Skip to main content

EasyGLM

Fit insurance pricing GLMs, check model performance and export rating tables from a local browser workbench.

Install and open

Requires Python 3.10–3.14. Run in a terminal:

pip install --upgrade easy_glm
easy-glm-workbench

Your browser opens automatically. Keep the terminal open while you work. Restart the workbench after upgrading.

Build a model

  1. Load data. Open your file or try the French motor claim-frequency or Swedish motorcycle claim-cost example. Supports CSV, Parquet, Excel, Arrow/Feather and SAS files.
  2. Choose variables. Set the target, exposure/weight and predictors. Check for missing data, possible leakage and redundant predictors before fitting.
  3. Explore. See observed rates and exposure by variable.
  4. Fit. Choose a model family, training/holdout split, factor shapes and interactions, then click Fit model.
  5. Review. Check actual versus expected, lift, Gini and variable importance. Compare alternative models.
  6. Adjust. Smooth, cap/floor or edit relativities, then apply your changes. The original fit stays available for comparison.

Supports Poisson, Gamma, Tweedie, Gaussian, binomial and inverse Gaussian GLMs, with regularisation and two-stage interactions.

Model design in EasyGLM

Export and keep your work

Export What you get
Excel Applied rating tables
Scorer (.easyglm) Applied rates for scoring new data
Project JSON Model setup, applied adjustments and named snapshots
HTML report Data summaries, importance, coefficient paths, rating factors and diagnostics
Python script Reproduce the model from its source data

Before closing, save the project JSON and scorer. Reopening a project requires its source data and a refit; fitted runs and session Undo are not saved in project JSON.

Already working in Python?

Open a pandas or Polars dataframe in the workbench:

import easy_glm

easy_glm.launch_workbench(data=df)

Workbench walkthrough · Python examples · Changelog

Why I built this

This started with my wish to port R's aglm to Python and give it a GUI. It's built primarily for my own pricing work and will probably have plenty of bugs, because… vibecode yo. Check the results before using them.

MIT licence.

Release files for easy-glm 0.462

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for easy-glm 0.462
File Size Uploaded
easy_glm-0.462.tar.gz 625.5 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for easy-glm 0.462
File Interpreter ABI Platform
easy_glm-0.462-py3-none-any.whl Python 3 none any Details

Total release size: 1.1 MB

Release files / easy_glm-0.462.tar.gz

Download URL easy_glm-0.462.tar.gz
Size 625.5 kB
Tags Source
SHA-256 checksum
How to use checksums
5bdf6fe153cbac52120b4b6eb4820b0a057fd90cf93dcee3e47615f1d2c16ab9
BLAKE2b-256 checksum
How to use checksums
f4c8b7d297f4fc9a537ab007c222acb26012d4882f8d63df2de41d188a4c87c8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 11, 2026.

Transparency log

Release files / easy_glm-0.462-py3-none-any.whl

Download URL easy_glm-0.462-py3-none-any.whl
Size 448.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
808ab8eefc15a3b114ca212096722aeb69fa3919aad1140e271a1b0b23d75d74
BLAKE2b-256 checksum
How to use checksums
e65cdaad44f561f61d1e6479c709c8c7aba7e597b586352a85d2bbb1e7e4beb4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 11, 2026.

Transparency log

Release history Release notifications | RSS feed

0.471

2 release files

0.470

2 release files

0.464

2 release files

0.463

2 release files

This release

0.462 This release

2 release files

0.461

2 release files

0.460

2 release files

0.452

2 release files

0.451

2 release files

0.4.5

2 release files

0.4.4

2 release files

0.4.3

2 release files

0.4.2

2 release files

0.4.1

2 release files

0.4.0

2 release files

0.3.0

2 release files

0.2.2

2 release files

0.2.0

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page