Steps

Collect data (e.g. in crisp_source folder). Data includes multiple text documents (txt or pdf) and ONE numeric CSV file with (optional) timestamps.
Import data into CRISP-T corpus and dataframe.
Perform linking between text and numeric data using various methods (id based, keyword based, time based, embedding based).
Explore text data using various methods (e.g., topic modeling, keyword extraction, sentiment analysis, visualizations).
Explore numeric data using various methods (e.g., summary statistics, classification, clustering, regression, association, visualizations, TDA, etc.).
Perform cross modal analysis using linked text and numeric data (e.g., text features as predictors for numeric outcomes, numeric features as predictors for text outcomes, etc.).
Add manual connections between text documents and numeric rows if needed to support theory driven analysis.
Derive insights from the analysis and document them.
Use an AI agent to help with analysis, interpretation, and documentation if needed using MCP tools.