Consider a classification problem with the data summarized in Table $1.$ The task is to predict if a given city has a risk of a disease epidemic or not. The data is defined using two input features or variables:

a$.$ Size of the city

b$.$ Distance to the nearest city with epidemic

The target is a binary $(0$ no risk and $1-$risk$).$

Table $1$: training data set.

City number Size of city Distance Risk

$121800\mathrm{.}011$

$2109018\mathrm{.}31$

$337030$

$41204\mathrm{.}11$

$542090$

$6907\mathrm{.}20$

$7480100$

$86202\mathrm{.}70$

$95702\mathrm{.}81$

$104400\mathrm{.}011$

Use Rapidminer software to:

a$.$ Develop neural network model to classify if a given city has a risk of a disease epidemic or not. Add snapshot of your work.

b$.$ Construct confusion matrix and compute recall, precision, accuracy, and F$1-$score

c$.$ Predict if the following new cities have a risk of a disease epidemic or not $($Use cross validation with k$=2)$

City number Size of city Distance

$121891\mathrm{.}5$

$2105820$

$3400030$