The long term memory used by the computer is called “RAM”
Answer:
public class Calculator {
private int total;
private int value;
public Calculator(int startingValue){
// no need to create a new total variable here, we need to set to the our instance total variable
total = startingValue;
value = 0;
}
public int add(int value){
//same here, no need to create a new total variable. We need to add the value to the instance total variable
total = total + value;
return total;
}
/**
* Adds the instance variable value to the total
*/
public int add(){
// no need to create a new total variable. We need to add the value to the instance total variable
total += value;
return total;
}
public int multiple(int value){
// no need to create a new total variable. We need to multiply the instance total variable by value.
total *= value;
return total;
}
//We need to specify which value refers to which variable. Otherwise, there will be confusion. Since you declare the parameter as value, you need to put this keyword before the instance variable so that it will be distinguishable by the compiler.
public void setValue(int value){
this.value = value;
}
public int getValue(){
return value;
}
}
Explanation:
I fixed the errors. You may see them as comments in the code
Answer:
Explanation:
If we want to insert the appropriate functions, we can know the operation, for example, it could be a sum, multiply, average or only divide, but if we want to apply for a format number like accounting number, we must go to the tab home and the section number, we can change the format number, an accounting or even text format.
Answer:
Collaborative filtering
Explanation:
This is one out of five on the Recommender system apart from most popular items, Association and Market Basket based Analysis, Content-based analysis, self and hybrid analysis where we use both content-based and collaborative based approach together. And the Recommender system is a very important topic in Data science. For this question, remember that Collaborative filtering focuses on user and various other user's choices which are mathematically alike to concerned users, and which we find with the study of a large data set. Thus, we can predict from our above study that what are going to be likes of concerned users, and at the item level, whether that item will be liked by the concerned user or not. And this is prediction, and we use this approach in Machine learning these days. For this question, and as mentioned in question the requirements, answer is Collaborative filtering.
