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1
Intro
2
Agenda
3
Workshop Overview
4
Data Preparation
5
Typical Data Problems
6
Example
7
We are dead values
8
Identifying missing values
9
Alternative approaches
10
Missing values
11
Pragmatic solution
12
Novel categorical levels
13
Training data
14
Problem statement
15
Capital categorical variables
16
Btree solution
17
Categorical variables
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Using impact
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Equity
20
Indicator Variables
21
Example Package
22
Treatment Path
23
Treatment Plan
24
Model Quality
25
Variable Treatment
26
Bad Days
Description:
Explore data preparation techniques for analysis in R through this comprehensive conference talk from ODSC West 2015. Learn how to detect and fix common data quality issues, automate routine steps, and improve the success rate of data science projects. Follow along with interactive demonstrations using R, RStudio, and essential packages as John Mount and Nina Zumel guide you through the fundamentals of data preparation. Discover practical solutions for handling missing values, novel categorical levels, and variable treatment. Gain insights into creating effective treatment plans and improving model quality. Access downloadable materials to practice and reinforce your learning at your own pace.

Prepping Data for Analysis in R

Open Data Science
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