Using Python,  Create a k-fold cross validation code with the number of folds being 10. The raw data needs to be split into training and testing. The number of folds is the number of times you should go through a for loop. Inside of the for loop, index into your raw data to pull out training and testing data based on the fold (think about how the folds are organized and how much data is in each fold, which is based on the total number of folds) Do normal linear regression model fitting Keep track of the R^2 scores of the folds Print the average and standard deviation of the R^2 value for the different folds

Please help (on jupyter note in python) Homecoming Shopping ListWe bought the following souvenirs for homecoming gathering: 5 shirts ( $ 10/item), $ 10 Polo Shirts ( $ 20.75/item), 5 trousers ( $ 29.95/item), 10 caps ( $ 11.95/item), 20 pens ( $ 1.99/item), 20 hoodies ( $ 24.95/item), and 30 chocoloates ( $ 2.95/item). Use Dot Product and NumPy arrays to compute the total cost of Homecoming.

When an author produces an index for his or her book, the first step in this process is to decide which words should go into the index; the second is to produce a list of the pages where each word occurs. Instead of trying to choose words out of our heads, we decided to let the computer produce a list of all the unique words used in the manuscript and their frequency of occurrence. We could then go over the list and choose which words to put into the index. The main object in this problem is a "word" with associated frequency. The tentative definition of "word" here is a string of alphanumeric characters between markers where markers are white space and all punctuation marks; anything non-alphanumeric stops the reading. If we skip all un-allowed characters before getting the string, we should have exactly what we want. Ignoring words of fewer than three letters will remove from consideration such as "a", "is", "to", "do", and "by" that do not belong in an index. In this project, you…

You will be using on RV related review comments to train a Sentence completion model. You could use the code below. The code is written in Jupyter notebook python.  please follow the following instructions: 1. Build a working sentence completion model. Training data is provided in train.csv. This model should take partial sentences (e.g., "hey how ar," "hello, please") and provide completion for that sentence. Not every input needs to be completed into a correct sentence (e.g., "asdfauaef" does not need to have a sentence completion). For this model's purposes, a sentence is considered completed if an "end of sentence" character (like a full stop, question mark, etc.) occurs within the next five words OR. We predict up to 5 words from the input. 2. BONUS: If you use word vectors or other techniques from what we shared in class using character encoding and have a working model. 3. The test data (test.csv) will be used to evaluate the model. The test file contains partial sentences. Use…