Correlation and Regression

path <- "C:/Users/andre/OneDrive/Área de Trabalho/salerno/blogdown/datasets/ncbirths" path <- paste0(path, "/ncbirths.csv") data <- read.csv(path, stringsAsFactors = FALSE) dim(data) ## [1] 1450 15 names(data) ## [1] "ID" "Plural" "Sex" "MomAge" ## [5] "Weeks" "Marital" "RaceMom" "HispMom" ## [9] "Gained" "Smoke" "BirthWeightOz" "BirthWeightGm" ## [13] "Low" "Premie" "MomRace" library(ggplot2) ggplot(data = data, aes(y = BirthWeightOz, x = Weeks)) + geom_point() ## Warning: Removed 1 rows containing missing values (geom_point). # Boxplot of weight vs.

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Diagnosing breast cancer with the kNN algorithm

1 - Introduction Could the Machine Learning Algorithms detect beforehand any abnormal cell process? We know that this clinical battle is not so easy and there are a lot of people envolved in this process trying to identify a clear path to the cure. In complement to the decision human process, coult the technology decrease the subjective bias inherently in the process and improve our decisions? We absolutely know that the human being process is limited when compared to high capacity of the computers.

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