Data Analytics For Business 1st Edition Fenio Annansingh, Joseph Bon Sesay - Solutions

Unlock the full potential of your studies with our comprehensive solutions for "Data Analytics For Business 1st Edition" by Fenio Annansingh and Joseph Bon Sesay. Access online answers key and detailed solutions manual to enhance your learning experience. Our solutions PDF offers step-by-step answers to solved problems, ensuring you grasp each concept thoroughly. Explore the test bank and instructor manual for in-depth chapter solutions, making textbook learning more interactive. Available for free download, our resources provide the questions and answers you need to excel in data analytics.

Predicting Delayed Flights (Boosting). The file FlightDelays.csv contains information on all commercial flights departing the Washington, DC area and arriving at New York during January 2004. For each flight, there is information on the departure and arrival airports, the distance of the route, the
eBay Auctions—Boosting and Bagging. Using the eBay auction data (file eBayAuctions.csv) with variable Competitive as the outcome variable, partition the data into training (60%) and validation (40%).a. Run a classification tree, using the default settings of DecisionTreeClassifier. Looking at the
Acceptance of Consumer Loan. Universal Bank has begun a program to encourage its existing customers to borrow via a consumer loan program. The bank has promoted the loan to 5000 customers, of whom 480 accepted the offer. The data are available in file UniversalBank.csv. The bank now wants to
Detecting Spam E-mail (from the UCI Machine Learning Repository). A team at Hewlett-Packard collected data on a large number of e-mail messages from their postmaster and personal e-mail for the purpose of finding a classifier that can separate e-mail messages that are spam vs. nonspam (a.k.a.
Identifying Good System Administrators. A management consultant is studying the roles played by experience and training in a system administrator’s ability to complete a set of tasks in a specified amount of time. In particular, she is interested in discriminating between administrators who are
Personal Loan Acceptance. Universal Bank is a relatively young bank growing rapidly in terms of overall customer acquisition. The majority of these customers are liability customers with varying sizes of relationship with the bank. The customer base of asset customers is quite small, and the bank
Direct Mailing to Airline Customers. East–West Airlines has entered into a partnership with the wireless phone company Telcon to sell the latter’s service via direct mail. The file EastWestAirlinesNN.csv contains a subset of a data sample of who has already received a test offer. About 13%
Car Sales. Consider the data on used cars (ToyotaCorolla.csv) with 1436 records and details on 38 attributes, including Price, Age, KM, HP, and other specifications. The goal is to predict the price of a used Toyota Corolla based on its specifications.a. Fit a neural network model to the data. Use
Neural Net Evolution. A neural net typically starts out with random values for bias and weights; hence, it produces essentially random predictions when presented with its first case. What is the key ingredient by which the net evolves to produce a more accurate prediction?
Credit Card Use. Consider the hypothetical bank data in Table 11.7 on consumers’use of credit card credit facilities. Create a small worksheet in Excel or a Python program to illustrate one pass through a simple neural network. (Randomly generate initial weight values.) TABLE 11.7 DATA FOR CREDIT
Competitive Auctions on eBay.com. The file eBayAuctions.csv contains information on 1972 auctions transacted on eBay.com during May–June 2004. The goal is to use these data to build a model that will distinguish competitive auctions from noncompetitive ones. A competitive auction is defined as an
Sales of Riding Mowers. A company that manufactures riding mowers wants to identify the best sales prospects for an intensive sales campaign. In particular, the manufacturer is interested in classifying households as prospective owners or nonowners on the basis of Income (in $1000s) and Lot Size
Identifying Good System Administrators. A management consultant is studying the roles played by experience and training in a system administrator’s ability to complete a set of tasks in a specified amount of time. In particular, she is interested in discriminating between administrators who are
Financial Condition of Banks. The file Banks.csv includes data on a sample of 20 banks. The “Financial Condition” column records the judgment of an expert on the financial condition of each bank. This outcome variable takes one of two possible values—weak or strong—according to the
Predicting Prices of Used Cars (Regression Trees). The file ToyotaCorolla.csv contains the data on used cars (Toyota Corolla) on sale during late summer of 2004 in the Netherlands. It has 1436 records containing details on 38 attributes, including Price, Age, Kilometers, HP, and other
Predicting Delayed Flights. The file FlightDelays.csv contains information on all commercial flights departing the Washington, DC area and arriving at New York during January 2004. For each flight, there is information on the departure and arrival airports, the distance of the route, the scheduled
Competitive Auctions on eBay.com. The file eBayAuctions.csv contains information on 1972 auctions that transacted on eBay.com during May–June 2004. The goal is to use these data to build a model that will classify auctions as competitive or noncompetitive. A competitive auction is defined as an
Automobile Accidents. The file accidentsFull.csv contains information on 42,183 actual automobile accidents in 2001 in the United States that involved one of three levels of injury: NO INJURY, INJURY, or FATALITY. For each accident, additional information is recorded, such as day of week, weather
Personal Loan Acceptance. The file UniversalBank.csv contains data on 5000 customers of Universal Bank. The data include customer demographic information (age, income, etc.), the customer’s relationship with the bank (mortgage, securities account, etc.), and the customer response to the last
Predicting Housing Median Prices. The file BostonHousing.csv contains information on over 500 census tracts in Boston, where for each tract multiple variables are recorded. The last column (CAT.MEDV) was derived from MEDV, such that it obtains the value 1 if MEDV > 30 and 0 otherwise. Consider the
Personal Loan Acceptance. Universal Bank is a relatively young bank growing rapidly in terms of overall customer acquisition. The majority of these customers are liability customers (depositors) with varying sizes of relationship with the bank. The customer base of asset customers (borrowers) is
Calculating Distance with Categorical Predictors. This exercise with a tiny dataset illustrates the calculation of Euclidean distance, and the creation of binary dummies. The online education company Statistics.com segments its customers and prospects into three main categories: IT professionals
Predicting Prices of Used Cars. The file ToyotaCorolla.csv contains data on used cars (Toyota Corolla) on sale during late summer of 2004 in the Netherlands. It has 1436 records containing details on 38 attributes, including Price, Age, Kilometers, HP, and other specifications. The goal is to
Predicting Airfare on New Routes.The following problem takes place in the United States in the late 1990s, when many major US cities were facing issues with airport congestion, partly as a result of the 1978 deregulation of airlines. Both fares and routes were freed from regulation, and low-fare
Predicting Software Reselling Profits. Tayko Software is a software catalog firm that sells games and educational software. It started out as a software manufacturer and then added third-party titles to its offerings. It recently revised its collection of items in a new catalog, which it mailed out
describes each of the predictors and the outcome variable.a. Why should the data be partitioned into training and validation sets? What will the training set be used for? What will the validation set be used for?b. Fit a multiple linear regression model to the median house price (MEDV) as a
Predicting Boston Housing Prices. The file BostonHousing.csv contains information collected by the US Bureau of the Census concerning housing in the area of Boston, Massachusetts. The dataset includes information on 506 census housing tracts in the Boston area. The goal is to predict the median
Table 5,7 shows a small set of predictive model validation results for a classification model, with both actual values and propensities.a. Calculate error rates, sensitivity, and specificity using cutoffs of 0.25, 0.5, and 0.75.b. Create a decile lift chart. TABLE 5.7 PROPENSITIES AND ACTUAL CLASS
A firm that sells software services has been piloting a new product and has records of 500 customers who have either bought the services or decided not to. The target value is the estimated profit from each sale (excluding sales costs). The global mean is about $2500. However, the cost of the sales
A large number of insurance records are to be examined to develop a model for predicting fraudulent claims. Of the claims in the historical database, 1% were judged to be fraudulent. A sample is taken to develop a model, and oversampling is used to provide a balanced sample in light of the very low
Consider Figure 5,12, the decile lift chart for the transaction data model, applied to new data.a. Interpret the meaning of the first and second bars from the left.b. Explain how you might use this information in practice.c. Another analyst comments that you could improve the accuracy of the model
FiscalNote is a startup founded by a Washington, DC entrepreneur and funded by a Singapore sovereign wealth fund, the Winklevoss twins of Facebook fame, and others. It uses machine learning and data mining techniques to predict for its clients whether legislation in the US Congress and in US state
Suppose that this routine has an adjustable cutoff (threshold) mechanism by which you can alter the proportion of records classified as fraudulent. Describe how moving the cutoff up or down would affecta. the classification error rate for records that are truly fraudulentb. the classification error
A data mining routine has been applied to a transaction dataset and has classified 88 records as fraudulent (30 correctly so) and 952 as non-fraudulent (920 correctly so).Construct the confusion matrix and calculate the overall error rate.
Chemical Features of Wine. Table 4,14 shows the PCA output on data (nonnormalized) in which the variables represent chemical characteristics of wine, and each case is a different wine?a. The data are in the file Wine.csv. Consider the rows labeled “Proportion of Variance.” Explain why the value
Sales of Toyota Corolla Cars. The file ToyotaCorolla.csv contains data on used cars(Toyota Corollas) on sale during late summer of 2004 in the Netherlands. It has 1436 records containing details on 38 attributes, including Price, Age, Kilometers, HP, and other specifications. The goal will be to
University Rankings. The dataset on American college and university rankings(available from www.dataminingbook.com) contains information on 1302 American colleges and universities offering an undergraduate program. For each university, there are 17 measurements that include continuous measurements
to explore and summarize the data as follows:a. Which variables are quantitative/numerical? Which are ordinal? Which are nominal?b. Compute the mean, median, min, max, and standard deviation for each of the quantitative variables. This can be done using pandas as shown in Table 4.3.c. Plot a
Breakfast Cereals. Use the data for the breakfast cereals example in Section
Laptop Sales at a London Computer Chain: Interactive Visualization. The next exercises are designed for using an interactive visualization tool. The file LaptopSales.csv is a comma-separated file with nearly 300,000 rows. ENBIS (the European Network for Business and Industrial Statistics) provided
Laptop Sales at a London Computer Chain: Bar Charts and Boxplots. The file LaptopSalesJanuary2008.csv contains data for all sales of laptops at a computer chain in London in January 2008. This is a subset of the full dataset that includes data for the entire year.a. Create a bar chart, showing the
Sales of Riding Mowers: Scatter Plots. A company that manufactures riding mowers wants to identify the best sales prospects for an intensive sales campaign. In particular, the manufacturer is interested in classifying households as prospective owners or nonowners on the basis of Income (in $1000s)
Shipments of Household Appliances: Line Graphs. The file ApplianceShipments.csv contains the series of quarterly shipments (in millions of dollars) of US household appliances between 1985 and 1989.a. Create a well-formatted time plot of the data using Python.b. Does there appear to be a quarterly
The dataset ToyotaCorolla.csv contains data on used cars on sale during the late summer of 2004 in the Netherlands. It has 1436 records containing details on 38 attributes, including Price, Age, Kilometers, HP, and other specifications.We plan to analyze the data using various data mining
Two models are applied to a dataset that has been partitioned. Model A is considerably more accurate than model B on the training data, but slightly less accurate than model B on the validation data. Which model are you more likely to consider for final deployment?
Statistical distance between records can be measured in several ways. Consider Euclidean distance, measured as the square root of the sum of the squared differences.For the first two records in Table 2.18, it is√(25 − 56)2 + (49, 000 − 156, 000)2.Can normalizing the data change which two
Normalize the data in Table 2.18, showing calculations.
A dataset has 1000 records and 50 variables with 5% of the values missing, spread randomly throughout the records and variables. An analyst decides to remove records with missing values. About how many records would you expect to be removed?
In fitting a model to classify prospects as purchasers or nonpurchasers, a certain company drew the training data from internal data that include demographic and purchase information. Future data to be classified will be lists purchased from other sources, with demographic (but not purchase) data
Using the concept of overfitting, explain why when a model is fit to training data, zero error with those data is not necessarily good.
Consider the sample from a bank database shown in Table 2.17; it was selected randomly from a larger database to be the training set. Personal Loan indicates whether a solicitation for a personal loan was accepted and is the response variable. A campaign is planned for a similar solicitation in the
Consider the sample from a database of credit applicants in Table 2.16. Comment on the likelihood that it was sampled randomly, and whether it is likely to be a useful sample.
Describe the difference in roles assumed by the validation partition and the test partition.
Assuming that data mining techniques are to be used in the following cases, identify whether the task required is supervised or unsupervised learning.a. Deciding whether to issue a loan to an applicant based on demographic and financial data (with reference to a database of similar data on prior
What is the four-step analytics process to look at for higher education operational efficiency? Please explain each of them.
Does learning analytics help students or the college and university?
Explain what donor and alumni analytics is.
What is performance-based funding? Is it a fair system?
Do you think retention rate is a good measure of the quality of an institution?
What is the value of enrolment analytics to a college or university?
What do you think should be included in admission analytics?
Explain what recruitment analytics is.
What do you think are the external factors higher education faces?
What are the internal challenges higher education faces? Please explain.
Discuss the growth of HE on a global scale. What are some of the reasons for this development?
What impact COVID-19 has had on the education industry? Do you think these changes will be temporary and are they for the better?
Explain some of the challenges faced by HE institutions?
Do you support the concept that education is a human right? If so, ex- plain why everyone must be educated. If not, please explain your reasons.
Identify how the transportation sector can employ Big Data analytics to optimise the usage of resources.
Describe how Big Data analytics can help companies develop sustainable transportation modes that have little or no impact on the environment.
Road transportation is one of the largest producers of carbon emission pollutants. So what can firms do to reduce the effects of carbon emissions?
Describe how the ITS detection system is helping transport managers to retrieve real-time traffic information from each collision case.
Explain the impact of the transportation industry on the environment. What can be done to reduce emissions?
Transportation and procurement can consume a tremendous amount of business resources. Explain how data analytics is helping companies to save resources.
Describe how data analytics is applied in logistics and supply chain strategy.
How are companies using Big Data analytics to prevent traffic conges- tion and rear-end accidents?
Identify how Big Data analytics assist with Maintenance and Operations in transportation?
How is Big Data used in the transportation sector to improve operational efficiency?
Identify and discuss the different modes of transportation.
Discuss the core components of transportation. Discuss which of these, if any, is most significant.
Define transportation and explain how it has helped the development of modem society.
How has data analytics made marketing segmentation easier?
What is the value of Big Data and data analytics in observing the behav- iours of customers?
What are the challenges of marketing analytics models?
What is a Custom Multi-Touch Attribution Model?
What is a W-shaped Multi-Touch Attribution Model?
Explain what Linear Multi-touch Attribution Model is.
Explain what touchpoints are and give at least six examples of touchpoints.
What is the significant difference between Media-Mix and Multi-Touch Attribution analytics?
Explain what Multi-Touch Attribution analytics is.
What are the challenges of Media-mix model analytics?
How is Media-Mix marketing analytics beneficial to marketers?
Explain what Media-Mix model marketing analytics is.
List and explain four benefits of marketing analytics.
What do you understand by marketing analytics?
What is one of the main objectives of marketing?
Before the digital age, what was the traditional marketing medium ad- vertisers used to reach their customers?
How has data analytics improved research and development (R&D) in the manufacturing process?
Discuss what Profit-per-hour (PPH) maximisation analytics is.
What is Yield-energy throughput (YET) analytics?

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Data Analytics For Business 1st Edition Fenio Annansingh, Joseph Bon Sesay - Solutions

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