Consider the provided dataset that you have contributed. You are required to

submit a report meeting the following criteria:

i$.$ Choose atleast $6$ attributes including $$Study hour$$ and $$Part

time work hour$$ and CGPA which will be the target class.

ii$.$ Make the target class into $4$ categorical value $($i$.$e$.,$ First class:

$3\mathrm{.}00$ to $4\mathrm{.}00,$ Second class: $2\mathrm{.}25$ to $2\mathrm{.}99,$ Third class: $2\mathrm{.}00$ to $2\mathrm{.}24$

and Below $2\mathrm{.}00$ is for Fail$).$

iii. Preprocess the data:

a$.$ Fill null values by statistical analysis

b$.$ Use encoding where necessary to convert nominal value

to numerical value

c$.$ Detect outlier if any

d$.$ Make the data homogeneous $($all values are in the same

format$)$

e$.$ Remove unnecessary data if any

f$.$ Normalze value if necessary

iv$.$ Visualize the data

a$.$ Show the correlation of each selected attribute with the

target attribute

b$.$ Show boxplot for each attributes

v$.$ Use atleast $3$ classification model $($i$.$e$.,$ any variant of Decision

tree out of $3$ types, KNN$,$ Neural Network, Naive Bayes etc.$)$

vi$.$ Show a comparison table among these $3$ models showing

Accuracy, Precision, Recall and F$-1$ Score.

vii. Discuss and conclude based on the comparative analysis among

these models mentioning which model outperforms and why,

what are the drawbacks might be for other models.