The objective of data mining on food inspections was to get causality of why the license are getting rejected. Also, determine the trend and the seasonality involved in number of licenses getting failed or passed, and forecast the same. What measures food establishments must take to make sure the license inspection gets pass.

1. Food Inspections
TEAM 7
Vibhor Kaushik
Pavan Ebbadi
Manas Kar
Yifei Tang
UCONN - OPIM 5671 - TEAM 7 1

2. Agenda
•Introduction
•Business Objective
•Dataset description
•Forecasting of Pass and Failed Inspections
•Primary factors involved in inspections
•Trivial and interesting facts in the dataset
•Recommendations and conclusions
UCONN - OPIM 5671 - TEAM 7 2

3. Introduction
•The data contains information from inspections of Restaurants and other food establishments in
Chicago.
•The records are accumulated over the period of 6 years starting from Jan 2010 till date.
•The inspection is done over 127,000 different food establishments in Illinois and neighboring
cities.
UCONN - OPIM 5671 - TEAM 7 3

4. Business Objectives
• Analyze the basic statistics and general trend of food inspections.
• What are the main factors a food inspection leads to a failed result?
• What are the basic standards that must be taken care by restaurants?
• What are the rare things on which a inspection has failed in previous instances?
Come up with a recommendation system which will guide Restaurants and
other food establishments on what to do and what not to do.
UCONN - OPIM 5671 - TEAM 7 4

5. Dataset
Dataset contains information from inspections of restaurants and other food establishments in
Chicago. It consists of:
 127,000 observations
17 variables
 Inspection type – 106 types : Canvass, License, Reinspection, Complaint etc
 Facility type – 425 types : Restaurant, Grocery store, School cafeteria
 Risk : 3 types: High, Medium, low
 Inspection date : Jan 2010 to May 2016
 Violation : Comments on the results of the inspection
 Result : Pass, Fail, Pass w/ Conditions, Out of Business, Not ready
UCONN - OPIM 5671 - TEAM 7 5

6. Visualizations of data
 Data is for Chicago and Suburbs as see by
mapping the Geo information.
 Count of Violations is correlated to Total
number of inspections over time.
UCONN - OPIM 5671 - TEAM 7 6

7. FORECASTING
UCONN - OPIM 5671 - TEAM 7 7

8. PASS vs FAIL INSPECTIONS
• FAIL result is slightly decreasing from 2010.
• Number of PASS inspection is increasing
from 2013.
• Number of Inspections are more in March.
So, Pass and Fail are more.
• In July, percentage of the inspections are
getting passed are lower compared to any
other month.
• There may be a seasonality in month of July.
UCONN - OPIM 5671 - TEAM 7 8

9. Forecasting of % of Passed Inspections
Forecasting of Pass percentage. Our best forecast is the Pass Percentage will
continue in the range of 67.5% to 75% over the
next 12 months.
This is a narrower range than before and indicates
better compliance from restaurants as well as
increasing maturity and predictability in
inspection process.
Best Model was a Seasonal Exponential
Smoothing Model.
Though data indicated possible existence of
Intervention, this could not be modelled.
Final model didn’t pass Ljung Box test.
UCONN - OPIM 5671 - TEAM 7 9

10. Forecasting of % of Failed Inspections
Our best forecast suggests the Fail Percentage
will be in the range of 16% to 20% over the next
12 months.
Seasonal variations will exist as in the past with
failures peaking in May and October.
This is a narrower range than before and
indicates levelling off of the fail percentage. This
is showing increasing maturity and predictability
in inspection process.
Forecasting of Fail percentage
UCONN - OPIM 5671 - TEAM 7 10

11. Forecasting of % of Failed Inspections
Note: The best Model was a Seasonal
Exponential Smoothing Model.
This model was a stationary model with
sinusoidal errors, insignificant auto-correlations
and errors are white noise.
Model Details for forecasting Fail percentage
UCONN - OPIM 5671 - TEAM 7 11

12. TEXT ANALYTICS
UCONN - OPIM 5671 - TEAM 7 14

13. Factors involved in food inspection
• Rat or Insect Exterminators
• Plumbing work
• Temperature of Food
• Denatured food items
• Mouse droppings
UCONN - OPIM 5671 - TEAM 7 15

14. Text Rule Builder Results
• The rules are key terms that
were identified to be significantly
associated with a particular level
of target variable.
• The second rule in the table is
extracted using the documents
that were not satisfied with the
first rule. Similarly, the third rule
is extracted using documents
that were not covered by the first
two rules.
UCONN - OPIM 5671 - TEAM 7 16

15. Interactive Filter Viewer
“Rodent proof door. Recommend to have a pest control operator”
“Must rodent proof door to be tight fitting”
UCONN - OPIM 5671 - TEAM 7 17

16. Passed vs Failed inspection
Not a single word that occurs more in passed inspection compared to failed inspection types.
Analyzed text data using SAS enterprise miner and python NLTK
UCONN - OPIM 5671 - TEAM 7 18

17. Trivial factors for failed inspections
UCONN - OPIM 5671 - TEAM 7 19

18. Interesting details
• Risk: High
• Inspection Type: Complaint / Canvas
• Results: FAIL
• Violations:
1.Noted plumbers potty inside compartment sink
2.Toliets were not flushed by restaurant staff and were
maintained in very bad condition.
3.Toilet rooms were used to store meat.
On an Average, 60 times inspections have failed in
Subway and 97% of them are Risk 1(High) violations.
• Risk: High
• Inspection Type: Complaint
• Results: FAIL
• Violations: 1. Rust observed in kitchen utensils.
2. 50 mice droppings observed in
cooking area.
On an average 50 failed food inspections are
reported in Chicago every year in McD’s.
Approximately 30% of the food inspection fail.
UCONN - OPIM 5671 - TEAM 7 20

19. Recommendation
Maintaining cleanliness
◦ Rodent proof entrances
◦ Keeping and using exterminators regularly
◦ Maintaining Garbage facilities
Following Rules
◦ Having certified staff in place
◦ Keeping written logs
◦ Keeping foods at appropriate temperatures
Facilities & Appliances
◦ Plumbing work
◦ Sanitation facilities
◦ Maintaining Refrigerators and purifiers
Quality control
◦ Take regular feedback from customers to mitigate
complaint based inspections.
◦ Hazardous food and non-food items should be used
with at most care.
◦ Kitchen, sanitation & plumbing facilities have to be
checked & repaired immediately if needed.
UCONN - OPIM 5671 - TEAM 7 21

20. Thank You
UCONN - OPIM 5671 - TEAM 7 22