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| # Load the raw training data and replace missing values with NA | |
| training.data.raw <- read.csv('train.csv',header=T,na.strings=c("")) | |
| # Output the number of missing values for each column | |
| sapply(training.data.raw,function(x) sum(is.na(x))) | |
| # Quick check for how many different values for each feature | |
| sapply(training.data.raw, function(x) length(unique(x))) | |
| # A visual way to check for missing data | |
| library(Amelia) | |
| missmap(training.data.raw, main = "Missing values vs observed") | |
| # Subsetting the data | |
| data <- subset(training.data.raw,select=c(2,3,5,6,7,8,10,12)) | |
| # Substitute the missing values with the average value | |
| data$Age[is.na(data$Age)] <- mean(data$Age,na.rm=T) | |
| # R should automatically code Embarked as a factor(). A factor is R's way of dealing with | |
| # categorical variables | |
| is.factor(data$Sex) # Returns TRUE | |
| is.factor(data$Embarked) # Returns TRUE | |
| # Check categorical variables encoding for better understanding of the fitted model | |
| contrasts(data$Sex) | |
| contrasts(data$Embarked) | |
| # Remove rows (Embarked) with NAs | |
| data <- data[!is.na(data$Embarked),] | |
| rownames(data) <- NULL | |
| # Train test splitting | |
| train <- data[1:800,] | |
| test <- data[801:889,] | |
| # Model fitting | |
| model <- glm(Survived ~.,family=binomial(link='logit'),data=train) | |
| summary(model) | |
| # Analysis of deviance | |
| anova(model,test="Chisq") | |
| # McFadden R^2 | |
| library(pscl) | |
| pR2(model) | |
| #------------------------------------------------------------------------------- | |
| # MEASURING THE PREDICTIVE ABILITY OF THE MODEL | |
| # If prob > 0.5 then 1, else 0. Threshold can be set for better results | |
| fitted.results <- predict(model,newdata=subset(test,select=c(2,3,4,5,6,7,8)),type='response') | |
| fitted.results <- ifelse(fitted.results > 0.5,1,0) | |
| misClasificError <- mean(fitted.results != test$Survived) | |
| print(paste('Accuracy',1-misClasificError)) | |
| # Confusion matrix | |
| library(caret) | |
| confusionMatrix(data=fitted.results, reference=test$Survived) | |
| library(ROCR) | |
| # ROC and AUC | |
| p <- predict(model, newdata=subset(test,select=c(2,3,4,5,6,7,8)), type="response") | |
| pr <- prediction(p, test$Survived) | |
| # TPR = sensitivity, FPR=specificity | |
| prf <- performance(pr, measure = "tpr", x.measure = "fpr") | |
| plot(prf) | |
| auc <- performance(pr, measure = "auc") | |
| auc <- [email protected][[1]] | |
| auc |
sir,
i got the same accuracy as you did but
when i write code of confusion matrix it shows the following error in r
confusionMatrix(data=fitted.results, reference=test$Survived)
Error in confusionMatrix(data = fitted.results, reference = test$Survived) :
unused arguments (data = fitted.results, reference = test$Survived)
kindly reply
Hi, i managed to get the error corrected by putting it in a "table": see below
confusionMatrix(table(data=fitted.results, reference=test$Survived))
sir,
i got the same accuracy as you did but
when i write code of confusion matrix it shows the following error in rconfusionMatrix(data=fitted.results, reference=test$Survived)
Error in confusionMatrix(data = fitted.results, reference = test$Survived) :
unused arguments (data = fitted.results, reference = test$Survived)kindly reply
Hi, i managed to get the error corrected by putting it in a "table": see below
confusionMatrix(table(data=fitted.results, reference=test$Survived))
yes, it is working thanks :)
Pls help when i run the confusionMatrix, using table()) error msg i got was all arguments must have the same length. Pls help advise whats that problem
Fix for the error (data and reference should be factors with the same levels.)in
confusionMatrix(data=fitted.results, reference=test$Survived)do:
install.packages("e1071")
confusionMatrix(data=as.factor(fitted.results), reference=as.factor(test$Survived))
thanks