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In this article, we are talking about wine Quality prediction models. Here we will give you complete information about the Wine Quality prediction model.
Older wine tastes better compared to new wine. However there are several features other than the age of wine into quality certification including the physiochemical test like alcohol quantity, volatile acidity, determination of density, pH,fixed quantity sulphate. Chloride etc.
Project Idea
The aim of this machine learning project is to build a machine learning model to predict the quality of wines by exploring the various chemical properties available in wine. After data visualisation technique or data plotting that will serve as an input to a machine learning model.
Dataset
To build the wine quality prediction model we have used a bank loan dataset. The data file winequality-red.csv contains the information used to create the model. It consists of 1599 rows and 12 columns. The columns represent the variables, while the rows represent the instances.
The Dataset is composed of four concepts.
Data source
Variables
Instances
Missing values
This dataset uses the following 9 variables:
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 flavour to wines
residual sugar : The amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/litre and
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); most wines are between 3-4 on the
Sulphates : a wine additive which can contribute to sulphur dioxide gas (S02) levels, which acts as an antimicrobial and
Alcohol : The percent alcohol content of the wine
Quality : output variable (based on sensory data, score between 0 and 10)
In our explanation video of Data-driven Wine Quality Prediction model using python, We cover techniques of exploratory analytics, data cleansing, and model building and evaluation. We utilised Random Forest Classifier, Support Vector Machine and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.
The Wine Quality Prediction Project is described in two videos part 1 and part 2.
Part 1: Title : PREDICTING WINE QUALITY Project Part 1 | AI ML Project Series
Description : This is the introduction part of PREDICTING WINE QUALITY Project where we provide the details and procedures of the coming project that we will build in Part2 of this Series. This is based on analysis of wine quality based on content or ingredients of the red wine provided such as sulphides, chlorides, alcohol, pH, citric acid etc. and categorise whether the wine is commercially good or not in terms of quality. The result will be that we will be able to analyse the quality of wine with all sets of various values using AI and know whether or not the product is high in quality.
Part 2 : Title : PREDICTING WINE QUALITY Project Part 2 | AI ML Project Series
Description : This is the second part of the PREDICTING WINE QUALITY Project where we create a complete project on Kaggle Community Platform regarding prediction of Quality of red wine based on various materials used to create the product. We use data cleaning, data plotting and utilised Random Forest Classifier, Support Vector Machine and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.
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