- Published on
Data transformation is key to effective machine learning. This post explores essential techniques in dealing with numerical data: normalization (linear, Z-score, log, clipping) and binning along with their pros and cons.
Feature-engineering
All Posts
- computer-science (10)
- python (9)
- machine-learning (5)
- project (3)
- c (2)
- dynamic-programming (2)
- text-classification (2)
- data-processing (2)
- data-science (2)
- docker (2)
- package (2)
- mutability (1)
- variables (1)
- data-types (1)
- bioinformatics (1)
- bugs (1)
- linear-alegbra (1)
- probability (1)
- naive-bayes (1)
- rasa (1)
- chatbot (1)
- nlp (1)
- encoding (1)
- software-engineering (1)
- angular (1)
- golang (1)
- mongodb (1)
- gin (1)
- agile (1)
- github (1)
- keras (1)
- open-source (1)
- build (1)
- deep-learning (1)
- linguistics (1)
- classification (1)
- cnn (1)
- neural-networks (1)
- computer-vision (1)
- demo (1)
- generative-ai (1)
- elo (1)
- games (1)
- cicd (1)
feature-engineering (1)
- normalization (1)
- cryptography (1)
- security (1)
- tool (1)
- deployment (1)
- aws (1)
- devops (1)
- fastapi (1)