Classification-Based Data Mining Approach for Quality Control in Wine Production

6442 Words Dec 10th, 2012 26 Pages
Classification-Based Data Mining Approach For Quality Control In Wine Production

GUIDED BY: | | SUBMITTED BY: | Jayshri Patel | | Hardik Barfiwala |

INDEX

Sr No | Title | Page No. | 1 | Introduction Wine Production | | 2 | Objectives | | 3 | Introduction To Dataset | | 4 | Pre-Processing | | 5 | Statistics Used In Algorithms | | 6 | Algorithms Applied On Dataset | | 7 | Comparison Of Applied Algorithm | | 8 | Applying Testing Dataset | | 9 | Achievements | |

1. INTRODUCTION TO WINE PRODUCTION

* Wine industry is currently growing well in the market since the last decade. However, the quality factor in wine has become the main issue
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* Free Sulfur Dioxide : Amount of Free Sulfur Dioxide present in wine. (In mg per liter)

* Total Sulfur Dioxide : Amount of free and combined sulfur dioxide present in wine. (In mg per liter) Used mainly as preservative in wine process.

* Density : The density of wine is close to that of water, dry wine is less and sweet wine is higher. (In kg per liter)

* PH : Measures the quantity of acids present, the strength of the acids, and the effects of minerals and other ingredients in the wine. (In values)

* Sulphates : Amount of sodium metabisulphite or potassium metabisulphite present in wine. (In mg per liter)

* Alcohol : Amount of Alcohol present in wine. (In percentage)

* Output variable (based on sensory data)

* Quality (score between 0 and 10) : White Wine : 3 to 9 Red Wine : 3 to 8

4. PRE-PROCESSING

* Pre-processing Of Data Preprocessing of the dataset is carried out before mining the data to remove the different lacks of the information in the data source. Following different process are carried out in the preprocessing reasons to make the dataset ready to perform classification process. * Data in the real world is dirty because of the following reason.

* Incomplete : Lacking attribute values, lacking certain attributes of interest, or containing
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