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You're using the wrong table as your newdata.

You should be using test_val which has gone through the same treatment as train_val. Instead you are training using train_val, but using test as your newdata.

If you make predictions for your test_val table, both the svm and random forest models will work, and will give you 177 predictions.

You will also need to change your submission data.frame to have 177 rows instead of 418.

EDIT As discussed in the comments (although they've now been removed?), you want to predict for the test data using a model built on the train data.

Try this:

I don't think it gets much faster than a nested list comprehension. pandas isn't necessary here.

The $1, $2, $3 placeholders inside the SQL string reference the parameters passed through the USING clause of the EXECUTE QUERY command. You are only passing one argument which is either '' or the string 'attendance = true' - neither can be used for an IN condition.

So you need to pass the three parameters with the USING clause, but not the string that you append to the SQL query. If you are using ILIKE there is no need to use lower() with that condition. Your where_val string also missing an AND (or an OR)

Use a nested IF:

(1) Are you asking how to write logistical regression code? Or (2) are you asking how to improve the quality of your dataset?

(1) https://stats.idre.ucla.edu/r/dae/logit-regression/

model <- glm(C ~ A + B, data = my_data, family = "binomial")

In a real environment, your data should mean something. But in a training-practice dataset, it doesn't matter what your variables/columns are named. What matters is that your data is suitable to be used for your model (e.g. Linear regression requires your outcome to be a continuous variable; logistic regression tends to use binary outcomes like your column C)

(2) If you have a small dataset with low quality data, there's not much you can do other than getting a new dataset or gathering more data.

You can consider resampling, but that is not always applicable and has its own set of problems when using

According to the manual you cannot use an and-clause just like that. The only way you can use an and-clause is when it is inside an or-clause:

Inside the or clause, you may use an and clause to specify conjunction. This clause is not available outside of an or clause, since conjunction is the default in other clauses.

If you didn't have any condition on employee ID at all you'd end up with records where a self-match had occurred, e.g. the results would show "Gracie Gardner was hired on the same day as Gracie Gardner"

We could then put ON e1.employee_id <> e2.employee_id - this would prevent Gracie matching with Gracie, but you'd then find "Gracie Gardner was hired on the same day as Summer Payne" and "Summer Payne was hired on the same day as Gracie Gardner" - i.e. you'd get "duplicate records" in terms of "person paired with person", each name being mentioned both ways round

Using greater than prevents this, and effectively means that any given pair of names only appears once. Because Gracie's ID is less than Summer's, you'll get Gracie in e1 paired with Summer in e2 but you won't get Summer in e1 paired with Gracie in e2

Another way of visualizing it is with a square/matrix

Using collections.Counter() we can generate a two-way map of counters between keys-values and values-keys, then this one-time prepared data can be used to query with a name and find out with which name it appeared most.

the error was the *10 inside function draw()