SemanticScuttle - klotz.me

klotz: feature engineering* + data science*

0 bookmark(s) - Sort by: Date ↓ / Title / - Bookmarks from other users for this tag

1. 5 Useful Python Scripts for Effective Feature Selection

   This article explores five Python scripts designed to streamline and automate the process of feature selection in machine learning projects. Feature selection is crucial for improving model performance, reducing complexity, and identifying the most impactful variables.
   The scripts cover techniques like filtering constant features, eliminating redundant features through correlation analysis, identifying significant features using statistical tests, ranking features with model-based importance scores, and optimizing feature subsets with recursive elimination. Each script is practical, minimal, and provides detailed reports to aid in understanding the selection process.
   These tools are valuable for data scientists looking to systematically evaluate feature importance and build more efficient and accurate models.

   2026-03-31 Tags: python, feature selection, machine learning, data science, feature engineering, statistical analysis, model importance, recursive elimination by klotz
2. Can LLM Embeddings Improve Time Series Forecasting? A Practical Feature Engineering Approach

   This tutorial explores how to use LLM embeddings as features in time series forecasting models. It covers generating embeddings from time series descriptions, preparing data, and evaluating the performance of models with and without LLM embeddings.

   2026-02-28 Tags: time series, forecasting, llm, embeddings, feature engineering, machine learning, natural language processing, transformers, data science, production engineering by klotz
3. 5 Useful Python Scripts for Effective Feature Engineering

   This article covers five Python scripts designed to automate impactful feature engineering tasks, including encoding categorical features, transforming numerical features, generating interactions, extracting datetime features, and selecting features automatically.

   2026-01-17 Tags: feature engineering, python, machine learning, data science, categorical encoding, numerical transformation, feature selection, datetime features by klotz
4. Prompt Engineering for Time-Series Analysis with Large Language Models

   This article explores how prompt engineering can be used to improve time-series analysis with Large Language Models (LLMs), covering core strategies, preprocessing, anomaly detection, and feature engineering. It provides practical prompts and examples for various tasks.

   2025-10-16 Tags: llm, prompt engineering, time series, forecasting, anomaly detection, feature engineering, data science, machine learning, production engineering, observability by klotz
5. Time Series Forecasting: A Practical Guide to Exploratory Data Analysis

   This article provides a comprehensive guide to performing exploratory data analysis on time series data, with a focus on feature engineering.

   2024-05-10 Tags: time series, analysis, data science, exploratory data analysis, feature engineering, python by klotz

Top of the page

First / Previous / Next / Last / Page 1 of 0