Feature Engineering And Model Improvement Suggestions

via “feature engineering agent with automated transformation generation”

An AI-powered data science team of agents to help you perform common data science tasks 10X faster.

Unique: Automates feature engineering by generating transformation code from natural language descriptions, integrating with scikit-learn transformers. Unlike manual feature engineering or AutoML systems, the agent generates interpretable, inspectable code that can be modified and version-controlled.

vs others: Provides automated feature engineering vs manual coding (faster, more consistent) and vs black-box AutoML (generates interpretable code), while supporting both numeric and categorical features.

A repository of useful data science prompts for ChatGPT.

Unique: Provides dedicated prompts for feature engineering ideation as a distinct workflow stage with role-assumption ('act as ML engineer') and guidance on suggesting features that align with model objectives. Treats feature engineering as a systematic, prompt-driven process rather than ad-hoc exploration.

vs others: More structured than manual brainstorming because prompts guide ChatGPT to consider multiple feature engineering techniques (domain-specific features, statistical transformations, interaction terms) and provide rationale for suggestions.

via “feature engineering and selection guidance with domain-specific examples”

robust introduction to the subject and also the foundation for a Data Analyst “nanodegree” certification sponsored by Facebook and MongoDB.

via “feature-engineering-guidance”

via “automated-feature-engineering”

via “automated-feature-engineering”

via “automated feature engineering”

via “automated-machine-learning-model-training”

via “prompt engineering technique examples”

via “prompt optimization and engineering”

via “open-source model customization”

via “feature engineering and data preparation”

via “model-hardening-guidance”

via “quality feedback collection and incorporation”