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build a multilayer perceptron model from the dataset with adequate neurons and layer size. your model can predict the export value of crop products for

build a multilayer perceptron model from the dataset with adequate neurons and layer size. 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. 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.. 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:(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. Other Important Notice
1. You are not required to code machine learning algorithms from scratch. You can use standard
machine learning libraries including Scikit-learn, PyTorch, TensorFlow. However, you must only
use standard libraries.
2. You are also allowed to use any standard software libraries (e.g. Python pandas/numpy,
Microsoft Excel, IBM SPSS, Stata, R libraries) for data extraction and preparation.
3. You are NOT permitted to use or submit someone elses code, output, or report as yours (you
are permitted to use code snippets from the lab materials given by the teacher, from machine
learning software library documentations as stated in #1 above, or from recommended
textbooks).
4. You must NOT use generative artificial intelligence (AI) to generate any materials or content
for your assessment submissions.

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