Get Started: BigFrames Predictor: Modeling Classical Chorales

This project combines data science with classical harmony. By importing a dataset of Bach chorales into BigQuery and querying the THIRI tension profile, we can train a classifier.

The classifier predicts the next voice-leading step based on historical preference values.

Python BigFrames Scaffolding:

import bigframes.pandas as bpd
# Load chorale data and join with THIRI tension parameters

🤖 File an Issue for an Autonomous Coding Agent

This project requires setting up an external application or workflow environment. If you want an autonomous coding agent (like Claude Code) to implement it in your repository:

1. Click here to open a pre-filled GitHub Issue on the repository
2. Submit the issue to trigger your repository’s autonomous builder agent.

Alternatively, you can copy the raw agent prompt instruction below:

Task Context: Build a code project utilizing the THIRI Model Context Protocol (MCP) server. Prompt Instruction: Write a Python Jupyter notebook utilizing bigframes.pandas. The script must load classical chorale dataset schemas from BigQuery, call the THIRI MCP analyze_chord tool in a mapping iteration to populate tension columns, and execute a BigQuery ML logistic regression model to predict next-chord resolutions.