Wine Quality Prediction Project

About wine

• Wine is a beverage made from fermented grape and other fruit juices with lower amount of alcohol content.
• Quality of wine is graded based on the taste of wine and vintage. This process is time taking, costly and not efficient.
• A wine itself includes different parameters like fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulphur dioxide, total sulphur dioxide, density, pH, sulphates, alcohol and quality.

Problem Statement

• In industries, understanding the demands of wine safety testing can be a complex task for the laboratory with numerous analytes and residues to monitor.
• But, our application’s prediction, provide ideal solutions for the analysis of wine, which will make this whole process efficient and cheaper with less human interaction
• Wine Quality Prediction with PCA & LDA Algorithms

Code and Resources Used

• Python Version: 3.7
• Packages: pandas, numpy, sklearn, seaborn, matplotlib

Attributes

• Alcohol
• Malic_Acid
• Ash
• Ash_Alcanity
• Magnesium
• Total_Phenols
• Flavanoids
• Nonflavanoid_Phenols
• Proanthocyanins
• Color_Intensity
• Hue
• OD280
• Proline

Data Handling

• Importing the libraries
• Data Preprocessing
• Importing the dataset
• Feature Scaling
• Splitting the dataset into the Training set and Test set
• Applying LDA or PCA
• Training the Logistic Regression model on the Training set
• Predicting the Test set results
• Visualising the Training set results
• Visualising the Test set results

Conclusion

Visualization using LDA Algorithm

For Training set

For Test Set

Visualization using PCA Algorithm

For Training set

For Test Set