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DataAnytime is a data analytics firm and has established itself as a leading analytics provider insince 2016.

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DataAnytime is a data analytics firm and has established itself as a leading analytics provider insince 2016. It has served a wide range of customers over the years, including many manufacturing firms, marketing firms, trading firms, charities, and even political parties.

DataAnalytics is well-recognized in the region for its specialization in gaining insights from raw data. Helen started working for DataAnytime just over a month ago and had an important meeting last week with the CEO of her firm, David. During the meeting, the CEO provided an overview of the kind of work DataAnytime has been engaged over the years.

He also discussed with Helen some of the jobs received by DataAnytime from its customers recently. The jobs were of various natures but all involved analyzing data and providing key insights from the data. An existing customer, by the name of QuietTime specializes in conducting workshops and organizing conferences in quiet and peaceful areas across the world and has asked DataAnytime if it could provide some insights regarding the noise level in various countries.

QuietTime is interested in the level of noise in various countries so that it can identify potential areas where it could organize different workshops and conferences in the year 2023. Another marketing firm by the name of AlwaysText is examining how companies are providing information in their annual reports.

AlwaysText is interested mainly in the non- financial and textual information provided in the annual reports that may not necessarily be reflected in the financial disclosures. AlwaysText has made a name for itself for analysing sentiments contained in textual information in annual reports and also for exposing the practice of ‘green-washing’ whereby companies make bold claims and promises regarding their commitment towards environment, but with very little meaningful action to support such claims.

Yet another company by the name of Movie247 specializes, as the name might suggest, in making award-winning movies. Movie247 has made several movies in 2022 and it plans to nominate some of its movies for the highly acclaimed Noscar award under various categories. Given that Movie247 can self-nominate only 10 movies for the award, it wants to make sure that the movies it submits for consideration have the highest chance of success of winning the Noscar award. Given the various tasks DataAnytime had to perform soon, David asked Helen if she could provide some key information and insights to help the customers.

David also handed her a USB drive with a folder called “Assignment_Data.zip” that contains relevant data for the tasks. “Good luck!”, said David, “but remember, having the data in structured and unstructured form is one thing, but making it ready for analysis is a different matter altogether”. Helen responded by saying that she would try her best. Requirement Q 1) It is understood that the noise level produced by various factories in different countries are denoted by ‘Noise’ in the file StataFiIe.dta. To help QuietTime choose the right place to organize seminars and conferences across the world, use StataFile.dta and Country.csv to:

i. Create a box plot for noise level by Nation ii. Create an interactive chart by Nation Provide the codes that can be replicated. Marking Scheme: Box plot by Nation — 15 Marks

( an excellent box plot would contain title [2 marks], labels in axes [2 marks], mean values, median values, quartiles [7 marks ], and outliers [2 marks] in a well-presented [2 marks] format) Interactive chart — 10 Marks Failure to provide working codes will nullify the respective marks above 2) To help Movie247 select its movies for nomination so that its chance of winning a Noscar award is maximized, use movies.csv file to perform the following tasks:

i. build a Decision Tree model to predict the likelihood of a movie winning a Noscar award​15 Marks

ii. build a Random Forest model to predict the likelihood of a movie winning a Noscar award​15 Marks

iii. present the accuracy of the Decision Tree model using confusion matrix.​5 Marks iv. present the accuracy of the Random Forest model using confusion matrix​5 Marks Marking Scheme: Building Decision Tree model — 15 Marks Building Random Forest model — 15 Marks Confusion matrix and accuracy of Decision Tree — 5 Marks Confusion matrix and accuracy of Random Forest — 5 Marks Failure to provide working codes will nullify the respective marks above.

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