In this you will be using the churn data: churn_data.txt Read data into a data frame using the function read.csv() with the following options: header=T, stringsAsFactors=F Assume that you saved the file churn_data.txt in C:/Datasets folder. Then you can read file into a data frame as follows: file=”C:/Datasets/churn_data.txt” churnData=read.csv(file, stringsAsFactors = FALSE,header = TRUE) A) Print the name of the columns. Hint: colnames() function. B) Print the number of rows and columns Hint: dim() C)  Count the number calls per state. Hint: table() function. D) Find mean, median,standard deviation, and variance of nightly charges, the column Night.Charge in the data. The R functions to be used are mean(), median(), sd(), var(). E) Find maximum and minimum values of international charges (Intl.Charge), customer service calls (CustServ.Calls), and daily charges(Day.Charge). F) Use summary() function to print information about the distribution of the following features: “Eve.Charge”     “Night.Mins”     “Night.Calls”    “Night.Charge”   “Intl.Mins”      “Intl.Calls” What are the min and max values printed by the summary() function for these features? Check textbook page 34 for a sample. G) Use unique() function to print the distinct values of the following columns: State, Area.Code, and Churn. H)  Extract the subset of  data for the churned customers(i.e., Churn=True). How many rows are in the subset? Hint: Use subset() function. Check lecture notes and textbook for samples. I)  Extract the subset of data for customers that made more than 3 customer service calls(CustServ.Calls). How many rows are in the subset? J) Extract the subset of churned customers with no international plan (Int.l,Plan) and no voice mail plan (VMail.Plan). How many rows are in the subset? K) Extract the data for customers from California (i.e., State is CA)  who did not churn but made more than 2 customer service calls. L) What is the mean of customer service calls for the customers that did not churn (i.e., Churn=False)? question2 related to above In this ,we will explore the churn data using graphics and visualization. One of the primary reasons for performing exploratory data analysis (EDA) is to investigate the variables, examine the distributions of the categorical variables, look at the histograms of the numeric variables, and explore the relationships among sets of variables. Although we are not going to develop any models for this project, in a real-world project our task is to identify patterns in the data that will help to reduce the proportion of churners. We will use the same data set we had in Week 2 assignment: Data file: churn_data.txt All graphics in this assignment have to be plotted using ggplot2 library. So, you need to install ggplot2 library for graphs: install.packages(“ggplot2”) Before using any methods from the libraries, you need to load these libraries into the R code using library(ggplot2) Here is how you can read data into a data frame named churnData: churnData <- read.csv(filePath, stringsAsFactors = FALSE,header = TRUE) where filePath is the location of the churn_data.txt file. For example, if you saved file in C:/tmp, then you should use C:/tmp/churn_data.txt The variables in the file churn_data.txt are State : Categorical, for the 50 states and the District of Columbia. Account length : Integer-valued, how long account has been active. Area code : Categorical Phone number : Essentially a surrogate for customer ID. International plan : Dichotomous categorical, yes or no. Voice mail plan : Dichotomous categorical, yes or no. Number of voice mail messages : Integer-valued. Total day minutes : Continuous, minutes customer used service during the day. Total day calls : Integer-valued. Total day charge : Continuous, perhaps based on above two variables. Total eve minutes : Continuous, minutes customer used service during the evening. Total eve calls : Integer-valued. Total eve charge : Continuous, perhaps based on...

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