$2.$ Classification Task $($predicting a class for a new input$)(50\%-100$ marks$)$

For this task, you will find a dataset and classify a set of records to a specific target using $3$ different

types of Machine learning algorithms $($again$,$ one must be neural networks$)$ and write a small report.

Your first sub$-$task is to find a dataset suitable for machine learning. For many classification models,

you will need to normalise the data so refer to methods on how to normalise numerical and string

data. If there is a feature which has groups of strings, use the one$-$hot encoder, this will add extra

columns in the dataset but will become much easier to train. For this task, you will be asked to use

one CSV file type dataset $($can use from task $1$ if there is a classification feature$).$ Once you have fit

the model and made some appropriate predictions, state the accuracy of the model after using the

test data, and evaluate its usefulness for your dataset. In the report, you must explain in detail as to

why the model has performed in such way and suggest ways of improving the model.

Create a classification model using algorithm $1$ state which algorithm and submit working source

code $(10$ marks$)$ Model can be either Support Vector Machines, Random Forest, Decision Tree,

Logistic Regression, Na$$ve Bayes or K$-$Nearest Neighbours $($you are not limited to these, but these

will be taught on the module$).$

Create a classification model using algorithm $2$ state which algorithm and submit working source

code $(10$ marks$)-$ Model can be either Support Vector Machines, Random Forest, Decision Tree,

Logistic Regression, Na$$ve Bayes or K$-$Nearest Neighbours $($you are not limited to these, but these

will be taught on the module$).$

Create a model using Neural Networks $$ submit working source code $(20$ marks$)$

Report $(60$ marks$)$

$4.$ Showing and comparing accuracies of each model. $(10$ marks$)$

$5.$ Explanation of how your two models work $($include references$)(25$ marks$)$

$6.$ Suggest on different methods to improve your models. E$.$g$.$ Remove$/$add data? If so$,$ which

features would you build on$?$ Think about visualising the data first and seeing which features

do not help with the prediction. For NN models, change the number of neurons, layers etc?

$(25$ marks$)$