Week 9: Lab - Using aRules on the Titanic dataset[Name][Date]InstructionsUse the Titanic dataset to explore descriptive statistics, functions, and association rules. Download the titanic dataset titanic.raw.rdata from one of two locations:https://github.com/ethen8181/machine-learning/tree/master/association_rule/Rhttps://sites.google.com/a/rdatamining.com/www/data/titanic.raw.rdata?attredirects=1Note that it is not a cvs file, but rather an RData workspace. So, to load the data (assuming you saved it to the project’s data folder), one would do:load("data/titanic.raw.rdata")You need to look at titanic.raw (the name of the R dataset)t <- titanic.rawNow that you have the datafile, do some descriptive statistics, getting some extra practice using R. Take the Quiz for Step 1 and 2. However your submission MUST include the process of you calculating the following values in Step 1 and 2.# Add your library below.Step 0 - Load the dataUsing the instructions above, load the dataset and save it as t.# Write your code below.Step 1 - Descriptive stats (0.5 point for each answer)Compute the percentage of people that survived.Compute the percentage of people that were children.Compute the percentage of people that were female.Finally, compute the percentage of people that were in first class.# Write your code below.Step 2 - More descriptive stats (0.5 point for each answer)What percentage of children survived? Your answer should be written such as # 13.75% of children survivedWhat percentage of female survived?What percentage of first-class people survived?What percentage of third-class people survived?# Write your code below.Step 3 - Writing a function (0.5 point for each answer)Step 3.1 - Function 1Write a function that returns a new dataframe of people that satisfies the specified criteria of sex, age, class and survived as parameters. I’m giving you the answer for this question:myfunction1 <- function(a,b,c,d){ df1 <- t[t$Class == a,] # filter the data that satisfied the criteria that "Class" = a df2 <- df1[df1$Sex == b,] # filter the data that satisfied the criteria that "Sex" = b df3 <- df2[df2$Age == c,] # filter the data that satisfied the criteria that "Age" = c df4 <- df3[df3$Survived == d,] # filter the data that satisfied the criteria that "Survived" = d return(df4)} # test the function with a sample data myfunction1("1st","Female","Adult","No")# Write your code below.Step 3.2 - Function 2Write a function, using the previous function, that calculates the percentage (who lives, who dies) for a specified (parameters) of class, gender and age considering the entire number of data. The function passes four arguments. Include the following code properly in your function by improvising names of objects.p <- nrow(df)/nrow(t) # calculate the percentage# Write your code below.Step 3.3 - Use the function (male)Use the function to compare age and third-class male survival rates.# Write your code below.People in which category are more likely to survive?[Type your answer here]Step 3.4 - Use the function (female)Use the function to compare age and first-class female survival rates.# Write your code below.People in which category are more likely to survive?[Type your answer here]Step 4 - Use aRules (0.5 point for each answer)Use aRules to calculate some rules (clusters) for the titanic dataset.Visualize the results.Pick the three most interesting and useful rules. Explain these rules using natural language. Answer this in the space provided below.How does this compare to the descriptive analysis we did on the same data set? Think critically. What was possible using one method that was not possible using the other method? Answer this in the space provided below.# Write your code below.Answer part 3 and 4 below.[Type your answers here]

“Martial Arts R Us” (MARU) needs a database. MARU is a martial arts school with hundreds of students. The database must keep track of all the classes that are offered, who is assigned to teach each class, and which students attend each class. Also, it is important to track the progress of each student as they advance. Create a complete Crow’s Foot ERD for these requirements:Students are given a student number when they join the school. The number is stored along with their name, date of birth, and the date they joined the school.All instructors are also students, but clearly not all students are instructors. In addition to the normal student information, for all instructors, the date that they start working as an instructor must be recorded along with their instructor status (compensated or volunteer).An instructor may be assigned to teach any number of classes, but each class has one and only one assigned instructor. Some instructors, especially volunteer instructors, may not be assigned to any class.A class is offered for a specific level at a specific time, day of the week, and location. For example, one class taught on Mondays at 5:00 p.m. in Room 1 is an intermediate-level class. Another class taught on Mondays at 6:00 p.m. in Room 1 is a beginner-level class. A third class taught on Tuesdays at 5:00 p.m. in Room 2 is an advanced-level class.Students may attend any class of the appropriate level during each week, so there is no expectation that any particular student will attend any particular class session. Therefore, the attendance of students at each individual class meeting must be tracked.A student will attend many different class meetings, and each class meeting is normally attended by many students. Some class meetings may not be attended by any students. New students may not have attended any class meetings yet.At any given meeting of a class, instructors other than the assigned instructor may show up to help. Therefore, a given class meeting may have a head instructor and many assistant instructors, but it will always have at least the one instructor who is assigned to that class. For each class meeting, the date of the class and the instructors’ roles (head instructor or assistant instructor) need to be recorded. For example, Mr. Jones is assigned to teach the Monday, 5:00 p.m., intermediate class in Room 1. During a particular meeting of that class, Mr. Jones was the head instructor and Ms. Chen served as an assistant instructor.Each student holds a rank in the martial arts. The rank name, belt color, and rank requirements are stored. Most ranks have numerous rank requirements, but each requirement is associated with only one particular rank. All ranks except white belt have at least one requirement.A given rank may be held by many students. While it is customary to think of a student as having a single rank, it is necessary to track each student’s progress through the ranks. Therefore, every rank that a student attains is kept in the system. New students joining the school are automatically given the rank of white belt. The date that a student is awarded each rank should be kept in the system. All ranks have at least one student who has achieved that rank at some time.You can use either Microsoft Visio or Vertabelo to draw and export the ERD.

Use Titanic dataset from here : https://www.kaggle.com/c/titanic/data perform EDA on various columns. Without using any modeling algorithms, and only using basic methods such as frequency distribution, describe the most important predictors of survival of Titanic passengers, were males or females more likely to survive, were young and rich females more likely to survive than old poor males etc?Submit your responses in a fully "knit" R Markdown file. Important note: Please make sure to describe your thinking and analysis results in words. NO PLAGIARISM.