Best Data Science Courses In Pune

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Eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo.

Udaypavaan

Uploaded on Sep 19, 2018

Category Education

ExcelR offers 160 hours classroom training on Business Analytics / Data Scientist / Data Analytics. We are considered as one of the best training institutes on Business Analytics in Pune. “Faculty and vast course agenda is our differentiator”. The training is conducted by alumni of premier institutions such as IIT & ISB who has extensive experience in the arena of analytics. They are considered to be one of the best trainers in the industry. The topics covered as part of this Data Scientist Certification program is on par with most of the Master of Science in Analytics (MS in Business Analytics / MS in Data Analytics) programs across the top-notch universities of the globe.

Category Education

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best data science courses in pune

Data Mining

Market
Basket

Analysis

Affinity
Analysis

Relationship
Mining

Association Rules

Market Basket Analysis

• Large number of transaction records through data collected using bar-code scanners

• Each record = All items purchased on a single purchase transaction

Association Rules

• What item goes with what

• Are certain groups of items consistently purchased together

• What business strategies will you device with this knowledge

Association Rules

• Products shelf placement – a specific product beside another

• Selling of prominent shelves – Slotting Fees

• Stocking – Supply Chain Management

• Price Bundling – Combo offers. How?

Source: http://www.economist.com/news/business/21654601-supplier-rebates-are-heart-some-supermarket-chains-woes-buying-up-shelves

https://en.wikipedia.org/wiki/Association_rule_learning

Association Rules – Cell phone faceplates

A store sells accessories for cellular
phones runs a promotion on
faceplates

OFFER!
Buy multiple faceplates from a choice of
6 different colors & get discount

How would you help store managers
device strategy to become more
profitable

Association Rules – Cell phone faceplates

Transaction # Faceplate colors purchased Transaction # Red White Blue Orange Green Yellow

1 Red White Green 1 1 1 0 0 1 0

2 White Orange 2 0 1 0 1 0 0

3 White Blue 3 0 1 1 0 0 0

4 Red White Orange 4 1 1 0 1 0 0

5 Red Blue 5 1 0 1 0 0 0

6 White Blue 6 0 1 1 0 0 0

7 White Orange 7 0 1 0 1 0 0

8 Red White Blue Green 8 1 1 1 0 1 0

9 Red White Blue 9 1 1 1 0 0 0

10 Yellow 10 0 0 0 0 0 1

List Format Binary Matrix Format

Association Rules are probabilistic “if-then” statements

2 Main Ideas:
 Examine all possible “if-then” rule formats
 Select rules, which indicates true dependence

Association Rules – Cell phone faceplates

Rules for { Red, White, Green}

1. If {Red, White} then {Green}

2. If {Red, Green} then {White}

3. If {White, Green} then {Red}

4. If {Red} then {White, Green}

5. If {White} then {Red, Green}

6. If {Green} then {Red, White}

Problem

• Many rules are possible

• How to select the
TRUE/GOOD rules from
all generated rules?

Association Rules – Terminology

• If {Red, White} then {Green}

• If Red & White phone faceplates are purchased, then Green
faceplate is purchased

 Antecedent: Red & White
 Consequent: Green

“IF” part = Antecedent = A

“THEN” part = Consequent = C

Association Rules – Performance Measures

Support

1
Confidence Lift

2 3

Association Rules – Support

Support

• Consider only combinations that occur with
higher frequency in the database

• Support is the criterion based on frequency

Percentage / Number of transactions in which
IF/Antecedent & THEN / Consequent appear in
the data

Mathematically:
# transactions in which A & C appear together
_____________________________________

Total no. of transactions

Support - Calculation

• What is the support for
“if White then Blue”?

1. 4
2. 40%
3. 2
4. 90%

Transaction # Faceplate colors purchased

1 Red White Green

2 White Orange

3 White Blue

4 Red White Orange

5 Red Blue

6 White Blue

7 White Orange

8 Red White Blue Green

9 Red White Blue

10 Yellow

• What is the support for
“if Blue then White”?

1. 4
2. 40%
3. 2
4. 90%

Support - Problem

• Generating all possible rules is exponential in the
number of distinct items

• Solution:
Frequent item sets using Apriori Algorithm

Apriori Algorithm For k products:

Set minimum support criteria

Generate list of one-item sets that meet the support
criterion

Use list of one-item sets to generate list of two-item sets
that meet support criterion

Use list of two-item sets to generate list of three-item sets
that meet support criterion

Continue up through k-item sets

Support – Criterion = 2

Transaction # Faceplate colors purchased

1 Red White Green

2 White Orange

3 White Blue

4 Red White Orange

5 Red Blue

6 White Blue

7 White Orange

8 Red White Blue Green

9 Red White Blue

10 Yellow

Item set Support (Count)

{Red} 5

{White} 8

{Blue} 5

{Orange} 3

{Green} 2

{Red, White} 4

{Red, Blue} 3

{Red, Green} 2

{White, Blue} 4

{White, Orange} 3

{White, Green} 2

{Red, White, Blue} 2

{Red, White, Green} 2

Create rules from
frequent item sets only

Support Criterion Example

Rules for { Red, White, Green}

1. If {Red, White} then {Green}

2. If {Red, Green} then {White}

3. If {White, Green} then {Red}

4. If {Red} then {White, Green}

5. If {White} then {Red, Green}

6. If {Green} then {Red, White}

How good are these
rules beyond the point
that they have high
support?

Association Rules – Confidence

Confidence

• Percentage of If/Antecedent transactions that
also have the Then/Consequent item set

Mathematically:

P (Consequent | Antecedent) = P(C & A) / P(A)

# transactions in which A & C appear together
_____________________________________

# transactions with A

Confidence - Calculation

• What is the confidence
for “if White then Blue”?

1. 4/5
2. 5/8
3. 5/4
4. 4/8

Transaction # Faceplate colors purchased

1 Red White Green

2 White Orange

3 White Blue

4 Red White Orange

5 Red Blue

6 White Blue

7 White Orange

8 Red White Blue Green

9 Red White Blue

10 Yellow

• What is the confidence
for “if Blue then White”?

1. 4/5
2. 5/8
3. 5/4
4. 4/8

Confidence - Weakness

• If antecedent and consequent have:

High Support => High / Biased Confidence

Association Rules – Lift Ratio

Lift Ratio

Confidence / Benchmark
confidence

Benchmark assumes independence between
antecedent & consequent:

P(antecedent & consequent) = P(antecedent) X P(consequent)Benchmark confidence
P(C|A) = P(C & A) / P(A) = P(C) X P(A) /P(A) = P(C)

# transactions with consequent item sets
_____________________________________

# transactions in database

Interpreting Lift

• Lift > 1 indicates a rule that is useful in finding consequent item
sets

• The rule above is much better than selecting random transactions

Lift - Calculation

• What is the Lift for “if White then Blue”?
1. 4/8
2. 5/10
3. 4/5
4. 1

Transaction # Faceplate colors purchased

1 Red White Green

2 White Orange

3 White Blue

4 Red White Orange

5 Red Blue

6 White Blue

7 White Orange

8 Red White Blue Green

9 Red White Blue

10 Yellow

Rules selection process

Generate all rules that meet
specified Support & Confidence

 Find frequent item sets based on
Support specified by applying
minimum support cutoff

 From these item sets, generate rules
with defined Confidence. By filtering
remaining rules select only those with
high Confidence

Rules
Inputs Data

# Transactions in Input Data 10

# Columns in Input Data 6

# Items in Input Data 6

# Association Rules 8

Minimum Support 2

Minimum Confidence 70.00%

List of
Rules

Rule: If all Antecedent items are purchased, then with Confidence percentage Consequent items will also be
purchased.

Row ID Confidence %
Antecedent

(A)
Consequent

A
Support for

C
Support for

A & C
Lift

Ratio

8 100 green red & white 2 4 2 2.5

4 100 green red 2 5 2 2

6 100 white & green red 2 5 2 2

3 100 orange white 3 8 3 1.25

5 100 green white 2 8 2 1.25

7 100 red & green white 2 8 2 1.25

1 80 red white 5 8 4 1

2 80 blue white 5 8 4 1

Alarming!

 Random data can generate apparently
interesting association rules

 More the rules you produce, greater the
danger

 Rules based on large numbers of records
are less subject to this danger

Profusion of rules

Applications
• What if Product & Stores are selected as a tuple for analysis?

• What if crimes in different
geographies for each week
is known?

Narcotics

Robbery

AssaultBattery Narcotics
Public
Peace

Violation

Recap with an example
• How can you use the information if you know about the

purchase history of customers in a specific geography?

• Supermarket database has 100,000 POS transactions

• 2000 transactions include both Strepsils & Orange Juice

• 800 of the above 2000 include Soup purchases

Recap with an example
• What is the support for rule “IF (Orange Juice & Strepsils) are purchased

THEN (Soup) is purchased on the same trip”?

1. 0.8 %
2. 2 %
3. 40 %

• What is the confidence for rule “IF (Orange Juice & Strepsils) are purchased
THEN (Soup) is purchased on the same trip”?

1. 0.8 %
2. 2 %
3. 40 %

Recap with an example

• What is the lift ratio for rule “IF (Orange Juice & Strepsils) are purchased
THEN (Soup) is purchased on the same trip”?

Sequential Pattern Mining

Purchases / events occur at

the same time

• If person X has taken “Data
Mining Unsupervised” training
in 1st Quarter, Person X has

also taken “Data Mining
Supervised” training in 2nd

Quarter

• Based on the statement
above, recommend “Data
Mining Supervised” training to
those who have enrolled for

“Data Mining Unsupervised”

NOT

IT IS

Association Rules vs. Sequential Pattern Mining

• Look for temporal patterns

• Order/sequence of a & b matters for a rule “b follows a”

• However, what happens in between a & b doesn’t matter

• In phone faceplates dataset:
 Association among items, which were bought within the

same week were discovered

 How about finding what they would buy next week or the
week after, if they had bought ‘x’ in this week?

Applications

• Identify the appropriate Basket

• Identify popular taxi routes

 Sequential pattern from GPS tracks;
spatiotemporal records of taxi
trajectories

 First cluster collocated customers

THANK YOU