Archives for August 2023

We’ve had some issues with our call center

When a client calls, they give their name and our tech support checks it in the database. If the client can’t be found, a new client is created.

We thought we created a fool-proof system but alas, it is not so. If someone spells a name differently then they can’t be found! This news was so shocking that we’re tasking you to filter our database on those similar-sounding names.

As a proof of concept, we’ve got the following table :

And we expect you to find that row 1 and 2 are similar sounding , but row 5 and 6 are not

In the returned table, we’d like you to show that you’ve compared row 1 to row 2-14, but that row 2 is going to be checked against 3-14 (but not row 1 since you’ve already checked):

CREATE OR REPLACE TABLE week_60 (
    name VARCHAR
);

INSERT INTO week_60 (name)
VALUES
    ('John Smith'),
    ('Jon Smyth'),
    ('Jane Doe'),
    ('Jan Do'),
    ('Michael Johnson'),
    ('Mike Johnson'),
    ('Sarah Williams'),
    ('Sara Williams'),
    ('Robert Brown'),
    ('Roberto Brown'),
    ('Emily White'),
    ('Emilie Whyte'),
    ('David Lee'),
    ('Davey Li');

Good luck!

Snowpark ML is another public preview feature we’re very excited to get playing with. Now, data science skills are specialist skills, and so we don’t want to go into too hard here, so we’ve made this as easy as we can.

(1) Ingest the data

(2) Create a connection.json file that looks a little like the one below

{
  "account"            : "",
  "user"               : "",
  "password"           : "",
  "role"               : "",
  "warehouse"          : "",
  "database"           : "",
  "schema"             : ""
}

(3) Connect to your data as in the code below

connection_parameters = json.load(open('credentials/connection.json'))
session = Session.builder.configs(connection_parameters).create()

(4) Fill in the gaps

#Load table
week59 = session.table('week59').to_pandas()

# Initialize the DecisionTreeClassifier with column names
model = 

# Fit the model to the data


# Make predictions


# Display the predictions

Your result should look something like:

Remember, if you want to participate:

1. Sign up as a member of Frosty Friday. You can do this by clicking on the sidebar, and then going to ‘REGISTER‘
2. Post your code to GitHub and make it publicly available (Check out our guide if you don’t know how to here)
3. Post the URL in the comments of the challenge