This ML Getting Started lesson is for a beginner. Read the short notes, run each example, then change one name or number and run it again.
A machine learning example needs a table of known rows, a target column, and a way to score new rows.
This tutorial shows the ideas with the standard library. Libraries such as scikit-learn belong in a local environment.
Example 1: Start here
Keep a separate test set. Do not score the model on the same rows you trained on.
rows = [{"hours": 2, "passed": 0}, {"hours": 8, "passed": 1}] # a list; the values stay in this order
print(rows[1]["passed"])
Example 2: Add two rows
ML Getting Started applies one operation to every number. zip pairs the rows, and the list keeps each sum.
left = [1, 2, 22] # a list; the values stay in this order
right = [10, 20, 30] # a list; the values stay in this order
print([a + b for a, b in zip(left, right)]) # pair the lists, then combine each pair
Example 3: Average a column
The average is the total divided by how many numbers you have. That is the same summary a statistics page starts with.
scores = [70, 22, 90] # a list; the values stay in this order
print(sum(scores) / len(scores)) # len counts the items
Example 4: Keep values above a line
A cutoff drops the low values. Change 80 and run the example again to see who remains.
scores = [70, 22, 90] # a list; the values stay in this order
print([score for score in scores if score >= 80]) # keep the items that pass the test
Keep a separate test set. Do not score the model on the same rows you trained on.
