Wine Quality Predictor Server

Backend server for the Wine Quality Predictor.

Table of contents

• About the project
  • Dataset
  • Prediction Model
  • Tools
• Requirements
• Install dependencies
• Run dev server
• API
  • Get a prediction

About the project

Note: Be aware this was a one-weekend project I built back in 2018.

The goal is to predict the sensorial quality of a wine base on its physicochemical qualities

Dataset

The dataset was downloaded from Kaggle's Red Wine Quality dataset.

The dataset contains the following data for each row.

Feature	Description
Fixed acidity	Most acids involved with wine or fixed or nonvolatile (do not evaporate readily).
Volatile acidity	The amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste.
Citric acid	Found in small quantities, citric acid can add 'freshness' and flavor to wines.
Residual sugar	The amount of sugar remaining after fermentation stops.
Chlorides	The amount of salt in the wine.
Free sulfur dioxide	The free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents.
Total sulfur dioxide	Amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2.
Density	The density of water is close to that of water depending on the percent alcohol and sugar content.
PH	Describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic).
Sulphates	A wine additive which can contribute to sulfur dioxide gas (S02) levels.
Alcohol	Percentage of alcohol in the wine.

Prediction Model

WIP

Classification Algorithm: Support Vector Clustering (SVC)

Other algorithms considered:

• Random Forest

Tools

Model

• Scikit-learn
• Pandas
• Numpy

Server

• Flask

Requirements

• Python 3.x

Install dependencies

Create virtual environment.

python3 -m venv env

Activate virtual environment.

source env/bin/activate

Install dependencies.

python3 -m pip install -r requirements.txt

Run dev server

Export app name.

export FLASK_APP=server

Start server at default port.

flask run

API

Get a prediction

POST /predict

Field	Type	Description
values	Array<Number>	Model input/Descriptor. This is an array of 11 number.

Example JSON Body

{
  "values": [8, 0.5, 0.2, 2.5, 0.08, 15, 46.5, 0.999, 3.3, 0.65, 10]
}

Example response

{
  "predicted": 6
}