# Using below library for creating Decision Tree
library("party")

# 1. Explain the concept of decision tree?
# Answer. A decision tree is an algorithm that builds a tree like graph to illustrate the possible outcomes of a decision, that classifies the datapoints. To build the tree, the algorithm first finds the variable that does the best job of separating the data into two groups. Then, it repeats the above step with the other variables. This results in a tree graph, where each split represents a decision. The algorithm chooses the splits such that the maximum number of observations are classified correctly.

# Reading the DataSet:
Bankruptcy<-read.csv("path/qzM6LNJsMDLtLbZMZkwz.csv")

# 1- Describe the data (use descriptive statistics methods you learned so far)
# Let's look at the Structure of the dataset
str(Bankruptcy)
## 'data.frame': 250 obs. of 7 variables:
## $ IR    : Factor w/ 3 levels "A","N","P": 3 2 1 3 2 1 3 3 3 3 ...
## $ MR    : Factor w/ 3 levels "A","N","P": 3 2 1 3 2 1 3 3 3 3 ...
## $ FF    : Factor w/ 3 levels "A","N","P": 1 1 1 3 3 3 1 3 1 1 ...
## $ CR    : Factor w/ 3 levels "A","N","P": 1 1 1 3 3 3 3 1 3 1 ...
## $ CO    : Factor w/ 3 levels "A","N","P": 1 1 1 3 3 3 3 1 1 3 ...
## $ OP    : Factor w/ 3 levels "A","N","P": 3 2 1 3 2 1 3 3 3 3 ...
## $ Bankruptcy: Factor w/ 2 levels "B","NB": 2 2 2 2 2 2 2 2 2 2 ...
# From the above output we can see that we have 250 observations and 7 variable that are all factors

# Now we'll look at the barplot frequency of each column

columns<-names(Bankruptcy)
for(i in columns){
barplot(table(Bankruptcy[i]),main=paste0("Barplot for ",i))
}

6. Consider the probability distribution shown here. x -4 -3 -2...