You are given the ML Coursework Dataset $($access it via the link$)$ to build a multilayer perceptron

model that can be used for forecasting the export value of crop products for a geographical region

three years into the future $($e$.$g$.$ given historical data up until or at Year $2984,$ your model can predict

the export value of crop products for Year $2987).$ Your model is allowed to be either a regression

model or a classification model. For more information about the dataset, see the README file

included in the dataset folder.

What To Submit

You are to submit THREE separate documents clarified in items A$-$C below. Do NOT submit them as

zipped $($or other compressed$)$ files. Make sure the file name and content for each of the $3$ files

indicate your Candidate No$.($and not your name$).$

A$.(90$ marks$)$ Report $-$ Prepare a report that answers ALL $4$ questions below. Use the EXACT

question themes highlighted in bold as section titles for your report, so that your report has $4$

$($titled$)$ sections exactly. Submit the document as a single pdf file.

$1.(15$ marks$)$ Performance

$(8$ marks max$)$ Report the performance of your model, and also describe $($using

maths formulae if appropriate$)$ how the reported performance was computed

from the model outputs.

$(7$ marks max$)$ State clearly the total number of instances used and then the

number of instances in your training and test sets and how these sets were

derived from the given data.

$2.(20$ marks$)$ MLP model

$(11$ marks max$)$ Describe your multilayer perceptron $($MLP$)$ model, making sure

to specify $($including maths formulae when appropriate$)$ at the very least the

activation function for its output layer, the loss function used to train it$,$ and the

number of units in its output layer.

$(9$ marks max$)$ List and describe the steps that you took to prevent overfitting

for your model $($using maths formulae when appropriate$).$

$3.(35$ marks$)$ Features & Labels

Machine Learning @ University of Sussex $2024($Temitayo Olugbade$)$

$(10$ marks max$)$ Describe $($as a list$)$ how the label$($s$)$ was$/$were derived from the

given data $($using maths formulae when appropriate$).$

$(25$ marks max$)$ Report the features that you used for your model, making sure

to specify the features used and the total number used, clarify why these

features were selected, and describe $($as a list$)$ how they were derived from the

given data, if applicable.

$4.(20$ marks$)$ Preprocessing

$(20$ marks max$)$ List and describe the preprocessing that you did on your

features for building your model $($using maths formulae when appropriate$).$

B$.(5$ marks$)$ Code $-$ Prepare all the code used to complete all the tasks above and submit as a

single pdf file.

C$.(5$ marks$)$ Model outputs for the test set$($s$)-$ Prepare the prediction outputs for your model in

a single csv file and using an appropriate header row. The file should have at least $3$ columns:

$1$ column for the data instance id$,1$ column for the corresponding true label, at least $1$ column

for the corresponding prediction$($s$)$ for your model. Submit the document as a single csv file.