Introduction Data Analysis and Classification on a subset of data about polls for the 2006 and 2010 elections in Brazil for the “Câmara Federal de Deputados”. Data was taken from the TSE portal which originally encompassed approximately 7300 candidates. Data Overview The variables The response variable is the variable that you are interested in reaching conclusions about. A predictor variable is a variable used to predict another variable.

Introduction Data Analysis with multivariate Linear Regression on data about polls for the 2006 and 2010 elections in Brazil for the lower house (Câmara Federal de Deputados). Data was taken from the TSE portal and encompasses approximately 7300 candidates. Data Overview The variables The response variable is the variable that you are interested in reaching conclusions about. A predictor variable is a variable used to predict another variable.

Introduction Data Analysis with multivariate Linear Regression on data about polls for the 2006 and 2010 elections in Brazil for the “Câmara Federal de Deputados”. Data was taken from the TSE portal and encompasses approximately 7300 candidates. Data Overview Loading Data eleicoes_data <- readr::read_csv( here::here('evidences/eleicoes_2006_e_2010.csv'), progress = FALSE, local=readr::locale("br"), col_types = cols( ano = col_integer(), sequencial_candidato = col_character(), quantidade_doacoes = col_integer(), quantidade_doadores = col_integer(), total_receita = col_double(), media_receita = col_double(), recursos_de_outros_candidatos.

Introduction This exploratory data analysis was made based on data provided by the Brazilian government about the expenses allowed to its parliamentarians or C.E.A.P. (Cota para o Exercício da Atividade Parlamentar / Quota for the Exercise of Parliamentary Activity). More information about it (in Portuguese) can be found in its official site Data Overview data <- read_csv(here::here("evidences/dadosCEAP.csv"), progress = F, col_types = cols( nomeParlamentar = col_character(), idCadastro = col_integer(), sgUF = col_character(), sgPartido = col_character(), tipoDespesa = col_character(), especDespesa = col_character(), fornecedor = col_character(), CNPJCPF = col_character(), tipoDocumento = col_integer(), dataEmissao = col_character(), valorDocumento = col_double(), valorGlosa = col_integer(), valorLíquido = col_double())) data %>% mutate(dataEmissao = parse_date_time(dataEmissao,"%Y-%m-%d %H:%M:%S"), year_month = paste(lubridate::year(dataEmissao), # extract year lubridate::month(dataEmissao), # extract month sep = "-"), tipoDespesa = toupper(tipoDespesa)) -> data state_info <- read_csv(here::here("/evidences/limiteMensalCEAP.

Introduction This exploratory data analysis was made based on data provided by the Brazilian government about the expenses allowed to its parliamentarians or C.E.A.P. (Cota para o Exercício da Atividade Parlamentar / Quota for the Exercise of Parliamentary Activity). More information about it (in Portuguese) can be found in its official site Data Overview data <- read_csv(here::here("evidences/dadosCEAP.csv"), progress = F, col_types = cols( nomeParlamentar = col_character(), idCadastro = col_integer(), sgUF = col_character(), sgPartido = col_character(), tipoDespesa = col_character(), especDespesa = col_character(), fornecedor = col_character(), CNPJCPF = col_character(), tipoDocumento = col_integer(), dataEmissao = col_character(), valorDocumento = col_double(), valorGlosa = col_integer(), valorLíquido = col_double())) data %>% mutate(dataEmissao = parse_date_time(dataEmissao,"%Y-%m-%d %H:%M:%S"), year_month = paste(lubridate::year(dataEmissao), # extract year lubridate::month(dataEmissao), # extract month sep = "-"), tipoDespesa = toupper(tipoDespesa)) -> data state_info <- read_csv(here::here("/evidences/limiteMensalCEAP.

This report contains regression models created based on data describing 5000 speed dates of 4 minutes of duration involving 310 american young adults. The original data were collected by Columbia Business professors. Further information and the data itself can be found in this report repository. Data Overview The variables The response variable is the variable that you are interested in reaching conclusions about.

This report contains regression models created based on data describing 5000 speed dates of 4 minutes of duration involving 310 american young adults. The original data were collected by Columbia Business School professors. Further information and the data itself can be found in this report repository. Data Overview The variables The response variable is the variable that you are interested in reaching conclusions about.

Introduction This report is an analysis on the dataset movielens which can be found in full here. The code, data and a description of the variables used in this report can be found in the original repository Data Overview readr::read_csv(here::here("evidences/lens_movies.csv"), progress = FALSE, col_types = cols( movieId = col_integer(), title = col_character(), genres = col_character() )) %>% group_by(movieId) %>% mutate(year = as.

Introduction The code and data employed here can be found at the original repository. The data employed on this report is a sample of the commits made on some of the repositories on Github each day from 2016 to 2017. Data Overview readr::read_csv(here::here("evidences/github-users-committing-filetypes.csv"), progress = FALSE, col_types = cols( file_extension = col_character(), month_day = col_integer(), the_month = col_integer(), the_year = col_integer(), users = col_integer() )) -> data data %>% glimpse() ## Observations: 13,802 ## Variables: 5 ## $ file_extension <chr> "md", "md", "md", "md", "md", "md", "md", "md", "… ## $ month_day <int> 18, 17, 27, 16, 26, 21, 4, 22, 23, 1, 12, 3, 2, 2… ## $ the_month <int> 2, 2, 1, 2, 1, 3, 2, 2, 2, 2, 4, 2, 2, 2, 4, 3, 4… ## $ the_year <int> 2016, 2016, 2016, 2016, 2016, 2017, 2016, 2016, 2… ## $ users <int> 10279, 10208, 10118, 10045, 10020, 10015, 9991, 9…

Introduction Employed data, scripts and a brief description can be found at the original repository. Further references can be found at the page of Prof. Ian Watson. library(FNN) library(here) library(magrittr) library(tidyverse) source(here::here("code/calc_KNN_error.R")) theme_set(theme_bw()) Data Overview Data has the following attributes: Make: Make of the car; Model: Model of the car; Year: Manufacturing Date; Engine.Fuel.Type: Kind of fuel the engine runs on; Engine.