Answer
sorry I only know how to make balloons :(
Explanation:
For the balloons the recipe that one is:
- you will need 6 latex
To make latex you will need two different elements one is 5 Carbon and the other one is 8 Hydrogen ( don't use crafting table to do it use the compound creator)
-Dye ( whatever color you want)
-Helium
-lead
Instructions
on the crafting table the latex will go on the sides (3 on the left and 3 on the right). The dye goes on top, the Helium in the middle and the lead at the bottom and there you go a balloon
The software is written in C++ and may be found in the explanation section below. C++ keywords and symbols are all capitalized. The least number of all three integers is determined via a nested if-else decision branch. After you've entered three integers, the application prints the least of them all.
<h3>
What is the example of C++?</h3>
#include <iostream>
using namespace std;
int main() {
int num1,num2,num3;
cout<<"enter first integers"<<endl;
cin>>num1;
cout<<"enter second integers"<<endl;
cin>>num2;
cout<<"enter the third integers"<<endl;
cin>>num3;
if(num1<num2){
if(num1<num3){
cout<<"Smallest integer is "<<num1<<endl;
} else{
cout<<"Smallest integer is "<<num3<<endl;
}
}else {
if(num2<num3){
cout<<"Smallest integer is "<<num2<<endl;
} else{
cout<<"Smallest integer is "<<num3<<endl;
}
}
return 0;
}
Thus, it is written in C++ language.
For more details about C++ click here:
brainly.com/question/19581899
#SPJ1
Answer:
user_name = input("input user name: ")
print(len(user_name))
Explanation:
input - input function in python
len - length of value
print - print data
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.
Try getting to know her or ask her about stuff she enjoys doing or hobbies she likes
