data binning - Learn Data Science with Travis

Data Preprocessing with Python Pandas — Part 5 Binning

Towards Data Science

Data binning (or bucketing) groups data in bins (or buckets), in the sense that it replaces values contained into a small interval with a single representative value for that interval. Sometimes…

Data Binning with Pandas Cut or Qcut Method

Towards Data Science

Binning the data can be a very useful strategy while dealing with numeric data to understand certain trends. Sometimes, we may need an age range, not the exact age, a profit margin not profit, a…

Is Binning in Data Analysis a Good Idea?

Python in Plain English

Data analysis is a very important part of the data scientist’s job. Because I am not actually employed by a company as a data scientist, I must acquire my skills by taking courses or entering…

All Pandas qcut() you should know for binning numerical data based on sample quantiles

Towards Data Science

Numerical data is common in data analysis. Often you have numerical data that is continuous, very large scales, or highly skewed. Sometimes, it can be easier to bin those data into discrete…

A Beginner’s Guide to Converting Numerical Data to Categorical: Binning and Binarization

Towards AI

That’s exactly what converting numerical data into categorical data can do for you! In today’s post, we’ll dive into two game-changing techniques: Binning and Binarization , perfect for scenarios like...

The Role of Data Blending and Data Munging in the Data Science Process

Python in Plain English

Data science is a multidisciplinary field that utilizes scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. The lifecycle of...

Data Scientists: STOP Randomly Binning Histograms

Analytics Vidhya

Histograms are a crucial part of Exploratory Data Analysis. But we often abuse them by randomly choosing a number of bins. Let’s use math.

Databaiting

Towards Data Science

Databaiting: to entice someone to submit their data by eliciting an emotional response. Is it a useful description?

Generating binary data by specifying the relative risk, with simulations

R-bloggers

The most traditional approach for analyzing binary outcome data is logistic regression, where the estimated parameters are interpreted as log odds ratios or, if exponentiated, as odds ratios (ORs). No...

Data Mining

Codecademy

Data mining is the process of applying algorithms to search for patterns within collections of data. Fundamentally, data mining is the deployment of an automated process for analyzing large amounts of...

Binary Data Services

The Python Standard Library

Binary Data Services The modules described in this chapter provide some basic services operations for manipulation of binary data. Other operations on binary data, specifically in relation to file fo...

Data Preprocessing

Towards Data Science

Data Preprocessing the data before use is an important task in the virtual realm. It is a data mining technique that transforms raw data into understandable, useful and efficient format.