a$.$ Apply collaborative filtering to recommend food items to students using table $1.$

b$.$ Develop a fast$-$food classifier based on it and use this classifier for food recommendation to students.

c$.$ Combine a and b and formulate semi$-$supervised learning methodology for classification and recommendation. Apart from labelling some unsupervised data can you think of any other approach for semi$-$supervised learning. Elaborate that approach.

d$.$ Determine most general and consistent hypothesis for this example.

Table: $1$

$\backslash$table$[[$Rating stars,Location,Food$-$Category,Price,Good taste,Include drink$],[******,$Campus,fish,cheap,Y$,$Y$],[****,$Sun$-$shine,chicken,cheap,Y$,$Y$],[****,$Station,veg,cheap,Y$,$Y$],[**,$Campus,fish,medium,N$,$N$],[**,$Campus,veg,costly$,$N$,$N$],[******,$Campus,fish,cheap,Y$,$N$],[**,$sun$-$shine,chicken,medium,N$,$Y$],[****,$sun$-$shine,veg,costly$,$N$,$N$],[******,$station,fish,cheap,Y$,$Y$],[****,$station,veg,cheap,Y$,$N$]]$

$**$Provide steps$/$pseudo code and algorithm and show diagrams wherever necessary.$**$