Assignment 9 Computing Proximities

1 2 3 95 108 110 126 118 102 124 121 145 118 140 155 185 158 8 190 178 9 205 159 10 222 184 The table shows scores of 10 students in Java programming and Data Science The estimated Spearman's rank-order coefficient of correlation p is: A. r=0.842 B. r = -0.842 C. r= 0.158 D. r=-0.158 5 Student 6 Java Programming Data Science

Observe the table DOCTOR: DOCTOR DoctorID Name DateOfMDGraduation NumberOfPatients D111 John 1/1/1999 100 D222 Tina 1/2/2003 117 D333 Ellen 1/1/2001 117 D444 Tina 1/1/2003 144 Normalizing table DOCTOR to 3NF will result in: O Five separate tables O Four separate tables O No changes (table DOCTOR remains as is, no additional tables) O Three separate tables O Two separate tables

Q2) Consider the following table is used to store contact information: Name | Company | Adcress | Pnonel | PhoneZ | Phone3 | ZpCode Joe | ABC 123 5532 2234 |3211 12345 Jane |XYZ 456 [3421 | 14454 Chris |PDQ [789 | 2341 | 6655 | 14423 The table is considered as not normalized, explain why? And what is the INF of the table?

ce_S1_Mangesh / My courses/55 ITSE415_AppDataSci_S1 / Chapter 4- Data Preprocessing / Quiz 2 - 10 June 2021 Quiz navigation When we try to analyze the result-data of the last 10 semesters for the subject 'Programming-1' and if you are asked to find what will be the final score of any new student based on the value of his Quiz 2 marks, then what type of analysis is this? of Finish attempt- O a. Prescriptive Analysis Time left 0:17:27 tion O b. None of these Oc. Predictive Analysis O d. Descriptive Analysis Next page Jump to.. pter 4- Presentation

Which of the following statement(s) is/are true for bagging and boosting? A) Bagging: Weak learners are built in parallel Boosting: Weak learners are built in sequence one after the other B) Bagging: More weight to those weak learners with better performance Boosting: Each weak learner has equal weight in the final prediction. C) Bagging: Samples are drawn from the original dataset with replacement to train each individual weak learner Boosting: Subsequent samples have more weight of those observations which had relatively higher errors in previous weak learners D) Bagging: Random forest is a special type of bagging technique Boosting: Adaboost is a special type of boosting technique

Answer the given question with a proper explanation and step-by-step solution.  Create a scatter plot of the following data of minutes driven and miles driven using Excel Driver Number Minutes driven Miles driven 1 30 15 2 22 8 3 62 32 4 45 18 5 15 10 6 24 22 7 37 12 8 14 3 9 48 15 10 36 52 11 45 40 12 24 16 13 50 48 14 32 21 15 8 2 Is there a relationship between minutes driving and miles driven?

Damon Davis was creating the Drano Plumbing Company's spreadsheet. He determined that the net income would be $50,000. When he tallied the columns on the Balance Sheet, the totals were as follows: debit, $400,000; and credit, $300,000. What was the most likely reason for this discrepancy? If this was not the case, how should he proceed to determine the source of the error?

Data Warehous

There are 40 questions in this paper. Choose the most suitable answers. Which of the following integer arithmetic operation will cause an overflow error in a processing device with 8-bit memory system? 1. А. -64-64 В. 64+64 С. 60*2 D. 60/2 Which of the following is an example of GIGO? A. "Trial-and-error" approach is not a systematic method to solve problem. When a student submits the wrong answer to an online test, he will get zero mark. Weather forecast by supercomputer is not always reliable. When a cashier inputs incorrect quantity, the customer will get an incorrect receipt. 2. В. С. D. 3. John is writing a new fiction. Which of the following file format(s) is/are suitable for him to save his work? (1) RTF (2) PDF (3) PNG A. (1) only (2) only C. (1) and (2) only D. (2) and (3) only В. ||

+ ill You're the ChiefData Science Officer at a large bank. You've instructed your team to experiment with using payment data for marketing purposes, predicting which customer might be interested in a golf tournament that the bank sponsors. So the data instances correspond to customers, and the features are unique account numbers. Your newly hired team is ready to shine and has put quite some effort in building a linear model, where each ac-count number that one can pay to is given a coefficient. The prediction model hence predicts interest based on whom the customer has made payments to. They proudly report to you that the accuracy of their model is 95%, on a test set chosen in January. 1. What further questions would you ask on the evaluation? Think of test data, metrics, and baselines. 2. What would be potential privacy risks related to re-identification or the revelation of sensitive information of customers to the data science team? How to measure these? 3. Might there be…

True/False 2. Standard deviation measures how spread out a data set is.

Do a normalization from 1NF to 3NF based on the table below

Here are the recurrence relations: TẠ(n) = 2 × TA + n %3D Тв(п) %3D 4 x Тв + n2 Tc(n) = 8 x Tc +n Tp(n) = 8 × Tp + 1 %3D TĄ(1) = TR(1) = Tc(1) = Tp(1) = 0(1) Arrange the respective time complexities from deepest to shallowest (assuming the same input n). Example: TA(n) > T8(n) → Tc(n) → To(n) →

Three classifiers are to be benchmarked. To this end, using the same data, the classifiers were trained and the following table shows the validation results obtained with n = 16 observations. 1 1 0 2 0 3 1 4 1 5 1 6 1 7 0 8 9 10 11 12 13 14 15 16 OTTOTOO 0 1 1 1 1 0 ZOOOoooo Hooo o Ytrue Y1 Y2 Y3 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 11O 1 1 1 1 0001 Match the classifiers with the performance measures. Accuracy and Error rate for Y3 Choose... Accuracy and Error rate for Y2 Choose... TPR and FPR for Y1 Choose...

Kindly explain the rationale for the use of inferential statistics.

Computer programming

population_df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/AnalyseProject/world_population.csv', index_col='Country Code') Question 1: Population Growth   The world population data spans from 1960 to 2017. We'd like to build a predictive model that can give us the best guess at what the population growth rate in a given year might be. We will calculate the population growth rate as follows:-   ?????ℎ_????=???????_????_??????????−????????_????_??????????????????_????_??????????     As such, we can only calculate the growth rate for the year 1961 onwards. Write a function that takes the population_df and a country_code as input and computes the population growth rate for a given country starting from the year 1961. This function must return a return a 2-d numpy array that contains the year and corresponding growth rate for the country. Function Specifications: Should take a population_df and country_code string as input and return a numpy array…

population_df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/AnalyseProject/world_population.csv', index_col='Country Code')   Question 1: Population Growth   The world population data spans from 1960 to 2017. We'd like to build a predictive model that can give us the best guess at what the population growth rate in a given year might be. We will calculate the population growth rate as follows:- The formula to use in calculating the growth rate is as below:- Growth_rate =( Current_year_ population  - Previous_year_population) / Previous_year_population     As such, we can only calculate the growth rate for the year 1961 onwards. Write a function that takes the population_df and a country_code as input and computes the population growth rate for a given country starting from the year 1961. This function must return a return a 2-d numpy array that contains the year and corresponding growth rate for the country. Function Specifications: Should take…

The students'grades % as shown 80] %Calculate the mean of each student using % one command

Using the images of the dataset below, please answer the following questions  NOTE: All answers should be supported by coding and/or some written discussion. 1. Explain what this dataset about?Hint: You may need to look at columns, rows, and cells to understand the dataset. 2. Define the neighbor hood and the year where was the biggest square feetHint: You may use .max() method 3. Is there is any relation between the income and the square feet?Hint: You may need to draw (plot) your dataset. 4. For each minimum net income in the dataset, what was the years?Hint: You may use grouped and aggregated calculations 5. Reshape the dataset based on units and value.Hint: You may apply tidy data approaches

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Please written by computer source

Abhay Shisodia please answer:   Create a simple binary dataset (6 points in total, 3 for each class) that can be linearly separated.

Given the following: Facts: K Knowledge Base W&D0 D&N W N&K>D Make Forward Chaining for the above KB with the facts? (show the steps of exceution)

implement a D-i-D in this problem. Load the dataset on STATA: use http://www.stata.com/data/jwooldridge/eacsap/jtrain1 This has data on firms and the amount of job training they get. Only use the data from 1987 and 1988. Carefully study the data before you proceed. Construct the D-i-D estimator in different ways: (a)  Run the regressionhrsempit = β0 + β1 grant it + β21( year = 1988) + β3Ei + uit where Ei is a dummy variable for being a treatment (i.e. someone who would receive the grant in 1988). (b)  Run the fixed effect regression with firm fixed effects θi: hrsempit = θi + β1 grant it + β21( year = 1988) + uit (c)  Construct the 4 means of controls and treatments, before and after, and es- timate the difference in difference with means. (d)  Do you get exactly the same answer, why or why not? (e)  Now include other controls to estimate the D-i-D regression. Justify what- ever you include and interpret. Provide line by line code for STATA and the solution