Nama : Ghozzy Averillio Purwana NPM : 140610230084

Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, your goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. More information is available from the website here: http://groupware.les.inf.puc-rio.br/har

PROGRES TUBES ARW

Datacamp

This projectclassifies BBC news articles into one of five categories automatically: - **Business** - **Entertainment** - **Politics** - **Sport** - **Tech** ## Methodology We will employ **matrix factorization** techniques, specifically **Non-negative Matrix Factorization (NMF)**, to: 1. Extract meaningful features from raw text documents 2. Reduce dimensionality while preserving important information 3. Build predictive models for article classification 4. Compare unsupervised (NMF) and supervised learning approaches

psych251_whitman_problem_set_3 Group B choice 3