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Item Identification
Text Mining/Machine Learning
Disjoint
Dressing
Chicken
Soup
Intersect portions are disjoint
Chicken
BurritosCheese
3/31/2017Umesh Bajaj 2
Disjoint/Independent
Soup
Beef
Chicken
Vegetable
noodle
Another look
3/31/2017Umesh Bajaj 3
Word Usage
From
Chicken
Sausages
Egg Roll
Fritters
Stock
For
Chicken
Gravy
3/31/2017Umesh Bajaj 4
Word Frequency
3/31/2017Umesh Bajaj 5
Word frequency – Chicken & Soup
3/31/2017Umesh Bajaj 6
Word Cloud
Chicken Soup
3/31/2017Umesh Bajaj 7
# A tibble: 22,572 × 11
line grp product type prep feature packaging size msg organic itemnm
<int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <fctr>
1 18 soup base beef ultimate n <NA> SOUP BASE BEEF ULTIMATE NO MSG
2 90 soup tomato basil bisque rtu <NA> <NA> SOUPTOMATO BASIL BISQUE RTU
3 177 soup beef <NA> <NA> SOUP BEEF & PPR
4 235 soup bowl <NA> <NA> MARINER BOWL SOUP 17.5Z ESPREE
5 347 soup tomato basil bisque rtu <NA> <NA> SOUP TOMATO BASIL BISQUE RTU
6 379 soup base chicken n <NA> SOUP BASE CHICKEN NO MSG ADD
7 432 soup base n <NA> SOUP BASE CHCKN CH NO MSG
8 433 soup base cream <NA> <NA> SOUP BASE CRM LOW SODIUM
9 582 soup vegetable rts <NA> <NA> SOUPVEG VEGAN RTS
10 648 soup vegetable quinoa <NA> y PACIFC SOUP,OG2,VEG QUINOA
# ... with 22,562 more rows
>
Result
3/31/2017Umesh Bajaj 8
Data Selection
? Total Population
? Vendor Item 3.5 million
? Manufacturer Item 600 K
? First Selection
? Two words frequency > 10000 (NGRAM, n = 2)
? Classified into group
? Second Selection
? Group basis
? Word frequency > 700
Random Sampling Multistage
3/31/2017Umesh Bajaj 9
Bayes’ Law ? Probability of an event based upon prior knowledge of
condition
? Two events are independent
? Determine probability of event A when event B is true? ? ? =
? ? ? ?(?)
?(?)
3/31/2017Umesh Bajaj 10
Bayes’ Law
cheese
No Soup
No Dressing
MOZZ/AMER/Cream
Pizza
Dressing
Soup
Implementation
cream
No Soup
No Dressing
No Cheese
Sour/Chocolate
Dressing
Soup
3/31/2017Umesh Bajaj 11
Cheese
Cheese (3.78%)
No Soup
No Dressing
MOZZ/AMER/Cream
Pizza (18.45%)
Dressing (1.60%)
Soup (0.56%)
“When you say I am a cheese” ? 3.78% items contain word cheese
? 0.56% items contain word soup in addition to cheese
? 1.60% items contain word dressing in addition to cheese
? 18.45% items contain word pizza in addition to cheese
? Probability for an item is a cheese
(0.0378 * 0.9944 * 0.9840 * 0.8154) ≈ 0.03 (3%)
3/31/2017Umesh Bajaj 12
Paradigm
? library(plyr)
? library(dplyr)
? library(tidytext)
? library(tm)
? library(magrittr)
? library(stringr)
? library(gtools)
? library(ggplot2)
? library(RODBC)
R
3/31/2017Umesh Bajaj 13
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Item Story

  • 2. Disjoint Dressing Chicken Soup Intersect portions are disjoint Chicken BurritosCheese 3/31/2017Umesh Bajaj 2
  • 6. Word frequency – Chicken & Soup 3/31/2017Umesh Bajaj 6
  • 8. # A tibble: 22,572 × 11 line grp product type prep feature packaging size msg organic itemnm <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <fctr> 1 18 soup base beef ultimate n <NA> SOUP BASE BEEF ULTIMATE NO MSG 2 90 soup tomato basil bisque rtu <NA> <NA> SOUPTOMATO BASIL BISQUE RTU 3 177 soup beef <NA> <NA> SOUP BEEF & PPR 4 235 soup bowl <NA> <NA> MARINER BOWL SOUP 17.5Z ESPREE 5 347 soup tomato basil bisque rtu <NA> <NA> SOUP TOMATO BASIL BISQUE RTU 6 379 soup base chicken n <NA> SOUP BASE CHICKEN NO MSG ADD 7 432 soup base n <NA> SOUP BASE CHCKN CH NO MSG 8 433 soup base cream <NA> <NA> SOUP BASE CRM LOW SODIUM 9 582 soup vegetable rts <NA> <NA> SOUPVEG VEGAN RTS 10 648 soup vegetable quinoa <NA> y PACIFC SOUP,OG2,VEG QUINOA # ... with 22,562 more rows > Result 3/31/2017Umesh Bajaj 8
  • 9. Data Selection ? Total Population ? Vendor Item 3.5 million ? Manufacturer Item 600 K ? First Selection ? Two words frequency > 10000 (NGRAM, n = 2) ? Classified into group ? Second Selection ? Group basis ? Word frequency > 700 Random Sampling Multistage 3/31/2017Umesh Bajaj 9
  • 10. Bayes’ Law ? Probability of an event based upon prior knowledge of condition ? Two events are independent ? Determine probability of event A when event B is true? ? ? = ? ? ? ?(?) ?(?) 3/31/2017Umesh Bajaj 10
  • 11. Bayes’ Law cheese No Soup No Dressing MOZZ/AMER/Cream Pizza Dressing Soup Implementation cream No Soup No Dressing No Cheese Sour/Chocolate Dressing Soup 3/31/2017Umesh Bajaj 11
  • 12. Cheese Cheese (3.78%) No Soup No Dressing MOZZ/AMER/Cream Pizza (18.45%) Dressing (1.60%) Soup (0.56%) “When you say I am a cheese” ? 3.78% items contain word cheese ? 0.56% items contain word soup in addition to cheese ? 1.60% items contain word dressing in addition to cheese ? 18.45% items contain word pizza in addition to cheese ? Probability for an item is a cheese (0.0378 * 0.9944 * 0.9840 * 0.8154) ≈ 0.03 (3%) 3/31/2017Umesh Bajaj 12
  • 13. Paradigm ? library(plyr) ? library(dplyr) ? library(tidytext) ? library(tm) ? library(magrittr) ? library(stringr) ? library(gtools) ? library(ggplot2) ? library(RODBC) R 3/31/2017Umesh Bajaj 13