Wine_Quality_Prediction

Thesis for Mathematics in Machine Learning course at @Politecnico di Torino.
The work aims to apply the theoretical knowledge acqired during the course on a real case. The analysis and the experiments are performed on UCI Machine Learning Wine Quality Data Set, that can be found at: https://archive.ics.uci.edu/ml/datasets/Wine+Quality . The dataset contains red and white variants of the ”Vinho Verde” wine. The data were collected from May/2004 to February/2007.

Main goals

Starting from two different sample sets - one focused on red wines and the other one on white wines - binary classification was performed, having wines’ final quality as target variable (Good or Bad wine quality).
Before applying the classification algorithms, the data was preprocessed in different steps:

1. Feature scaling: data was standardized, according to the estimates of mean and standard deviation computed with the bootstrap method;
   
2. Dimensionality Reduction with PCA to decrease the number of features describing the dataset, retaining at least 90% of its cumulative variance;
   
3. Oversampling with ADASYN to balance the White Wine data set, which were imbalanced on the positive class.
   

In order to find the best hyperparameters for each classification algorithm, a grid search with 5-fold cross validation was applied. Models were trained on both oversampled and non-oversampled training sets. Results were evaluated in terms of accuracy and F1-score. The proposed methods were:

1. Logistic Regression;
   
2. Support Vector Machines.
   

Subsequently, regression was performed to predict wines' quality over all possible output values.
Before applying the regression models, outliers were found and dropped in order to achieve a better result.
The proposed methods were:
1. Linear Regression;
2. Polynomial Regression;
3. Ridge Regression;
4. Lasso Regression.