Question

In: Computer Science

Task 1 Please import the “admit.csv” into Rstudio. In this dataset, we know the GRE score,...

Task 1

Please import the “admit.csv” into Rstudio. In this dataset, we know the GRE score, the GPA, and the rankof 400 applicants for a graduate program. We also know if each of the candidates is admitted. In the admit column, 1 stands for “admitted”, and 0 stands for “rejected”. Please answer the following questions and include the codes.

1. import the dataset and call it "mydata". Then check the structure of the data

2. convert the data type of the admit and the rank column from int to factors

3. randomly select 80% of the dataset as training set and the rest as the testing set

4. train a decision tree model, using admit as the category, and gre, gpa, and rank as predictors. Then plot the tree

5. Please answer the question: if a candidate has a GPA of 3.7, and rank of 4, does this candidate have a higher chance to be admitted or to be rejected? Please note that when you only have two categories, the darker proportion stands for the proportion for 1 in the end node of the tree plot

6. Please calculate the accuracy of your decision tree model

admit,gre,gpa,rank

0,380,3.61,3

1,660,3.67,3

1,800,4,1

1,640,3.19,4

0,520,2.93,4

1,760,3,2

1,560,2.98,1

0,400,3.08,2

1,540,3.39,3

0,700,3.92,2

0,800,4,4

0,440,3.22,1

1,760,4,1

0,700,3.08,2

1,700,4,1

0,480,3.44,3

0,780,3.87,4

0,360,2.56,3

0,800,3.75,2

1,540,3.81,1

0,500,3.17,3

1,660,3.63,2

0,600,2.82,4

0,680,3.19,4

1,760,3.35,2

1,800,3.66,1

1,620,3.61,1

1,520,3.74,4

1,780,3.22,2

0,520,3.29,1

0,540,3.78,4

0,760,3.35,3

0,600,3.4,3

1,800,4,3

0,360,3.14,1

0,400,3.05,2

0,580,3.25,1

0,520,2.9,3

1,500,3.13,2

1,520,2.68,3

0,560,2.42,2

1,580,3.32,2

1,600,3.15,2

0,500,3.31,3

0,700,2.94,2

1,460,3.45,3

1,580,3.46,2

0,500,2.97,4

0,440,2.48,4

0,400,3.35,3

0,640,3.86,3

0,440,3.13,4

0,740,3.37,4

1,680,3.27,2

0,660,3.34,3

1,740,4,3

0,560,3.19,3

0,380,2.94,3

0,400,3.65,2

0,600,2.82,4

1,620,3.18,2

0,560,3.32,4

0,640,3.67,3

1,680,3.85,3

0,580,4,3

0,600,3.59,2

0,740,3.62,4

0,620,3.3,1

0,580,3.69,1

0,800,3.73,1

0,640,4,3

0,300,2.92,4

0,480,3.39,4

0,580,4,2

0,720,3.45,4

0,720,4,3

0,560,3.36,3

1,800,4,3

0,540,3.12,1

1,620,4,1

0,700,2.9,4

0,620,3.07,2

0,500,2.71,2

0,380,2.91,4

1,500,3.6,3

0,520,2.98,2

0,600,3.32,2

0,600,3.48,2

0,700,3.28,1

1,660,4,2

0,700,3.83,2

1,720,3.64,1

0,800,3.9,2

0,580,2.93,2

1,660,3.44,2

0,660,3.33,2

0,640,3.52,4

0,480,3.57,2

0,700,2.88,2

0,400,3.31,3

0,340,3.15,3

0,580,3.57,3

0,380,3.33,4

0,540,3.94,3

1,660,3.95,2

1,740,2.97,2

1,700,3.56,1

0,480,3.13,2

0,400,2.93,3

0,480,3.45,2

0,680,3.08,4

0,420,3.41,4

0,360,3,3

0,600,3.22,1

0,720,3.84,3

0,620,3.99,3

1,440,3.45,2

0,700,3.72,2

1,800,3.7,1

0,340,2.92,3

1,520,3.74,2

1,480,2.67,2

0,520,2.85,3

0,500,2.98,3

0,720,3.88,3

0,540,3.38,4

1,600,3.54,1

0,740,3.74,4

0,540,3.19,2

0,460,3.15,4

1,620,3.17,2

0,640,2.79,2

0,580,3.4,2

0,500,3.08,3

0,560,2.95,2

0,500,3.57,3

0,560,3.33,4

0,700,4,3

0,620,3.4,2

1,600,3.58,1

0,640,3.93,2

1,700,3.52,4

0,620,3.94,4

0,580,3.4,3

0,580,3.4,4

0,380,3.43,3

0,480,3.4,2

0,560,2.71,3

1,480,2.91,1

0,740,3.31,1

1,800,3.74,1

0,400,3.38,2

1,640,3.94,2

0,580,3.46,3

0,620,3.69,3

1,580,2.86,4

0,560,2.52,2

1,480,3.58,1

0,660,3.49,2

0,700,3.82,3

0,600,3.13,2

0,640,3.5,2

1,700,3.56,2

0,520,2.73,2

0,580,3.3,2

0,700,4,1

0,440,3.24,4

0,720,3.77,3

0,500,4,3

0,600,3.62,3

0,400,3.51,3

0,540,2.81,3

0,680,3.48,3

1,800,3.43,2

0,500,3.53,4

1,620,3.37,2

0,520,2.62,2

1,620,3.23,3

0,620,3.33,3

0,300,3.01,3

0,620,3.78,3

0,500,3.88,4

0,700,4,2

1,540,3.84,2

0,500,2.79,4

0,800,3.6,2

0,560,3.61,3

0,580,2.88,2

0,560,3.07,2

0,500,3.35,2

1,640,2.94,2

0,800,3.54,3

0,640,3.76,3

0,380,3.59,4

1,600,3.47,2

0,560,3.59,2

0,660,3.07,3

1,400,3.23,4

0,600,3.63,3

0,580,3.77,4

0,800,3.31,3

1,580,3.2,2

1,700,4,1

0,420,3.92,4

1,600,3.89,1

1,780,3.8,3

0,740,3.54,1

1,640,3.63,1

0,540,3.16,3

0,580,3.5,2

0,740,3.34,4

0,580,3.02,2

0,460,2.87,2

0,640,3.38,3

1,600,3.56,2

1,660,2.91,3

0,340,2.9,1

1,460,3.64,1

0,460,2.98,1

1,560,3.59,2

0,540,3.28,3

0,680,3.99,3

1,480,3.02,1

0,800,3.47,3

0,800,2.9,2

1,720,3.5,3

0,620,3.58,2

0,540,3.02,4

0,480,3.43,2

1,720,3.42,2

0,580,3.29,4

0,600,3.28,3

0,380,3.38,2

0,420,2.67,3

1,800,3.53,1

0,620,3.05,2

1,660,3.49,2

0,480,4,2

0,500,2.86,4

0,700,3.45,3

0,440,2.76,2

1,520,3.81,1

1,680,2.96,3

0,620,3.22,2

0,540,3.04,1

0,800,3.91,3

0,680,3.34,2

0,440,3.17,2

0,680,3.64,3

0,640,3.73,3

0,660,3.31,4

0,620,3.21,4

1,520,4,2

1,540,3.55,4

1,740,3.52,4

0,640,3.35,3

1,520,3.3,2

1,620,3.95,3

0,520,3.51,2

0,640,3.81,2

0,680,3.11,2

0,440,3.15,2

1,520,3.19,3

1,620,3.95,3

1,520,3.9,3

0,380,3.34,3

0,560,3.24,4

1,600,3.64,3

1,680,3.46,2

0,500,2.81,3

1,640,3.95,2

0,540,3.33,3

1,680,3.67,2

0,660,3.32,1

0,520,3.12,2

1,600,2.98,2

0,460,3.77,3

1,580,3.58,1

1,680,3,4

1,660,3.14,2

0,660,3.94,2

0,360,3.27,3

0,660,3.45,4

0,520,3.1,4

1,440,3.39,2

0,600,3.31,4

1,800,3.22,1

1,660,3.7,4

0,800,3.15,4

0,420,2.26,4

1,620,3.45,2

0,800,2.78,2

0,680,3.7,2

0,800,3.97,1

0,480,2.55,1

0,520,3.25,3

0,560,3.16,1

0,460,3.07,2

0,540,3.5,2

0,720,3.4,3

0,640,3.3,2

1,660,3.6,3

1,400,3.15,2

1,680,3.98,2

0,220,2.83,3

0,580,3.46,4

1,540,3.17,1

0,580,3.51,2

0,540,3.13,2

0,440,2.98,3

0,560,4,3

0,660,3.67,2

0,660,3.77,3

1,520,3.65,4

0,540,3.46,4

1,300,2.84,2

1,340,3,2

1,780,3.63,4

1,480,3.71,4

0,540,3.28,1

0,460,3.14,3

0,460,3.58,2

0,500,3.01,4

0,420,2.69,2

0,520,2.7,3

0,680,3.9,1

0,680,3.31,2

1,560,3.48,2

0,580,3.34,2

0,500,2.93,4

0,740,4,3

0,660,3.59,3

0,420,2.96,1

0,560,3.43,3

1,460,3.64,3

1,620,3.71,1

0,520,3.15,3

0,620,3.09,4

0,540,3.2,1

1,660,3.47,3

0,500,3.23,4

1,560,2.65,3

0,500,3.95,4

0,580,3.06,2

0,520,3.35,3

0,500,3.03,3

0,600,3.35,2

0,580,3.8,2

0,400,3.36,2

0,620,2.85,2

1,780,4,2

0,620,3.43,3

1,580,3.12,3

0,700,3.52,2

1,540,3.78,2

1,760,2.81,1

0,700,3.27,2

0,720,3.31,1

1,560,3.69,3

0,720,3.94,3

1,520,4,1

1,540,3.49,1

0,680,3.14,2

0,460,3.44,2

1,560,3.36,1

0,480,2.78,3

0,460,2.93,3

0,620,3.63,3

0,580,4,1

0,800,3.89,2

1,540,3.77,2

1,680,3.76,3

1,680,2.42,1

1,620,3.37,1

0,560,3.78,2

0,560,3.49,4

0,620,3.63,2

1,800,4,2

0,640,3.12,3

0,540,2.7,2

0,700,3.65,2

1,540,3.49,2

0,540,3.51,2

0,660,4,1

1,480,2.62,2

0,420,3.02,1

1,740,3.86,2

0,580,3.36,2

0,640,3.17,2

0,640,3.51,2

1,800,3.05,2

1,660,3.88,2

1,600,3.38,3

1,620,3.75,2

1,460,3.99,3

0,620,4,2

0,560,3.04,3

0,460,2.63,2

0,700,3.65,2

0,600,3.89,3

Expert Solution

please see the attachment to view the answer

venereology answered 5 months ago

To import the Carseats dataset into Rstudio: library("ISLR") data(Carseats) view(Carseats) Then, provide necessary codes for the...

To import the Carseats dataset into Rstudio: library("ISLR") data(Carseats) view(Carseats) Then, provide necessary codes for the following: a. Split the data into a training set and a test set. b. Fit a linear model using least squares on the training set to predict Sales using the entire collection of predictors. Report Cp , BIC, R2 , and RSS for this model c. Use the fitted model to predict responses for the test data and report the test error (RSS) obtained....

To import the Auto dataset into Rstudio: library("ISLR") data(Auto) view(Auto) Then, provide necessary codes for the...

To import the Auto dataset into Rstudio: library("ISLR") data(Auto) view(Auto) Then, provide necessary codes for the following: a. Use the vehicle name to name the rows and then remove the variable name from the data set since it is not of use for modelling. b. Split the data into a training set and a test set. c. Fit a regression tree to the the training set. Report the training error obtained. d. Plot the tree. e. How many terminal nodes...

Data shows graduate program admission decisions (Yes: 1 and No: 2), GRE score and undergraduate GPA...

Data shows graduate program admission decisions (Yes: 1 and No: 2), GRE score and undergraduate GPA for twenty-five students. Tasks: Examine if the given data is suitable for the application of linear discriminant analysis. Create a linear discriminant function predicting admission decisions. Comment on the classification accuracy. Predict the admission decision given GRE score = 690 and GPA = 3.2. Perform logistic regression analysis for the data. Compare the classification accuracies of both methods. Admit GRE GPA 2 790 3.8...

Use Minitab Data shows graduate program admission decisions (Yes: 1 and No: 2), GRE score and...

Use Minitab Data shows graduate program admission decisions (Yes: 1 and No: 2), GRE score and undergraduate GPA for twenty-five students. Tasks: Examine if the given data is suitable for the application of linear discriminant analysis. Create a linear discriminant function predicting admission decisions. Comment on the classification accuracy. Predict the admission decision given GRE score = 690 and GPA = 3.2. Perform logistic regression analysis for the data. Compare the classification accuracies of both methods. Admit GRE GPA 2...

In what real life research situations would you use a z-score? t-score? when would we know...

In what real life research situations would you use a z-score? t-score? when would we know the population standard deviation? When would we not know the population standard deviation?

Problem 1: An investigator in a university wants to know the mean math SAT score among...

Problem 1: An investigator in a university wants to know the mean math SAT score among all the applications to the university this year (denoted by μ). A random sample of n = 50 applications are selected. The sample mean is X = 530 and the sample variance is s2 = 8100. a. Consider the hypothesis testing problem: H0 : μ = 520 against Ha : μ ̸= 520. Use a significance level of α = 0.05. Compute the test...

1 a)What is an ‘import’? Please give a ’high profile’ example of a product or a...

1 a)What is an ‘import’? Please give a ’high profile’ example of a product or a service that our country imports. b). What is an ‘export’? Please give a ‘high profile’ example of a product or service that our country exports.

Please give me an example of how we import stock data in jupyter notebook(Python) and analyze...

Please give me an example of how we import stock data in jupyter notebook(Python) and analyze each step.

Please do these questions in the R language 1. Load the cars dataset into R. It...

Please do these questions in the R language 1. Load the cars dataset into R. It is a built-in dataset. 2. Do an str() to determine the number of observations and variables. Enter your answer as a comment. 3. Plot speed on x axis and distance on y axis. 4. Find the correlation between speed and distance. What does the magnitude and sign indicate? Enter your answer as a comment. 5. Build a linear regression model with speed as the...

Task 1: This is the instructions for the previous set, where we worked with list of...

Task 1: This is the instructions for the previous set, where we worked with list of lists as character records, (there is already a solved Chegg for task 1 (J. [40 points]). I need help with the code of converting isInrecord(), AddToRecord(), createRecord(), and frequencies() from task J to use dictionaries instead of lists (as is asked in task A, which is located below the line of ########). J. [40 points] This tasks includes working with lists and lists of...