Answer:
The complete program is as follows:
def convert_distance(miles):
km = miles * 1.6 # approximately 1.6 km in 1 mile
return km
my_trip_miles = 55
# 2) Convert my_trip_miles to kilometers by calling the function above
my_trip_km =convert_distance(my_trip_miles) #3) Fill in the blank to print the result of the conversion
# 4) Calculate the round-trip in kilometers by doubling the result,
print("The distance in kilometers is " +str(my_trip_km))
# and fill in the blank to print the result
print("The round-trip in kilometers is " + str(my_trip_km * 2))
Explanation:
<em>The program is self-explanatory because I used the same comments in the original question.</em>
The default hyperlink color is automatically set when you type
O AND U which spells ABOUT
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.
