Answer:
The Python code is given below with appropriate comments
Explanation:
def predict_population_growth():
#Prompt and read the input from the user
num_org = int(input("Enter the initial number of organisms: "))
GR = float(input("Enter the rate of growth [a real number > 0]: "))
numHour = int(input("Enter the number of hours to achieve the rate of growth: "))
totalHours = int(input("Enter the total hours of growth: "))
#caluclate the total poulation growth
population = num_org
hours = 0
while hours < totalHours:
population *= GR
hours += numHour
print(" The total population is " + str(int(population)))
predict_population_growth()
Answer:
A. With content-based filtering, users
receive recommendations for items
liked by similar users.
Explanation:
A database management system (DBMS) can be defined as a collection of software applications that typically enables computer users to create, store, modify, retrieve and manage data or informations in a database. Generally, it allows computer users to efficiently retrieve and manage their data with an appropriate level of security.
A data dictionary can be defined as a centralized collection of information on a specific data such as attributes, names, fields and definitions that are being used in a computer database system.
In a data dictionary, data elements are combined into records, which are meaningful combinations of data elements that are included in data flows or retained in data stores.
This ultimately implies that, a data dictionary found in a computer database system typically contains the records about all the data elements (objects) such as data relationships with other elements, ownership, type, size, primary keys etc. This records are stored and communicated to other data when required or needed.
Content-based filtering uses an algorithm to recommend to users what they like.
The statement about content-based filtering which is false is that, with content-based filtering, users
receive recommendations for items
liked by similar users.
Answer:
................. crackhead
