Results for “analysis”

Guides data analysis, EDA, and ML tasks by teaching, diagnosing MCPs, and deciding with the user, offering multiple options and documenting decisions.

Integrates multi-dimensional health data to detect anomalies, predict risks (hypertension, diabetes, cardiovascular), and generate personalized recommendations and interactive HTML reports.

Extends pandas for geospatial vector data analysis, including reading/writing shapefiles, GeoJSON, GeoPackage, and PostGIS, performing spatial joins, geometric operations, coordinate transformations, and creating static or interactive maps.

Generates spectrograms and multi-panel audio feature visualizations from audio files via a command-line tool.

Create, scaffold, and edit Jupyter notebooks for experiments, exploratory analysis, or tutorials using bundled templates and a helper script.

Create publication-quality statistical graphics from tabular datasets with minimal code, supporting multivariate analysis, statistical estimation, and complex multi-panel figures.

Create publication-quality statistical graphics with dataset-oriented plotting, multivariate analysis, and automatic statistical estimation using minimal code.

Create publication-quality statistical graphics in Python with dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and multi-panel figures.

Perform numerical computing, matrix operations, data analysis, and scientific visualization using MATLAB or GNU Octave.

Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.

Create publication-quality statistical graphics using Seaborn, with dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures.

Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap.

Creates interactive, publication-quality charts with 40+ types, hover tooltips, zoom, and web-embeddable exports for dashboards and exploratory analysis.

Create, analyze, and visualize complex networks and graphs in Python, covering graph construction, algorithms, generators, I/O, and visualization.

Run standard single-cell RNA-seq analysis pipelines: QC, normalization, dimensionality reduction, clustering, differential expression, and visualization using Scanpy.

Cleans datasets, detects anomalies, generates reports, and creates visualizations using pandas, scikit-learn, and plotting libraries to turn raw data into client-ready deliverables.

Create data-driven charts with Vega-Lite and Vega, covering bar, line, scatter, heatmap, area, radar, and word cloud visualizations from structured data arrays.

Analyzes uploaded data files with full Python access, producing cleaned datasets, statistical reports, charts, and dashboards via the CellCog coding agent.

Process satellite imagery, perform GIS analysis, and apply spatial machine learning across 70+ geospatial topics with code examples in 8 programming languages.

Create, analyze, and visualize complex networks and graphs in Python using NetworkX, including graph construction, algorithms, generators, I/O, and plotting.

Performs differential expression analysis on label-free quantitative (LFQ) proteomics data from MaxQuant and DIA-NN outputs, including preprocessing, imputation, statistical testing, and visualization.

Creates, manipulates, and analyzes complex networks and graphs using the NetworkX Python package, including graph algorithms, synthetic network generation, I/O, and visualization.

Create interactive, publication-quality visualizations with 40+ chart types, hover, zoom, pan, and web embedding. Choose between Plotly Express and Graph Objects for dashboards, exploratory analysis, and presentations.

Generates a polished, leadership-ready performance briefing with KPI tiles, executive narrative bullets, and a driver analysis — all as a print-ready HTML document.

Create, analyze, and visualize complex networks and graphs in Python with NetworkX, including graph algorithms, community detection, synthetic network generation, and multiple I/O formats.

Analyze S&P 500 market breadth charts and CSV data to assess market health, identify trading signals, and generate positioning recommendations for different trader types.

Create interactive, publication-quality visualizations with 40+ chart types, including scatter, bar, line, 3D, maps, and financial charts, with support for hover, zoom, pan, subplots, and export to HTML or static images.

Builds a navigable knowledge graph from any folder of files, with community detection, an audit trail, and outputs including interactive HTML, GraphRAG-ready JSON, and a plain-language report.

Map cloud infrastructure assets and relationships into a Neo4j graph using Cartography to discover attack paths, IAM permission chains, and security gaps across AWS, GCP, and Azure.

Execute Python code in a safe sandboxed environment with 100+ pre-installed libraries for data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, and automation.