Publications by Kacper Gruca
ML - German_Credit_Risk
Introduction Classification problem The main goal of this paper is to apply ML methods to classify clients as “good” or “bad”. We base on the dataset delivered by our lecturer. The dataset contains additional 10 “mysterious” variables (feat01 - feat10) generated by him. While the exact details of how these variables were generated are n...
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ML - German_Credit_Risk
Introduction Classification problem The main goal of this paper is to apply ML methods to classify clients as “good” or “bad”. We base on the dataset delivered by our lecturer. The dataset contains additional 10 “mysterious” variables (feat01 - feat10) generated by him. While the exact details of how these variables were generated are n...
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MC Simulation - put down and out option
Introduction The purpose of this document is to present the results of the function getPutPrice from custom-made putOptionPricer (.Rcpp) package to calculate the theoretical value of put down and out option with a barrier active between the moment of pricing and the option expiry. The put down and out option means that the barieer is set below the ...
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Clustering - Cryptocurrencies
Introduciton The main goal of this paper is to perform clustering methods on the first 200 cryptocurrencies ranked by market cap. Clustering is a machine learning technique used to group similar objects or data points together into distinct subsets or clusters based on their similarities or dissimilarities. The goal of clustering is to divide a...
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Association Rules - Groceries Market Basket
Introduction The main goal of this paper is to perform the basket analysis using association rules. Association rules are a type of rule-based model used in data mining and machine learning to identify patterns and relationships between variables in a dataset. The goal of association rule analysis is to discover frequent patterns or association...
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Dimension Reduction - Housing market data
Introduction The aim of this article is to apply PCA algorithm for dimension reduction on housing market data. PCA stands for Principal Component Analysis, and it is a statistical technique used for analyzing datasets in order to identify patterns and relationships between variables. The goal of PCA is to reduce the dimensionality of a dataset b...
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