8 Outline different data transformation methods. Examine the methods such as: min-max, z-score and decimal scaling for normalization of the following group of data: 200, 300, 500, 1000, 1300
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- + 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…In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset. 1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 3535How to plot using any data of y against x in a scatterplot.
- 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.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 solutionA researcher conducted computational thinking course over a period of five days among a group of beginners, intermediate and advanced learners of computer science. At the end of the programme, the researcher administered a test to determine which group of students benefited most from the programme. Explain how you would select a sample of 10 students for each group from 100 computer science students using stratified random sampling.
- Some of your friends have gotten into the burgeoning field of time-series data mining, in which onelooks for patterns in sequences of events that occur over time. Purchases at stock exchanges–what’s beingbought are one source of data with a natural ordering in time. Given a long sequence S of such events, yourfriends want an efficient way to detect certain “patterns” in them–for example, they may want to know ifthe four eventsbuy Yahoo, buy eBay, buy Yahoo, buy Oracleoccur in this sequence S, in order but not necessarily consecutively.They begin with a collection of possible events (e.g., the possible transactions) and a sequence S of n ofthese events. A given event may occur multiple times in S (e.g., Yahoo stock may be bought many times ina single sequence S). We will say that a sequence S′ is a subsequence of S if there is a way to delete certainof the events from S so that the remaining events, in order, are equal to the sequence S′. So, for example, the sequence of the four events…MatLab Load the data flu.mat (you can do this by typing load flu in your script). This data is the flu trends seen in the United States 2005-2006, divided by region. We will use regressions to look at the data during flu season in the Pacific region. Create your x data: have x equal to 1:30. These represent 30 weeks between Oct. 2005 and May 2006. Create your y data: have y equal to flu.Pac(1:30)’. This is the flu trend for each week. Make sure you have an apostrophe after the last parenthesis. Fit the data below with a straight line and with a 2nd order polynomial. Use least-squares regression. Calculate the coefficient of determination (r^2) and the correlation coefficient (r) for each regression. Plot the two regression curves against the data. Which regression is better? Is there a polynomial you think would work better? Describe the data – what does it mean to you?In order to address scale dependency of the correlation formula Corr, y = ENE x[n]y[n] we use O Convolution O Length Normalized Correlation Correlation with j=lag O Normalized correlation
- 4. Backpropagation is the most widely applied technique to optimize NN weights. Explain it briefly!4. Outliers can be detected using a statistical approach, one of popular approaches is the Inter Quartile Range (IQR) method. A sample is categorized as an outlier if its value is less than (Q1 - 1.5 * IQR) or greater than (Q3 + 1.5 * 1QR). Use these conditions to detect outliers in the following data: 130,132,138,136,131,153,131,133,129,133,110,132,129,134,135,132, 135,134,133,132,130,131,134,135,135,134,136,133,133,130 note: Q1 = quartile 1, Q3 = quartile 3 Draw the Boxplot to show the outliers %3DGiven a dataset X consists of over one million entries of research papers published in business journals and conferences. Among these entries, there are a good number of authors that have coauthor relationships. Propose a method to efficiently mine a set of co-author relationships that are closely related (e.g. often co-authoring papers together.) What pattern evaluation measures would you apply to convincingly uncover close collaboration patterns better than others.