Path-dependent option pricing with Monte Carlo and Rcpp package

In the following paper, with the help of association rules (Apriori and ECLAT algorithms) I will try to discover the relationships between shopping baskets and indicate which products imply the purchase of other products.

In this paper, I will perform dimension reduction using Principal component analysis (PCA).

In this paper I will calculate the percentage of Warsaw’s green space. In order to do that, I will use unsupervised machine learning algorithm - Clustering Large Applications algorithm. With a help of CLARA algorithm, I will do an image clustering to simplify the given image (reduce the number of colors). Then, I will calculate the share (percentage) of each color, so does the size of Warsaw’s green space. The idea is that different urban space objects have different colors, so that they will be assigned to different groups. Therefore, buildings, roads or other artificial urban facilities will be assigned to one group, when forests, parks, trees and lawns to another.