The weekly sales transaction dataset (posted here 
https://web.njit.edu/~usman/courses/cs675_fall20/Sales_Transactions_Dataset_Weekly.csv) 
shows weekly sales of over 800 items across a year. Your task is to predict the 
final week's sales from the previous values for each item in the dataset. Report 
your mean squared error which is defined as the mean squared error of your 
predictions 1/n(sum_i (y'i - yi)**2). The best mean squared that we achieve in this 
dataset is about 17.5 with ridge regression applied to an LSTM encoding of the data.

To qualify for full points you should achieve an MSE below 23.

You may use numpy, sklearn, and pandas in your solution. Your program should 
consider the last week 51 as the test data and prior weeks as training.

Submit your program that takes as input the dataset 
Sales_Transactions_Dataset_Weekly.csv and outputs the predictions of week 51 for 
each item and the mean squared error.

It's very important that your code does not consider the last column during training.