Answer:
numbers = 1:1:100;
for num=numbers
remainder3 = rem(num,3);
remainder5 = rem(num,5);
if remainder3==0
disp("Yee")
else
if remainder3 == 0 && remainder5 == 0
disp ("Yee-Haw")
else
if remainder5==0
disp("Haw")
else
disp("Not a multiple of 5 or 4")
end
end
end
end
Explanation:
- Initialize the numbers variable from 1 to 100.
- Loop through the all the numbers and find their remainders.
- Check if a number is multiple of 5, 3 or both and display the message accordingly.
Protocal
I need to make this 20 charaters so ye
There are several things you can look for on a website to help you figure out if the information is reliable. The first thing you should evaluate is the audience that the website is intended for. Is it intended for academics? School children? The general public?
The next thing you should look at is the author of the website. Is the author identified? Is the author an expert in their field? Can you establish the author's credibility? Is the author affiliated to an academic institution or credible organisation?
Look at the accuracy of the website. Check for spelling errors, proper grammar, and well-written text. Are there any sources cited? Are those sources credible?
You should also check to see when the information was published. Is the information up to date? Are all of the links up to date and functioning?
There is one last thing you can look at, and this is the domain of the website. Domains like .edu and .gov are more credible than .com or .net domains.
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.
