Write a python application that allows a user to enter any number of student test scores until the user enters 999. If the score entered is less than 0 or more than 100, display an appropriate message and do not use the score. After all the scores have been entered, display the number of scores entered and the arithmetic average.

This program is as follows

total = 0; count = 0

testscore = int(input("Score: "))

while testscore != 999:

   if testscore < 0 or testscore > 100:

       print("Out of range")

   else:

       total+=testscore

       count+=1

   testscore= int(input("Score: "))

print(count,"scores entered")

print("Arithmetic Average:",total/count)

Explanation:

This initializes total and count to 0

total = 0; count = 0

This gets input for score

testscore = int(input("Score: "))

The following iteration stop when 999 is entered

while testscore != 999:

This prints out of range for scores outside 0 - 100

   if testscore < 0 or testscore > 100:

       print("Out of range")

Otherwise

   else:

The total score is calculated

       total+=testscore

The number of score is calculated

       count+=1

Get another input

   testscore = int(input("Score: "))

The number of score is printed

print(count,"scores entered")

The average of score is printed

print("Arithmetic Average:",total/count)

Bayes’ Theorem provides a way that we can calculate the probability of a piece of data belonging to a given class, given our prior knowledge.

P(class|data) = (P(data|class) * P(class)) / P(data)

Where P(class|data) is the probability of class given the provided data.

Explanation:

Naive Bayes is a classification algorithm for binary  and multiclass classification problems. It is called Naive Bayes or idiot Bayes because the calculations of the probabilities for each class are simplified to make their calculations tractable.

This Naive Bayes tutorial is broken down into 5 parts:

Step 1: Separate By Class :  Calculate the probability of data by the class they belong to, the so-called base rate. Separate our training data by class.

Step 2: Summarize Dataset : The two statistics we require from a given dataset are the mean and the standard deviation

The mean is the average value and can be calculated using :

mean = sum(x)/n * count(x)

Step 3: Summarize Data By Class : Statistics from our training dataset organized by class.

Step 4: Gaussian Probability Density Function : Probability or likelihood of observing a given real-value. One way we can do this is to assume that the values are drawn from a distribution, such as a bell curve or Gaussian distribution.

Step 5: Class Probabilities :  The statistics calculated from our training data to calculate probabilities for new data.  Probabilities are calculated separately for each class. This means that we first calculate the probability that a new piece of data belongs to the first class, then calculate the second class, on for all the classes.