Wine Classification and Forecasting
This work is about wine.csv (found in the UCI Machine Learning Repository) which contains the results of the chemical analysis of 174 Italian wines from three known cultivars (in this context, a cultivar is a set of grapes selected for desirable characteristics that can be maintained through propagation).
By using different techniques to achieve the goal, it was possible to infer that, in a near future, the technique I will apply first in similar situation will be the decision tree since the resulting tree was simple to understand and interpret which brought great advantages because, by mere observation, I was able to easily conclude which variables are important to this classification problem and to understand how the classification was made. Moreover, it was a quick technique to perform / had a lower cost and the model obtained through this technique was better.
