Progetto svolto in collaborazione con Alessandro Vavalà e Andrei Sauca

Data Mining: Foundations

Tasks of the project:

1. Data Understanding: Explore the dataset with the analytical tools studied and write a concise “data understanding” report describing data semantics, assessing data quality, the distribution of the variables and the pairwise correlations. (see Guidelines for details)
2. Clustering analysis: Explore the dataset using various clustering techniques. Carefully describe your's decisions for each algorithm and which are the advantages provided by the different approaches. (see Guidelines for details)
3. Classification: Explore the dataset using classification trees. Use them to predict the target variable. (see Guidelines for details)
4. Association Rules: Explore the dataset using frequent pattern mining and association rules extraction. Then use them to predict a variable either for replacing missing values or to predict target variable. (see Guidelines for details)