Course Project with Shiny, Developing Data Products

Arif Arshad
August 25, 2017

*“There are thousands of wines that can take over our minds.

Don't think all ecstasies are the same!”*

Rumi (trans. Coleman Barks), *The Essential Rumi*

My Project

All wines are not alike. They differ according to many features. I developed an app called the “Wine Quality Predictor” in order to both predict the quality of wine on the basis of its ingredients and learn what ratios and proportions of different ingredients go into good wine.

Data Sources

I took the data from the following site to train my predictive model.
The data can also be found at the UIC Machine Learning Repository at https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/. Response (Factor) Quality

Predictors (Numeric) Fixed Acidity, Volatile Acidity, Citric Acid, Residual Sugar, Chlorides, Free Sulfur Dioxide, Total Sulfur Dioxide, Density, pH, Sulphates, and Alcohol

The App

The app can be found at https://arifarshad74.shinyapps.io/cp3-ddp/. The code for the ui and server files can be found at https://github.com/arifarshad/CP3-DDP.

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