Here in this guide, let’s try and predict the very quality of the wine based on the given features. The dataset has the key features that are very much responsible for influencing the quality of a wine. Here we’ll only deal with a white kind of wine quality; the classification technique is used to check further the very quality of bad or good wine.
The dataset description:
Well, in the dataset, the classes are generally ordered. However, it wasn’t balanced. Here, the red wine cases are higher, and the white wine instance is less than the red.
Listed below are the names of attributes from a dataset -:
- The type
- The fixed acidity
- The volatile acidity
- The citric acid
- The residual sugar
- The chlorides
- Free Sulfur dioxide
- The total sulfur dioxide
- The density
- The ph
- The sulphates
- The alcohol
- The quality
Preparing datasets for machine learning projects feature variables
At this point, one will felt that he’s ready to go on and prepare datasets for machine learning projects. The very first thing, one would go on to standardize the data. Standardizing data usually means that it’ll go on to transform data so that the distribution will likely have a mean of zero and the standard deviation- 1.
It is crucial to standardize the data to equalize the series of data. For instance, imagine the dataset with a couple of input features: the height in mm and the weight in terms of pounds. Because values of the ‘height’ are much higher because of its measurement, the greater stress will be placed automatically on height when compared to weight, making a bias.
