Logistic Regression With R

Raghavan Madabusi runs through a sample logistic regression:

Input Variables: These variables are called as predictors or independent variables.

  • Customer Demographics (Gender and Senior citizenship)
  • Billing Information (Monthly and Annual charges, Payment method)
  • Product Services (Multiple line, Online security, Streaming TV, Streaming Movies, and so on)
  • Customer relationship variables (Tenure and Contract period)

Output Variables: These variables are called as response or dependent variables. Since the output variable (Churn value) takes the binary form as “0” or “1”, it will be categorized under classification problem in the supervised machine learning.

One of the interesting things in this post was the use of missmap, which is part of Amelia.

Related Posts

dplyr Mutate Quirks

John Mount explains a quirk in dplyr’s mutate function: It is hard for experts to understand how frustrating the above is to a new R user or to a part time R user. It feels like any variation on the original code causes it to fail. None of the rules they have been taught anticipate this, or tell them how […]

Read More

Regular Expression Cheat Sheets

Mara Averick shows off a collection of regular expression guides: There are helpful string-related R packages 📦, stringr (which is built on top of the more comprehensive stringi package) comes to mind. But, at some point in your computing life, you’re gonna need to get down with regular expressions. And so, here’s a collection of some of the Regex-related […]

Read More

Categories

April 2017
MTWTFSS
« Mar May »
 12
3456789
10111213141516
17181920212223
24252627282930