Food Inspections in Chicago, IL

UCONN - OPIM 5671 - TEAM 7 1

Food Inspections TEAM 7

Vibhor KaushikPavan EbbadiManas KarYifei Tang


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


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.


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.


Dataset Dataset contains information from inspections of restaurants and other food establishments in Chicago. It consists of:

127,000 observations17 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


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.


FORECASTING


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.


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.


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


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


TEXT ANALYTICS


Factors involved in food inspection

• Rat or Insect Exterminators

• Plumbing work

• Temperature of Food

• Denatured food items

• Mouse droppings


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.


Interactive Filter Viewer

“Rodent proof door. Recommend to have a pest control operator” “Must rodent proof door to be tight fitting”


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


Trivial factors for failed inspections


Interesting details• Risk: High• Inspection Type: Complaint / Canvas• Results: FAIL• Violations:

1.Noted plumbers potty inside compartment sink2.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.


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.


Thank You

Food Inspections in Chicago, IL

Data & Analytics

Transcript of Food Inspections in Chicago, IL