Initial commit

bmarketing.R

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+library(tidyverse)
+
+#################Loading data into the environment#################
+bmarketing <- read.csv2("bank.csv")
+
+#Lets look at dataset and generate initial understanding about the column types
+str(bmarketing)
+summary(bmarketing)
+
+# A quick check:
+# If newdata has same number of observation that implies no NA value present
+# is.na(bmarketing)
+newdata <- na.omit(bmarketing)
+nrow(newdata)==nrow(bmarketing)
+
+#A deep check for a particular column let say age
+if(length(which(is.na(bmarketing$y)==TRUE)>0)){
+  print("Missing Value found in the specified column")
+} else{
+  print("All okay: No Missing Value found in the specified column")
+}
+
+# Let's find the range of individual variables
+summary(bmarketing)
+
+## ------------------------------------------------------------------------
+bmarketing %>% 
+  ggplot() + geom_histogram(aes(age), bins = 30) + 
+  geom_vline(aes(xintercept= median(age)), color = "red")
+
+# TODO: do boxplots for each data
+# boxplot(duration~y,data=bmarketing_sub,col="red")
+
+#################Decision Tree#################
+library(rpart)
+library(rpart.plot)
+library(caret)
+
+dt_model<- rpart(y ~ ., data = bmarketing)
+rpart.plot(dt_model)
+summary(dt_model)
+
+#################Testing Decision Tree #################
+predictions <- predict(dt_model, bmarketing, type = "class")
+
+## Compute the accuracy
+mean(bmarketing$y == predictions)
+
+# Lets look at the confusion matrix
+table(predictions, bmarketing$y)