Data Analytics / Machine Learning

Implement both simple and complex analytics and machine learning models:

Perform basic data preparation – filter, splits, append, merge, duplicate; and other text, numerical, and logical functions.

Text mining capabilities like text extraction, hashing and text grouping.

Feature Extraction – word to vector, Inverse document frequency, etc.

Feature Transformation – binarize, scale, normalize, etc.

Feature Selection – slice vector, chi-sq select, etc.

Develop clustering models – K-means, Bisecting K-means, Guassian mixture, Latent Dichrichlet Allocation (LDA), and others.

Develop regression models – linear, decision tree, survival, isotonic.

Develop classification models – logistic regression, random forest, gradient busted tree (GBT), Naive Bayes, and others.

Perform model tuning and scoring.

Build pipelines to execute a group of algorithms simultaneously.

Configure multiple sandboxes View and modify machine generated source code.

Monitor real-time model execution.