write a function called task$1($data$,$ filter$\_$value, type$\_$of$\_$card$),$ wheredata contains all records from the dataset and filter$\_$value is an area name and type$\_$of$\_$card is the

name of the card provider. The function should return a list containing values from the following questions.

Return all results rounded to two decimal points.

Input:

cos$\_$dist, var, median, corr, pca $=$ task$1($data$,$ 'Port Lincoln $\text{'},$ 'ANZ $\text{'})$

output:

$[0\mathrm{.}06,1337142\mathrm{.}45,[5\mathrm{.}75,7\mathrm{.}21],-0\mathrm{.}06,[0\mathrm{.}73,0\mathrm{.}81,0\mathrm{.}7,0\mathrm{.}93,0\mathrm{.}72,...]]$

i$.$ cos$\_$dist: Calculate cosine distance between normal and malicious transactions based on

IP$\_$validity$\_$score.

Formula:

$=1$

$(,)=$

Output:

print$($cos$\_$dist$)=0\mathrm{.}06$

ii$.$ var: Filter transactions based on certain geographical area e$.$g$.$ Port Lincoln and calculate and display

the variance of transaction amount for a specific area. Note: use the Actual area column from the

dataset. Use sample variance formula for calculation.

Output:

print$($var$)=1337142\mathrm{.}45$

iii. median: Filter data based on Type$\_$of$\_$card and then calculate the median of

Authentication$\_$score value for transactions that are in the lower $25$th $($inclusive$)$ and upper

$75$th $($inclusive$)$ percentile.

Output:

print$($median$)=[5\mathrm{.}75,7\mathrm{.}21]$

iv$.$ corr: Filter malicious transactions where Actual and Origin places are different. Calculate

elementwise product between Authentication$\_$score and IP$\_$validation$\_$score and then

perform correlation between the resultant vector and Amount column.

Output:

print$($corr$)=-0\mathrm{.}06$

v$.$ pca: Create a N x $5$ matrix where N is number of rows in the dataset and $5$ is the number of

columns, we will call these features $($Transaction$\_$type, Entry$\_$mode, Amount,

Authentication$\_$score, and IP$\_$validity$\_$score$)($before that you need to convert all