Task $1$: Considering the array

$\backslash$table$[[,$Column $1,$Column $2,$Column $3],[$Row $1,8,7,6],[$Row $2,3,9,5],[$Row $3,8,7,6]]$

Write the code for the following points using python and present the output

print the array elements with the columns names

print the number of records and attributes of the array

print the first row and the last column of the array

print the cell in the second row and third column

perform a visualization of the array data in line and scatter plotting

Task $2$: Considering the data file $($iris$.$csv$)$

Write the code for the following points using python and present the output

Print the rows from $50$ to $70$

Print the records for "Iris$-$versicolor" Species class

Plot the records with the relation between PetalLengthCm and PetalWidthCm

Task $3$:

The cancer data set has $100$ records, $80$ of these records are cancer records. The model was able to predict $65$ out of the $80$ records as cancer records correctly. Moreover, $12$ of the noncancer records are also predicted correctly as non$-$cancer.

Build the confusion matrix, and calculate the model accuracy, precision, and recall.