A data set from the MLC++ machine learning software for modeling customer churn. There are 19 predictors, mostly numeric: state (categorical), account_length, area_code, international_plan (yes/no), voice_mail_plan (yes/no), number_vmail_messages, total_day_minutes, total_day_calls, total_day_charge, total_eve_minutes, total_eve_calls, total_eve_charge, total_night_minutes, total_night_calls, total_night_charge, total_intl_minutes, total_intl_calls, total_intl_charge and number_customer_service_calls.
fires
(Package: gencve) :
Forest Fires in Montesinho Natural Park
The forest fire data were collected during January 2000 to December 2003 for fires in theMontesinho natural park located in the northeast region of Portugal. The response variable of interest was area burned in ha. When the area burned as less than one-tenth of a hectare, the response variable as set to zero. In all there were 517 fires and 247 of them recorded as zero. The region was divided into a 10-by-10 grid with coordinates X and Y running from 1 to 9.
Detroit
(Package: gencve) :
Detroit Homicide Data for 1961-73
For convenience we have labelled the input variables 1 through 11 to be consistent with the notation used in Miller (2002). Only the first 11 variables were used in Miller's analyses. The best fitting subset regression with these 11 variables, uses only 3 inputs and has a residual sum of squares of 6.77 while using forward selection produces a best fit with 3 inputs with residual sum of squares 21.19. Backward selection and stagewise methods produce similar results. It is remarkable that there is such a big difference. Note that the usual forward and backward selection algorithms may fail since the linear regression using 11 variables gives essentially a perfect fit.
● Data Source:
CranContrib
● Keywords: datasets
● Alias: Detroit
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Data to examine the correlation between the level of prostate-specific antigen and a number of clinical measures in men who were about to receive a radical prostatectomy.
meatspec
(Package: gencve) :
Meat Spectrometry to Determine Fat Content
A Tecator Infratec Food and Feed Analyzer working in the wavelength range 850 - 1050 nm by the Near Infrared Transmission (NIT) principle was used to collect data on samples of finely chopped pure meat. 215 samples were measured. For each sample, the fat content was measured along with a 100 channel spectrum of absorbances. Since determining the fat content via analytical chemistry is time consuming we would like to build a model to predict the fat content of new samples using the 100 absorbances which can be measured more easily.
The total age adjusted mortality rate, our response variable, for the years 1959-1961. The data from the U.S. covers 201 Standard Metropolitan Statistical Areas (SMSA).
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