This project is a gateway to the machine learning in the 42 school. It will also help you become a master sommelier.
Final mark: 125/100 ✅
ADALINE training animation
Project goal: Given the chemical attributes of a wine, classify it as "good" or "bad".
Allowed libraries: matplotlib, pandas, standard python lbraries.
Not allowed libraries: numpy, scipy, scikit-learn, tensorflow, etc...
Implemented models:
- Perceptron
- ADALINE
Also, as the bonus part, both these models implemented using Cython.
Implemented validations:
- Hold-out validation
- K-fold cross-validation
Other ML stuff:
- Feature scaling
- Scatterplot matrix
Scatterplot matrix of the red wine dataset (showing first three features)