what is the pseudo code for this code

### Random Forest

column$\_$range $=$ all$\_$Program.columns$[$all$\_$Program.columns.get$\_$loc$("$I$\mathrm{.}1.")$:all$\_$Program.columns.get$\_$loc$("$R$\mathrm{.}4\mathrm{.}7.")+1]$

important$\_$features $=$ all$\_$Program.loc$[$:$,$ column$\_$range.tolist$()+[\text{'}$Decision Type'$]]$

cat$\_$cols $=$ important$\_$features.select$\_$dtypes$($include$=[\text{'}$object$\text{'}]).$columns

le$\_$dict $=\{\}$

for col in cat$\_$cols:

le $=$ LabelEncoder$()$

important$\_$features$[$col$]=$ le$.$fit$\_$transform$($important$\_$features$[$col$])$

le$\_$dict$[$col$]=$ le

X $=$ important$\_$features.drop$(\text{'}$Decision Type', axis$=1)$

y $=$ important$\_$features$[\text{'}$Decision Type'$]$

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}2,$ random$\_$state$=42)$

model$\_$rf $=$ RandomForestClassifier$($random$\_$state$=42,$ class$\_$weight$=$'balanced'$)$

model$\_$rf$.$fit$($X$\_$train, y$\_$train$)$

y$\_$pred $=$ model$\_$rf$.$predict$($X$\_$test$)$

accuracy $=$ accuracy$\_$score$($y$\_$test, y$\_$pred$)$

print$($f$\text{'}$Accuracy with selected features: $\{$accuracy:$\mathrm{.}2$f$\}\text{'})$

class$\_$name$\_$map $=\{$

$0$: 'Withdrawal of Program Accreditation',

$1$: $\text{'}$Re$-$accreditation',

$2$: 'Conditional for $24$ Months',

$3$: 'Conditional for $18$ months',

$4$: 'Conditional for $12$ Months',

$5$: 'Warning for Freezing',

$6$: 'Freezing of Program Accreditation',

$7$: 'New Program Accreditation',

$8$: 'Decline'

$\}$

class$\_$names $=[$class$\_$name$\_$map$[$name$]$ for name in y$.$unique$()]$

report $=$ classification$\_$report$($y$\_$test, y$\_$pred, target$\_$names$=$class$\_$names$)$

print$($report$)$

cm $=$ confusion$\_$matrix$($y$\_$test, y$\_$pred, labels$=$model$\_$rf$.$classes$\_)$

plt$.$figure$($figsize$=(6,4))$

sns$.$heatmap$($cm$,$ annot$=$True, fmt$=\text{'}$g$\text{'},$ cmap$=$'BuPu', cbar$=$False, xticklabels$=$class$\_$names, yticklabels$=$class$\_$names$)$

plt$.$xlabel$(\text{'}$Predicted labels'$)$

plt$.$ylabel$(\text{'}$True labels'$)$

plt$.$title$(\text{'}$Confusion Matrix'$)$

plt$.$show$()$

and how to interpitation the model