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Flashcards in Business Intelligence (BI) And Business Analytics Deck (36)
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1

The ability to gather and make sense of information in the context of a business

Business intelligence

2

Purpose of business intelligence

Gain superior insight and understanding of the business and it's ecosystem
Understand the past and the present -> predict the future
Make better decisions

3

Components of business intelligence

Storage (data warehouse/data marts)
Data mining tools (for business analytics)
Reporting and visualization tools (e.g. Dashboards)

4

What is data mining?

The computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems

5

Customer segmentation

Who are the most valuable customers to a girl

6

Marketing and promotion targeting

Identifying which customers will respond to each offer

7

Market basket analysis

Which products customers buy together and how an organization can use this information to cross sell more
Type of association rules mining determining what products go together in a shopping cart at a retailer

8

Collaborative filtering

Personalizing an individual customers experience based on trends and preferences exhibited by similar customers

9

Customer churn

Which customers are more likely to leave and which retention strategies are most likely to succeed

10

Fraud detection

Uncover patterns consistent with criminal activity

11

Financial modeling

Building trading systems that adapt to historical trends or risk models to identify customers with the highest likelihood to default on a credit

12

Five classes of data mining tasks

Association detection (can be both)
Clustering (unsupervised)
Classifications (supervised)
Regressions (supervised)
Anomaly/outlier

13

Unsupervised data mining

Analysts do NOT create the model before running analysis
Apply data mining technique and observe results
Hypothesis created AFTER analysis as explanation for results
Ex. Cluster analysis, cluster creation for collaborative filtering

14

Supervised data mining

Model developed BEFORE analysis
Statistical techniques used to estimate parameters
Ex. Classification, regression analysis

15

Association rules mining

Determine which behaviors/outcomes go together
Find relationships among attributes in data that frequently occur together
Ex. Products bought together, symptoms and illnesses manifest together

16

Product affinities

Likelihood of two or more products being sold together

17

Support (association rule evaluation)

How often do these things appear together?
The probability that things will occur together
s{product1, product2}
SLHS&RHS

18

Confidence (association rule evaluation)

Given LHS, how often do we see RHS?
P(RHS|LHS)
sLHS&RHS/sLHS
*asymmetric

19

Lift (association rule evaluation)

How often does LHS appear with RHS, compared to how often chance would predict RHS would occur anyway?
The ratio of observed support to the expected support assuming the events are independent
c(LHS->RHS)/s(RHS)
(sLHS&RHS/sLHS)/sRHS
>1 indicates positive correlation (co occurance more likely than chance)
= approx 1 indicates almost no correlation, events are independent
<1 indicates negative correlation - co occurrence is less likely than chance

20

Complementary products lift

Greater than 1

21

Substitute products lift

Less than 1

22

Cluster analysis

Similar records (or characteristics) are grouped together
Does not rely on predefined categories (labels, groups) - records grouped together on the basis of self-similarity (unsupervised data mining)

23

Classification

Arrange the data into predefined groups (supervised data mining)

24

Recursive partitioning

A technique for creating a decision free to reach the desired level of purity

25

Purity of a subgroup

The proportion of its records that belong to the same class

26

Error rate

Percent of misclassified records out of the total records in the validation data

27

Positive Predicted, Positive Actual

True positive

28

Positive predicted, negative actual

False positive

29

Negative predicted, positive actual

False negative

30

Negative predicted, negative actual

True negative