Data ScientistFeature EngineeringAdvancedSingle prompt

Embedding Features from Text AI Prompt

This prompt converts text columns into usable numerical representations at several levels of sophistication. It is appropriate when text may contain sentiment, topic, style, or semantic meaning that can improve predictive performance. It combines lightweight handcrafted features with sparse and dense text representations.

Prompt text
Generate numeric features from the text columns in this dataset for use in a machine learning model.

For each text column:
1. Basic statistical features: character count, word count, sentence count, average word length, punctuation count
2. Lexical features: unique word ratio (vocabulary richness), stopword ratio, uppercase ratio
3. Sentiment features: positive score, negative score, neutral score, compound score using VADER
4. TF-IDF features: top 50 unigrams and top 20 bigrams (sparse matrix)
5. Dense embedding: use sentence-transformers (all-MiniLM-L6-v2) to produce a 384-dimensional embedding, then reduce to 10 dimensions using UMAP or PCA

Return code for each feature group as a modular function.
Note which features are suitable for tree models vs neural networks.

When to use this prompt

Use case 01

The dataset includes review text, descriptions, comments, tickets, or other free text.

Use case 02

You want both simple text statistics and modern embeddings in one plan.

Use case 03

You need modular code that can be reused across different text columns.

Use case 04

You want guidance on which text features fit tree models versus neural models.

What the AI should return

Modular Python functions for each text feature family, a clear separation between basic, sparse, and dense features, and notes on model compatibility for each feature type.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Feature Engineering.

Frequently asked questions

What does the Embedding Features from Text prompt do?+

It gives you a structured feature engineering starting point for data scientist work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for data scientist workflows and marked as advanced, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Embedding Features from Text is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Date Feature Extraction, Feature Ideas Generator, Feature Selection.