psprao95 / Wine-Classification

Python | Tensorflow | A neural network for classifying the wine dataset from the UCI Machine Learning Repository

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Overview

The wine dataset contains the results of a chemical analysis of wines grown in a specific area of Italy. Three types of wine are represented in the 178 samples, with the results of 13 chemical analyses recorded for each sample. The Type variable has been transformed into a categoric variable.

The data contains no missing values and consists of only numeric data, with a three class target variable (Type) for classification.

Format

A data frame (csv file) containing 178 observations of 13 variables.

Type The type of wine, into one of three classes, 1 (59 obs), 2(71 obs), and 3 (48 obs).

The attributes are:

  1. Alcohol
  2. Malic acid
  3. Ash
  4. Alcalinity of ash
  5. Magnesium
  6. Total phenols
  7. Flavanoids
  8. Nonflavanoid phenols
  9. Proanthocyanins
  10. Color intensity
  11. Hue
  12. OD280/OD315 of diluted wines
  13. Proline

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Python | Tensorflow | A neural network for classifying the wine dataset from the UCI Machine Learning Repository


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