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-0.1.8.3.tar.gz (7.2 kB view details)

Uploaded Source

Built Distribution

MLXpress-0.1.8.3-py3-none-any.whl (17.8 kB view details)

Uploaded Python 3

File details

Details for the file MLXpress-0.1.8.3.tar.gz.

File metadata

  • Download URL: MLXpress-0.1.8.3.tar.gz
  • Upload date:
  • Size: 7.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.12.0

File hashes

Hashes for MLXpress-0.1.8.3.tar.gz
Algorithm Hash digest
SHA256 652c2d76064fed6138481917a56ffc00c640373f4e192421a4bdf5d82b2d1727
MD5 e2f8b722831fc71ff8557a3f175affd9
BLAKE2b-256 f4499043633d46bd7fdf8b3773b90eb3cf2dd6c1b8cab152547a2c0b41a9324c

See more details on using hashes here.

File details

Details for the file MLXpress-0.1.8.3-py3-none-any.whl.

File metadata

  • Download URL: MLXpress-0.1.8.3-py3-none-any.whl
  • Upload date:
  • Size: 17.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.12.0

File hashes

Hashes for MLXpress-0.1.8.3-py3-none-any.whl
Algorithm Hash digest
SHA256 6ff8b8bb9efcfaed6aadaad2eaab8e7a1410ac4c5a6706cf9b3949df9d0480c2
MD5 bb39c841583c4140abe0aaa29fa686a6
BLAKE2b-256 58bd473af2afb05b37e3585a6b313d61b2a1a279e29beb0534385a374089d9b4

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