Tony AntoniosBarotsis

## rustfmt.toml
tab_spaces = 2
edition ="2021"

## rss2email_msrv.json
{"schemaVersion":1,"label":"MSRV","message":"1.71.1","color":"32ca55","labelColor":"353d46"}

## config.toml
# add the below section to `.cargo/config.toml`

[target.'cfg(all())']
rustflags = [
    # BEGIN - Embark standard lints v6 for Rust 1.55+
    # do not change or add/remove here, but one can add exceptions after this section
    # for more info see: <https://github.com/EmbarkStudios/rust-ecosystem/issues/59>
    "-Dunsafe_code",
    "-Wclippy::all",
    "-Wclippy::await_holding_lock",

## findWifiPass.ps1
netsh wlan show profile [NETWORK NAME] key=clear | findstr "Key Content"

## kmeans_elbow.R
set.seed(1234)
wcss <- vector()

for(i in 1:10) {
  wcss[i] = sum(kmeans(X, i)$withinss)
}

plot(1:10, wcss, type = 'b',
     main = paste('Clusters of <data>'),
     xlab = 'Number of Clusters',

## backward_elimination.R
# Automatic backward elimination
backwardElimination <- function(x, sl) {
  # Number of features
  numVars = length(x)
  for (i in c(1:numVars)){
    # Train regressor on all (remaining) features
    regressor = lm(formula = Profit ~ ., data = x)
    # Query all the p-values and extract the largest one
    maxVar = max(coef(summary(regressor))[c(2:numVars), "Pr(>|t|)"])
    # If that value is greater than the significance level (SL) then drop the feature
	# add the below section to `.cargo/config.toml`

	[target.'cfg(all())']
	rustflags = [
	# BEGIN - Embark standard lints v6 for Rust 1.55+
	# do not change or add/remove here, but one can add exceptions after this section
	# for more info see: <https://github.com/EmbarkStudios/rust-ecosystem/issues/59>
	"-Dunsafe_code",
	"-Wclippy::all",
	"-Wclippy::await_holding_lock",
	set.seed(1234)
	wcss <- vector()

	for(i in 1:10) {
	wcss[i] = sum(kmeans(X, i)$withinss)
	}

	plot(1:10, wcss, type = 'b',
	main = paste('Clusters of <data>'),
	xlab = 'Number of Clusters',
	# Automatic backward elimination
	backwardElimination <- function(x, sl) {
	# Number of features
	numVars = length(x)
	for (i in c(1:numVars)){
	# Train regressor on all (remaining) features
	regressor = lm(formula = Profit ~ ., data = x)
	# Query all the p-values and extract the largest one
	maxVar = max(coef(summary(regressor))[c(2:numVars), "Pr(>\|t\|)"])
	# If that value is greater than the significance level (SL) then drop the feature