IIBM – Analytics with R Answer sheet

 

IIBM – Analytics with R Answer sheet

1. R is a programming language?
2. a) Closed source
3. b) GPL
4. c) Open source
5. d) Definite source

1. Who developed R?

1. a) Dennis Ritchie
2. b) John Chambers
3. c) Bjarne Stroustrup

III. R was named partly after the first names of R authors.

1. a) One
2. b) Two
3. c) Three
4. d) Four

1. Packages are useful in collecting sets into units.
2. a) Single
3. b) Multiple

1. R is an interpreted language so it can be accessed through?
2. a) Disk operating system
3. b) User interface operating system
4. c) Operating system
5. d) Command line interpreter

1. Many quantitative analysts use R as their tool.

1. a) Leading tool
2. b) Programming tool
3. c) Both the above

VII. Predictive analysis is the branch of analysis.

1. a) Advanced
2. b) Core
3. c) Both the above

VIII. ————— is used to make predictions about unknown future events?

1. a) Descriptive analysis
2. b) Predicative analysis
3. c) Both the above

1. How many steps does the predictive analysis process contain?
2. a) 5
3. b) 6
4. c) 7
5. d) 8

1. How many types of R objects are present in the R data type?
2. a) 4
3. b) 5
4. c) 6
5. d) 7

Part Two:

1. Explain the data import in R language. (5)

1. Explain how to communicate the outputs of data analysis using R language. (5)

1. What is R? (5)

1. What are the disadvantages of R Programming? (5)

Section B: Case lets (40 marks)

Caselet 1

IIBM – Analytics with R Answer sheet

In the internet era, prediction of customer behavior is a very valuable insight, since it helps a marketer to analyze its products’ value and send updates for selling its products. The online market depends on the history of its customers. Devising new strategies for markets attracting customers to stores and trying to convert the incoming traffic into sales profitably are all vital to the financial health of retailers. Every retailer uses different strategies to increase store traffic and convert traffic into profits. They invest in prime real estate with desirable properties such as high foot traffic of their targeted customer segments, customer populations, customer convenience, and visibility. Once they determine a location, retailers drive store traffic in a variety of ways such as spending on advertising, offering loss-leaders about the products with various discounts, or conducting various promotional events in local markets, such as offering discounts at various levels or price deductions. Whenever customers visit a store, retailers try to convert the customers profitably through several means. They ensure that the right product is available at the right place, at the right time, and at the right price. They invest in-store labor to ensure that customers experience a good and competitively priced shopping service that would encourage them to purchase and return to the store in the future as well. Such relationships are critical to retailers for the following reasons. First, they get to know the feedback of other stores and the requirements of the customers. Financial data of the local customers can be calculated using time series data. A decision tree is very important for this type of problem as we can calculate the risk factors in the local market and understand the needs of the customers from their previous behavior. This is also known as learning the cognitive behavior of the customer. Let us take the example of the iPhone 7 that was launched recently. This brand also uses time-series analysis to understand the behavior of its customers using data gathered from earlier models like the iPhone 6 and iPhone 6s. How the customer used these earlier models and what features they look for in competitive products provide important insights for product development. A decision tree is very useful for gathering information about new market values as these depend on the time series that comes from historical data. Using such data, we can analyze information from new products as well. We can analyze customer behavior in conjunction with their financial status and give them the best discounts for their needs. If we analyze historical data, many products have failed badly because they were not able to understand the requirements of the market at that time. So, to play it safe, every company nowadays tries to understand the market and its needs as per the market values, thus, creating a decision tree from the time-series data is an essential task for them. Decision trees can help in reducing errors using information gained from the parent to the child. Tree-based induction in ID3 helps to generate a recommendation engine. Such an engine is a powerful tool to understand the needs of the market and help companies choose profitable markets. Decision trees have many features that are very helpful to retailers and companies for offering discounts by comparing the information gain and loss in the market. This is also done by understanding the behavior of the customer with regards to the new product and older products 6 and 6s being pertinent examples here because after launching iPhone 7 and 7s the prices of iPhone 6 and 6s were reduced by 20k in the Indian Market. Using decision trees and their properties in data mining, we can increase the profits for retailers and help companies convert customer traffic into profits. Data mining is presented in more detail in the next few chapters.

Questions

1. What are the features of decision trees? (10)

1. Define properties in data mining, by which we can increase the profits for retailers. (10)

Caselet 2

The term, ‘recommender systems’ is widely used nowadays. Recommender systems are composed of very simple algorithms that aim to provide the most relevant and accurate information to users by sorting/filtering useful information from very large databases. Recommendation engineers discover data patterns from a given dataset by learning the consumers’ information and then producing outcomes that correlate to their needs and interests. In addition, recommendation engines narrow down the risk that could become a complex decision to just a few recommendations search. Big data supports recommendations at an unimaginable level these days. Recommendation engines work mainly in one of the following two ways, viz., either they rely on the properties of items with their bread crumbs that a uses likes, which are analyzed to determine what else the user may like, or they rely on the likes and dislikes of other users, which the recommendation engine uses to compute a similarity index between users and recommends items to them accordingly. It is also possible to combine both these methods to build a highly advanced recommendation engine. The main goal is to achieve the recommended collective information of users for the items that might interest customers. These systems have access to user-centric information with profile attributes, such as demographics and product descriptions. They differ in the way they interact while analyzing the data to develop affinity values between users and items, which can be used to identify well-matched pairs. A collaborative filtering system is used for matching and analyzing historical interaction alone, while content-based filtering is used for profiling-based attributes. Let us see how we can implement a recommendation engine with a collaborative memory-based recommendation engine. However, before that, we must first understand the logic behind such a system. To this engine, each item and each user is nothing but an identifier or token element. Let us take the example of Netflix. Please note that we will not take any other attribute of a movie, such as cast, director, genre, etc., into consideration while generating recommendations for users. The similarity between two users is represented by using a decimal number between- 1.0 and 1.0. We will call this number, the similarity index. The possibility of a user liking a movie will be represented by using another decimal number between -1.0 and 1.0. Now that we have modeled the world around this system using simple terms, we can unleash a handful of elegant mathematical equations to define the relationship between these identifiers and numbers. In our recommendation algorithm, we will maintain several sets, which should represent a member of supersets with all users and identifiers. Each user will have two sets, viz., a set of movies the user likes and a set of movies the user dislikes, Each movie will also have two sets associated with it, viz., a set of users who liked the movie and a set of users who disliked the movie. During the performance where recommendations start to be generated, several sets will be produced, mostly unions or intersections of the other sets. We will also have ordered lists of suggestions and similar users for each user. Similarly, like movies, we can use the following recommendations. Personalized Product Information E-commerce Sites Such engines help in understanding customers’ preferences based on their visit to the website. They show the customers the most relevant recommendation-type products as per their needs or their likes in real-time. Recommendation improves as cognitive learning improves with regression about each visitor each time. Website Personalization

This is used by many organizations to calculate revenue based on the number of hits from visitors. It increases their sales and targets new customers through segmentation into different clusters. It also allows getting in touch by message-centric methods. Real-time Notifications This is used by e-commerce to let their customers know about the new top-selling brands and available discounts. Such engines help brands build trust among their customers and create a sense of presence and urgency while showing real-time notifications of shoppers’ activities on their website.

Questions

1. Which filtering system is used for matching and analyzing historical interaction alone and defines? (20)

Section C: Applied Theory (30 marks)

1. Compare R & Python (15)

1. Explain the data import in R language (15)

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