Python Seaborn PairPlot Example <- read.csv(“../input/train.csv”,header=T,na.strings=c(“”)) sapply(,function(x) sum( sapply(, function(x) length(unique(x))) library(Amelia) missmap(, main = “Missing values vs observed”) data <- subset(,select=c(2,3,5,6,7,8,10,12)) data$Age[$Age)] <- mean(data$Age,na.rm=T) is.factor(data$Sex) is.factor(data$Embarked) contrasts(data$Sex) contrasts(data$Embarked) data <- data[!$Embarked),] rownames(data) <- NULL train <- data[1:800,] test <- data[801:889,] model <- glm(Survived ~.,family=binomial(link=’logit’),data=train) summary(model) anova(model, test=”Chisq”) library(pscl) pR2(model) fitted.results <- predict(model,newdata=subset(test,select=c(2,3,4,5,6,7,8)),type=’response’) fitted.results 0.5,1,0) misClasificError <- mean(fitted.results != test$Survived) print(paste(‘Accuracy’,1-misClasificError)) library(ROCR) p <- predict(model, newdata=subset(test,select=c(2,3,4,5,6,7,8)), type=”response”) pr <- prediction(p, test$Survived) prf <- performance(pr, measure = “tpr”, x.measure = “fpr”) plot(prf) auc <- performance(pr, measure = “auc”) auc <- auc@y.values[[1]] auc This script has been released under the Apache 2.0 open source license.

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