Hiya!
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^Hope this helps
In the NumPy function, the data preparation technique that is used to help machine learning algorithms is called the reshape technique or function
For better understanding, let us explain what the reshape function means
- The numpy package helps to give the right tools for scientific and mathematical computations in python
. it includes functions that cam be used to perform common linear algebra operations, fast Fourier transforms, and statistics
The reshape function simply alter or change the row and column arrangement of data in numpy function and it is said to just give new shape to an array without the altering of its data.
from the above, we can therefore say that the answer In the NumPy function, the data preparation technique that is used to help machine learning algorithms is called the reshape technique or function is correct
learn more about reshape function from:
brainly.com/question/24728884
Answer:
i dont know spanish but i am gussing that you want to find differnce in virus information?
Explanation:
Answer:
ALTER TABLE Products
ADD Products_price float(5,2) DEFAULT 9.99,
ADD Adding_time datetime;
Explanation:
So at first we need to use ALTER TABLE statement, when we use this statement we also need to provide the table name. In this case, i assume it is 'Products'.
Then, as per question, we need to add two columns.
The first one is 'product_price' and it contains decimal points. So, we need to use ADD statement and give a column name like 'Prodcuts_price' and its datatype 'float' because it contains decimal or floating points. so, for 3 digits on the left and 2 digits on the right, it makes a total of 5 digits. So, we need to declare it like this. (5,2) it means a total of 5 digits and 2 after the decimal point. after that, we need to set our Default value using the DEFALUT statement. so DEFAULT 9.99. it will set the default value to 9.99 for existing products.
And for our next column, we give its a name like 'Adding_time' then it's datatype 'datetime' because it will contain date and times.