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As Jay s wealth grows he has taken interest in the more finer things in life. Wine is one of them. He enjoys drinking wine
As Jays wealth grows he has taken interest in the more finer things in life. Wine is one of them. He enjoys drinking wine so much that he has decided to buy a vineyard. He currently is looking at different vineyards that are for sale and will decide to purchase one.
To decide which vineyard to purchase we need to know the price, size and quality of the wine that it produces. The size and price are easy to figure out. Quality is another story. Figuring out the quality of wine is not easy.
Here is what we are going to do We have a wine data set. In the data set we know attributes about almost wines. These are easy to measure, like pH level. There is a simple test for that. The people that made the data set hired experience wine judges to judge each wine. Their quality score from to is in column
Fixed acidity
Volatile acidity
Citric acid
Residual sugar
Chlorides
Free sulfur dioxide
Total sulfur dioxide
Density
pH
Sulphates
Alcohol
Quality this is the value that we are trying to predict.
Before we try to create a wine classification solution, we see a problem with the data. The values are very different ranges and scales. For example Chlorides is normally less than while Total sulfur dioxide is normally over
You and Bill decided that you need some preprocessing of the data. For each column you would like the minimum value to scale to and the maximum value scale to For example, lets say that the values in a column of data are is the smallest, that stays is the largest, that becomes Your scaled data would be
Instructions
Create a Python program to load the wine quality data file. Scale all of the columns to be scaled values between and Then print a few lines of the original data, followed by the scaled data. You must use Scikit Learn; you cannot just hand write a bunch of Python code to do it
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