Product 001

UNEEFLOW

A guided Windows desktop app for building, evaluating and testing machine-learning models with tabular data, without requiring a code-heavy workflow.

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UNEEFLOW

Current release

A guided desktop workflow for practical machine learning.

Status
Released for Windows
Version
Version 1
Platform
Windows 10 or Windows 11, 64-bit
Published
13 January 2026
Availability
Free, with name-your-own-price support on itch.io

8 GB or more RAM recommended

What it does

From imported data to a model you can test.

UNEEFLOW is designed for offline-first use, so imported data stays on the user's device.

  1. 01Import CSV, Excel or JSON datasets
  2. 02Generate an automated YData Profiling report
  3. 03Select target and input variables through a guided workflow
  4. 04Handle missing values and encode categorical data
  5. 05Train classification or regression models
  6. 06Review model metrics with optional LLM-generated interpretation
  7. 07Export trained models as .pkl files
  8. 08Test a model through an automatically generated prediction interface

Inside UNEEFLOW

A closer look at the current Windows release.

UNEEFLOW main menu with options to build a model or test a model
Start

Choose whether to begin a model-building workflow or test an existing model.

UNEEFLOW dataset preview showing tabular rows, columns and missing-value totals
Dataset preview

Review the dataset before configuring the model workflow.

UNEEFLOW data profile report overview with dataset statistics and variable types
Automated profiling

Generate a YData Profiling report for an automated view of dataset quality.

UNEEFLOW Step 2 target-variable selection with guidance from UneePhi
Step 2 · Target

Select the variable that the model will learn to predict.

UNEEFLOW Step 3 input-variable selection with dataset columns selected
Step 3 · Inputs

Choose the dataset columns that will be used as model inputs.

UNEEFLOW missing-value screen listing affected columns and handling choices
Step 4 · Missing values

Choose how missing values should be handled for each affected column.

UNEEFLOW encoding screen with UneePhi guidance about categorical encoding methods
Step 5 · Encoding

Configure categorical encoding with in-app guidance when it is needed.

UNEEFLOW Step 6 training screen with train-test split and model selection controls
Step 6 · Training

Set the train-test split and select a classification or regression model.

UNEEFLOW model evaluation screen with performance metrics and UneePhi interpretation
Step 7 · Evaluation

Read core evaluation metrics alongside an optional LLM-generated interpretation.

UNEEFLOW Step 8 export screen showing the contents saved with a trained model
Step 8 · Export

Review the model package contents before exporting the trained model as a .pkl file.

UNEEFLOW generated prediction interface filled with sample input values and a result
Instant prediction

Test an exported model through a generated prediction interface.

Who it is for

A practical starting point for working with tabular data.

  • Students and beginners learning practical machine learning
  • Small business owners who want to work with their data
  • People who want to train and test models without writing lots of code
  • Users who prefer an offline workflow for privacy or convenience

By UNEEVERSE

Product 001 in a growing product universe.

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