-code-plugins-plus-skills

Automates clustering workflows on supplied datasets with K-means, DBSCAN, or hierarchical algorithms, handling preprocessing, validation, metrics, and visualizations to expose groupings and anomalies.

Automatic Visa Document Translation Tool

visa-doc-translate

affaan-m/everything-claude-code

This comprehensive tool automates the process of translating non-English official documents (images) for visa applications. It handles image preprocessing (conversion and rotation), performs robust OCR using multiple frameworks, translates the extracted text into professional English while maintaining document structure, and generates a certified bilingual PDF containing both the original image and the formatted translation.

FlowIO FCS Handler

K-Dense-AI/claude-scientific-skills

FlowIO reads and writes Flow Cytometry Standard (FCS) files, exposing metadata, channels, and NumPy event arrays while allowing CSV/DataFrame conversion, making it ideal for cytometry pipelines and backend preprocessing services.

Neuropixels Analysis Toolkit

neuropixels-analysis

K-Dense-AI/claude-scientific-skills

End-to-end Neuropixels recording analysis covering SpikeGLX/OpenEphys/NWB loading, preprocessing, motion correction, Kilosort4 sorting, quality metrics, Allen/IBL curation, and AI-assisted visualization for extracellular electrophysiology.

PathML Computational Pathology Toolkit

K-Dense-AI/claude-scientific-skills

PathML is a Python toolkit for computational pathology workflows, loading 160+ slide formats, preprocessing H&E images, constructing spatial graphs, training HoVer-Net/HACTNet models, and analyzing multiplex imaging for ML-ready pathology data.

Scikit Learn Toolkit

K-Dense-AI/claude-scientific-skills

Guides developers through scikit-learn workflows for classical supervised and unsupervised tasks, data preprocessing, evaluation schemes, hyperparameter tuning, and end-to-end pipelines when building interpretable production models.

Fast UMAP Dimensionality Reduction

K-Dense-AI/claude-scientific-skills

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.

FlowIO: FCS File Parser and Handler

K-Dense-AI/scientific-agent-skills

A lightweight Python library designed for reading, parsing, and writing Flow Cytometry Standard (FCS) files. It extracts event data as NumPy arrays, handles complex metadata, and supports multiple FCS versions (2.0-3.1). Ideal for data preprocessing, building cytometry pipelines, and inspecting experimental results.

Analyzing Neuropixels Neural Recordings

neuropixels-analysis

K-Dense-AI/scientific-agent-skills

An end-to-end toolkit designed for analyzing high-density Neuropixels electrophysiology recordings. It implements best practices using SpikeInterface, covering the entire workflow: data loading (SpikeGLX, NWB), preprocessing, motion/drift correction, advanced spike sorting (Kilosort4), quality metric computation, and unit curation (including AI-assisted review). Ideal for neuroscience research.

Python Library For Machine Learning

K-Dense-AI/scientific-agent-skills

A comprehensive resource detailing the use of scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for the entire ML lifecycle, including supervised tasks (classification, regression), unsupervised tasks (clustering, dimensionality reduction), data preprocessing, model evaluation, and building production-ready pipelines.

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