Chicago Food Inspection Forecasting

One of the biggest challenges The City of Chicago - Department of Public Health faces is keeping up with the the sheer volume of food establishments across the city that require sanitation inspection. The goal is to make that inspection prior to critical health violations.

There are just three dozen inspectors responsible for inspecting these 15,000 establishments on regular basis, amounting to ~420 establishments per person. Given the large number of inspections to be completed, the time and effort it takes to discover critical violations can mean prolonged exposure to potential disease, illness, and unsanitary conditions at some food establishments. This necessitates identifying of establishments that could likely be making a critical violation and the number of days before an inspection has to be made in those establishments.

The data for this forecast is taken from The City of Chicago’s GitHub location. On top of the basic models provided by them, I developed Logitic Regression, XGBoost, Extra Trees, and GBM predictive models. Using ShinyApps I created visuals to show the performance of each model and the number of days before an establishment has to be inspected.

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Housing Loan Approval System

Using ShinyApps, I developed an interactive, end-to-end application that predicts whether a housing loan can be approved (or not) for customers when they apply for a loan at a financial institution. To predict the status, the customer’s basic personal and financial information are collected and fed into the system.

Based on the existing dataset, the system trains 5 different models and predicts the loan status of a new customer for each model. The final loan status will be determined by the majority ranking of all the 5 models.

Click here to look at this application in full screen. To have a quick look, scroll below: