For this project, I used Kaggle’s Wine Quality dataset to build best classification model to predict whether a particular wine is “good quality” or not.The dataset can be found at the UCI Machine Learning Repository. Wines are divided into 2 categories, white wines and red wines. Each wine in this dataset is given a “quality” score between 0 and 10.Such a model can be used not only by the certification bodies but also by the wine producers to improve quality based on the properties and by the consumers to predict the quality of wines.The quality of a wine is determined by 11 input variables:
- Fixed acidity
- Volatile acidity
- Citric acid
- Residual sugar
- Chlorides
- Free sulfur dioxide
- Total sulfur dioxide
- Density
- pH
- Sulfates
- Alcohol
The goal is to explore the Wine Quality dataset in order to extract the main features and characteristics from the data and predict the wine quality. We will consider this problem as a regression task.
- Exploratory Data Analysis
- Data preprocessing
- Quality prediction
Cheers! 🍷
