IIBM MBA CASE LET ANSWER SHEETS - The term, ‘recommender systems’ is widely used nowadays. Recommender systems are composed of very simple algorithms that aim to provide
IIBM MBA CASE LET ANSWER SHEETS - The term, ‘recommender systems’ is widely used nowadays. Recommender systems are composed of very simple algorithms that aim to provide
IIBM MBA CASE LET ANSWER SHEETS - The term, ‘recommender systems’ is widely used nowadays. Recommender systems are composed of very simple algorithms that aim to provide
For
answersheets contact
info.answersheets@gmail.com
+91
95030-94040
Analytics with R
I. R is a programming language?
a) Closed source
b) GPL
c) Open source
d) Definite source
II. Who developed R?
a) Dennis Ritchie
b) John Chambers
c) Bjarne Stroustrup
III. R was named partly after the first
names of R authors?
a) One
b) Two
c) Three
d)
Four
IV. Packages are useful in collecting
sets into
unit ?
a) Single
b)
Multiple
V. R is an interpreted language so it can
access through ?
a) Disk operating
system
b) User interface
operating system
c) Operating
system
d) Command line interpreter
VI. Many quantitative analysts use R as
their tool?
a) Leading tool
b) Programming
tool
c) Both the above
VII. Predictive analysis is the branch of
analysis?
a) Advanced
b) Core
c) Both the above
VIII. Is used to make predictions about unknown future events?
a) Descriptive analysis
b) Predicitive analysis
c) Both the above
XI. How
many steps does the predictive analysis process contained?
a) 5
b) 6
c) 7
d)
8
X.
How many types of R objects are present in R data type?
a) 4
b) 5
c) 6
d)
7
Part Two:
1. Explain the data import in R language.
(5)
2. Explain how to communicate the outputs
of data analysis using R language. (5)
3. What is R? (5)
4. What are the disadvantages of R
Programming? (5)
Section
B: Case lets (40 marks)
Caselet 1
In the internet era, prediction of
customer behavior is a very valuable insight, since it helps a marketer to analyse
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 and 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-leader 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
costumers 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
labour to ensure that costumers experience a good and competitively priced
shopping service that would encourage them to purchase and return to the store
in future as well. Such relationships are critical to retailers for the
following reasons. First , they get to know the feedback of other stores and
requirements of the customers. Financial data of the local customers can be
calculated using time series data. Decision tree is very important for this
type of problem as we can calculate the risk factors in the local market and
understands 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 iphone 7 that was launched recently.
This brand also uses
time-series analysis for understanding the behavior of their customers by means
of data gathered from the earlier models like iphone 6 and iphone 6s. How the
customer used these earlier models and what features they look for in
competitive products provides important insights for product development.
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 analyse information from new products as well . We can analyse
customer behavior in conjunction with their financial status and give them best
discounts for their needs.
If we analyse 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 by means of information gain from the parent
to the child. Tree baised 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-iphone 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 tree and its 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)
2. Define properties in data mining, by
which we can increase the profits for retailers? (10)
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| IIBM MBA CASE LET ANSWER SHEETS - The term, ‘recommender systems’ is widely used nowadays. Recommender systems are composed of very simple algorithms that aim to provide |
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
enginers 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 crumps that a uses likes, which are
analysed 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 indentify 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-
1.0and 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 a number of 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 generate, a number of 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. Personalised Product
Information E-commerce Sites Such engines help in understanding customers’
preferences on the basis of their visit on the website. They show the customers
the most relevant recommendation-type products as per their needs or there
likes in real time. Recommendation improve as the cognitive learning improves
with regression about each visitor each time. Website Personalisation
This is used by many organizations to
calculate revenue on the basis of the number of hits from visitors. It
increases their sales and targets new customers through segmentation into
different cluster. It also allows getting in touch by message-centric methods.
Real-time Notifications This is used by e-commerce for letting 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 notification of shoppers’ activities on
their website.
Questions
1. Which filtering system is used for
matching and analysing historical interaction alone and define? (20)
Section C: Applied Theory (30
marks)
1. Compare R & Python (15)
2. Explain the data import in R language
(15)
For
answersheets contact
info.answersheets@gmail.com
+91
95030-94040
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