gender_career_classifier

interactive-kinect-memory-game

Planetary themed memory game: Whole Body interaction with Kinect

it-academy

Notebooks from IT academy Data science course.

predictive_allostasis

In this work, we simulate competing emerging needs such as thirst and internal temperature by adding a feedforward module (Allostasis), responsible for the predictive behavior of a simulated agent over an already existing model of reactive homeostasis, in which the agent is placed within an environment of constantly changing temperatures.

regression_models

Comparison between multilinear regression model and random forest on a regression task to predict housing prices.

sentiment_analysis

Sentiment analysis exercise using Natural Language Toolkit

supervised_learning.knn_and_randomforests

Notebook on classification task between RF & KNN

unsupervised_learning_algorithms.k-means_and_hclustering

valeorozco

Valeria Orozco Portfolio

web_scraping

Web scraping exercises using BeautifulSoup and Selenium libraries

why-people-are-reluctant-to-tempt-fate-replication-of-study-6-and-extension

The present research focused on the replication of the sixth experimental study (Study 6) from the paper “Why People are reluctant to tempt fate”, published by authors Risen and Gilovich in 2008. The replication was developed with the aim of supporting previous results from the authors that had revealed the dual thinking process (System 1 and System 2) to be the cause of the belief that it is bad luck to “tempt fate”. A higher perception in the likelihood of a negative outcome had been predicted when the participants were under cognitive load while performing the task, in other words, when System 2 was loaded. A close replication of the study was made, and cognitive load did not show to have an effect on the perceived likelihood of a negative outcome after tempting fate. Having failed to replicate the results of the original study, a further methodological extension was proposed. The extension of the study followed the same methodology proposed by the authors, with three modifications: (1) two new variables were added (Importance and Relevance) to control if whether the participants cared or not about the tempting fate scenario of the student, (2) changes in sampling, and (3) the method by which participants were loaded cognitively, which will be described in detail in the method section. The results showed that participants who had rated high in Importance (i.e. those who would have felt bad had they been called by the professor without having read the required reading), when loaded cognitively, had a greater perception of a negative outcome. This is regardless of the condition of tempting fate or not, which is similar to a replication done by Maya Matur in 2016. The other main effect from the original study (tempting fate has an effect on perception of negative outcomes) and the interaction between cognitive load and tempting fate could not be replicated.

wine-classifier-binary-and-multiple-classification-problems.

Supervised learning for wine classification: Use of SKLEARN to solve a binary and multi-class classification problem related to wine type and quality.