Project developed for the undergraduate elective course "Data mining and Machine Learning" at CEID.
Dataset: winequality-red.csv
- Suport Vector Machines (SVM)
- Missing data handling
- Drop column
- Fill NaN values with column average
- Logistic Regression imputation
- Imputation based on K-means
Evaluation metrics: f1 score, precision, recall and accuracy
Dataset: onion-or-not.csv
- Data preprocessing (NLTK)
- Word tokenizer
- Stemming
- Stopwords removal
- Tf-idf matrix
- Neural network (Tensorflow keras)
Evaluation metrics: f1 score, precision, recall and accuracy
- Zisis Stylianos Tramparis
- Romanos Kapsalis