Predicting wine quality with several classification techniquesYou need machine learning testing to define the quality of the wine. High-quality wine testing analysis can be done through a machine learning process to arrive on better results.
Introduction
It requires some machine leaning adaptation to know the difference between a good wine and a bad one. While some people can boast of their taste and can certainly tell the difference just by tasting it, with machine learning one can arrive on the best results. The quantity of the wine is generally decided by 11 input variables.
- Fixed acidity
- Volatile Acidity
- Citric Acid
- Residual Sugar
- Chlorides
- Free Sulphur Dioxide
- Total Sulphur Dioxide
- Density
- pH
- Sulphates
- Alcohol
Objectives
The objectives of the test to determine the quality of the wine are as follows
The experiment the different classification methods to see which test yields higher accuracy.
To determine the features which are indicative of good quality wine?
Setup
The test starts with importing all the relevant libraries that will be used as the data. It is best to get a better idea of what you’re working with to understand the data and its flow. The data at the earlier set is beginner friendly and you might have to deal with missing values if the data input is not done correctly. You need some flexibility to conduct the feature engineering given on the variables.
Exploring variables
Histogram of quality variable
Start with a quality variable. Make an accurate distribution of the quality variable on a histogram. You need to have enough good quality wine in your datasheet to have a good comparison. The test will help you arrive at what we mean by ‘good quality’ wine.
Correlation Matrix
You also need a correlation between the variables you are working with to sketch a graph for ease of understanding. It will allow you to have a quick glimpse and a better understanding of the variables you are working with. There are many correlation factors linked to quality. It is also an important feature of the machine learning model.
Conversion to a classification problem
We must not forget the main agenda which is to compare the effectiveness of different classification techniques. The output variable thus needs to be changed to a binary output. You need to give the scoring system to the bottles of wine and a score above 7 means good wine, the higher you go on the score the better the wine is.
The proportion of good wines to bad wines
You also need to compare the samples based on total samples you tested. It is important to know how many good quality wines you arrived on in comparison to the total number of bottles that are tested.
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