Skip to main content

A framework for fast and interactive conducting machine learning experiments on tabular data

Project description

KTS logo

PyPI version Docs CI Codecov CodeFactor

An interactive environment for modular feature engineering, experiment tracking, feature selection and stacking.

Install KTS with pip install kts. Compatible with Python 3.6+.

Modular Feature Engineering

Define features as independent blocks to organize your projects.

Source Code Tracking

Track source code of every feature and experiment to make each of them reproducible.

Parallel Computing and Caching

Compute independent features in parallel. Cache them to avoid repeated computations.

Experiment Tracking

Track your progress with local leaderboards.

Feature Selection

Compute feature importances and select features from any experiment
with experiment.feature_importances() and

Interactivity and Rich Reports

Monitor the progress of everything going on in KTS with our interactive reports.
From model fitting to computing feature importances.

Getting Started

Titanic Tutorial

Start exploring KTS with tutorial based on Titanic dataset. Run notebooks interactively in Binder or just read them in NBViewer.

1. Feature Engineering

nbviewer Binder

2. Modelling

nbviewer Binder

3. Stacking

nbviewer Binder


Check out for a more detailed description of KTS features and interfaces

Inline Docs

Most of our functions and classes have rich docstrings. Read them right in your notebook, without interruption.


MVP of the project was designed and implemented by the team of Mikhail Andronov, Roman Gorb and Nikita Konodyuk under the mentorship of Alexander Avdyushenko during a project practice held by Yandex and Higher School of Economics on 1-14 February 2019 at Educational Center «Sirius».

Project details

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kts-0.4.0.tar.gz (60.7 kB view hashes)

Uploaded source

Built Distribution

kts-0.4.0-py3-none-any.whl (80.5 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page