Skip to main content

A powerful and user-friendly machine learning toolkit for data science and ML professionals to accelerate their workflow

Project description

MLXpress is a comprehensive and intuitive machine learning toolkit that simplifies the development and deployment of machine learning models. It offers a wide range of essential functionalities for data science and ML professionals, enabling a streamlined and efficient workflow. With MLXpress, you can easily implement commonly used data science and ML functionality, making complex tasks simpler and more accessible.

Key Features:

  • Data Preprocessing: Handle missing values, perform feature scaling, and handle categorical variables.
  • Feature Selection: Select the most relevant features using statistical tests and evaluation metrics.
  • Model Evaluation: Evaluate model performance using various metrics like accuracy, precision, recall, and F1 score.
  • Cross-Validation: Perform cross-validation to assess model generalization and avoid overfitting.
  • Clustering: Apply clustering algorithms for unsupervised learning tasks.
  • Classification: Build and evaluate classification models using popular algorithms like SVM, Random Forest, and Logistic Regression.
  • Regression: Perform regression analysis using linear regression, decision trees, and ensemble methods.
  • Visualization: Visualize data distributions, feature relationships, and model performance metrics.
  • Hypothesis Testing: Perform hypothesis testing to assess statistical significance.

With MLXpress, you can accelerate your machine learning projects, save time on repetitive tasks, and focus on developing accurate and robust models. It provides an intuitive interface, extensive documentation, and comprehensive support, making it suitable for both beginners and experienced practitioners.

Project details


Download files

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

Source Distribution

MLXpress-0.1.9.0.tar.gz (18.6 kB view details)

Uploaded Source

Built Distribution

MLXpress-0.1.9.0-py3-none-any.whl (31.4 kB view details)

Uploaded Python 3

File details

Details for the file MLXpress-0.1.9.0.tar.gz.

File metadata

  • Download URL: MLXpress-0.1.9.0.tar.gz
  • Upload date:
  • Size: 18.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.12.0

File hashes

Hashes for MLXpress-0.1.9.0.tar.gz
Algorithm Hash digest
SHA256 c09408811b4d3657d1a1d0de6ac43a6d54fda107fa38bb6256d6c80493a7a260
MD5 e871a6ba6da028030fb5440364194d7b
BLAKE2b-256 69ec36434548b709b4f0148b16d9f05e0045d76ad815b4695235dd677fc33bd8

See more details on using hashes here.

File details

Details for the file MLXpress-0.1.9.0-py3-none-any.whl.

File metadata

  • Download URL: MLXpress-0.1.9.0-py3-none-any.whl
  • Upload date:
  • Size: 31.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.12.0

File hashes

Hashes for MLXpress-0.1.9.0-py3-none-any.whl
Algorithm Hash digest
SHA256 d4095321846c4f801e04d092b873016aeaa135ecd5697bc9a0157329de2c6b8b
MD5 51cb30b655491038362f145d92f4895d
BLAKE2b-256 fd2219a8971c4ff5e920749067d7ff116cc417dd7cde810a358bf1405e569a75

See more details on using hashes here.

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