Calculate the average of numbers in a list

 numbers = [3, 7, 2, 8, 1]

total = sum(numbers)

average = total / len(numbers)

print("The average is", average)


Comments

Post a Comment

Popular posts from this blog

Hello World Python Code with Explanation

here's an example of a "Hello, World!" program in Python:pythonCopy code print("Hello, World!") This program simply prints the message "Hello, World!" to the console. This program is a simple example of a Python program. The print statement is used to output text to the console. In this case, the text being output is "Hello, World!", which is enclosed in quotation marks. When the program is executed, the text "Hello, World!" will be printed to the console. The program does not take any input or perform any calculations - it simply prints the message and then exits. This program is often used as a simple starting point for learning how to write Python code, as it demonstrates the basic syntax of the language and how to output text to the console.
Read more

Addition of Two numbers in Python

num1 = 5 num2 = 7 sum = num1 + num2 print("The sum of", num1, "and", num2, "is", sum) This program adds two numbers (num1 and num2) and stores the result in a variable called sum. The print statement is used to display the result to the console.
Read more

Iris Flowers Classification ML Project with Code

In this code, we first load the iris dataset using the load_iris() function from scikit-learn. We then split the data into training and testing sets using the train_test_split() function. Next, we scale the features using the StandardScaler() function from scikit-learn. We then train the KNN model with n_neighbors = 3 using the KNeighborsClassifier() function. After training the model, we predict the species of the iris flowers in the testing set using the predict() function. Finally, we evaluate the performance of the model using the accuracy_score() , precision_score() , recall_score() , and f1_score() functions from scikit-learn. Note that the values of the metrics may vary slightly depending on the random seed used for splitting the dataset.   # Import necessary libraries from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler from sklea...
Read more