Classification-Based Data Mining Approach for Quality Control in Wine Production
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 in wine making and selling. * To meet the increasing demand, assessing the quality of wine is necessary for the wine industry to prevent tampering of wine quality as well as maintaining it. * To remain competitive, wine industry is investing in new technologies like data mining for analyzing taste and other properties in wine. * Data mining techniques provide more than summary, but valuable information such as patterns and relationships between wine properties and human taste, all of which can be used to improve decision making and optimize chances of success in both marketing and selling. * Two key elements in wine industry are wine certification and quality assessment, which are usually conducted via physicochemical and sensory tests. * Physicochemical tests are lab-based and are used to characterize physicochemical properties in wine such as its density, alcohol or pH values. * Meanwhile, sensory tests such as taste preference are performed by human experts. Taste is a particular property that indicates quality in wine, the success of wine industry will be greatly determined by consumer satisfaction in taste requirements. * Physicochemical data are also found useful in predicting human wine
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 in wine making and selling. * To meet the increasing demand, assessing the quality of wine is necessary for the wine industry to prevent tampering of wine quality as well as maintaining it. * To remain competitive, wine industry is investing in new technologies like data mining for analyzing taste and other properties in wine. * Data mining techniques provide more than summary, but valuable information such as patterns and relationships between wine properties and human taste, all of which can be used to improve decision making and optimize chances of success in both marketing and selling. * Two key elements in wine industry are wine certification and quality assessment, which are usually conducted via physicochemical and sensory tests. * Physicochemical tests are lab-based and are used to characterize physicochemical properties in wine such as its density, alcohol or pH values. * Meanwhile, sensory tests such as taste preference are performed by human experts. Taste is a particular property that indicates quality in wine, the success of wine industry will be greatly determined by consumer satisfaction in taste requirements. * Physicochemical data are also found useful in predicting human wine