To begin, you need to define a main$($filename$,$ filter$\_$value, type$\_$of$\_$card$)$ function that will

read the dataset and store the transaction records in data and call the below functions to display appropriate

results.

Sample Input:

main$(\text{'}$CreditCard$\_2024\_$Project$2.$csv$\text{'},$ 'Port Lincoln', 'ANZ'$)$

Task $1$: Data Analysis using NumPy Mark: $15$

Answer the following $5$ NumPy related tasks for data analysis. These will require use of NumPy functions and

methods, matrix manipulations, vectorized computations, NumPy statistics, NumPy where function, etc. To

complete this task, write a function called task$1($data$,$ filter$\_$value, type$\_$of$\_$card$),$ where

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data 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 dot

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

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string values to numerical values. You can assume there will always be $3$ Transaction$\_$type and

use the following values $-$ ATM: $1,$ EFTPOS: $2,$ and Internet: $3$ and four Entry$\_$mode $-$ Magnetic

Stripe: $1,$ Manual: $2,$ Chip Card Read: $3,$ and NFC: $4).$ Calculate principal component analysis $($PCA$)$

to reduce the dimensionality of data to N X $1.$

The algorithm for PCA is:

a$)$ Standardize the data along all the features $($subtract mean and divide by standard deviation

over the feature dimension$).$

b$)$ Calculate the covariance matrix for the features

c$)$ Perform eigen decomposition on the covariance matrix to get eigenvectors $($principal

components$)$ and eigenvalues

d$)$ Sort the eigenvectors based on their eigenvalues from highest to lowest

e$)$ Select top k eigenvectors $($k$=1)$

f$)$ Transform the data using the selected eigenvectors $($dot product of eigenvectors and

Standardized data in step a$)$

Output:

print$($pca$.$shape$)=(10000,1)$

print$($pca$[0$:$5])=[0\mathrm{.}73,0\mathrm{.}81,0\mathrm{.}7,0\mathrm{.}93,0\mathrm{.}72]$