Santander Customer Satisfaction
Santander Bank is asking Kagglers to help them identify dissatisfied customers early in their relationship. Doing so would allow Santander to take proactive steps to improve a customer’s happiness before it’s too late.
In this competition, you’ll work with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.
Library(randomForest)
Library(readr)
test <- read.csv("C:/Users/Liew Keong Han/Desktop/Machine Learning/test – santander.csv")
train <- read.csv("C:/Users/Liew Keong Han/Desktop/Machine Learning/train – santander.csv")
numTrain = 76020
numTrees = 80
rows <- sample(1:nrow(train), numTrain)
TARGET <- as.factor(train[rows,371])
train1 <- train[rows, -371]
rf <- randomForest(train1, TARGET, xtest = test, ntree=numTrees, keep.forest = TRUE, importance = TRUE)
prob <- rf$test$votes
head(prob)
predictions <- data.frame(ID =test$ID, TARGET=prob[,2])
write_csv(predictions, “rf_76020_80-santander.csv”)
Digit Recognizer
The goal in this competition is to take an image of a handwritten single digit, and determine what that digit is.
test <- read.csv("C:/Users/Liew Keong Han/Desktop/Machine Learning/test – Digit Recognizer.csv")
train <- read.csv("C:/Users/Liew Keong Han/Desktop/Machine Learning/train – Digit Recognizer.csv")
numTrain <- 42000
numTrees <- 80
rows <- sample(1:nrow(train),numTrain)
labels <- as.factor(train[rows, 1])
train <- train[rows, -1]
rf <- randomForest(train, labels, xtest = test , ntree=numTrees)
predictions <- data.frame(ImageId =1:nrow(test), Label=levels(labels)[rf$test$predicted])
head(predictions)
write_csv(predictions, “rf_42000_80-digit_recognizer.csv”)