READ FIRST:
Before we touch the massive real-world datasets, we need to prove our Pandas skills. I (Mathew) will provide a small, messy dummy_data.csv file. Write a Python script using Pandas to load this file, clean it, and prepare it for machine learning.
Definition of Done:
[ ] Python script successfully loads dummy_data.csv into a Pandas DataFrame.
[ ] All rows with missing (NaN) values are dropped.
[ ] The cleaned DataFrame is printed to the terminal.
[ ] Code is pushed to a new branch (e.g., feature/ml-dummy-data) and a PR is opened.
READ FIRST:
Before we touch the massive real-world datasets, we need to prove our Pandas skills. I (Mathew) will provide a small, messy dummy_data.csv file. Write a Python script using Pandas to load this file, clean it, and prepare it for machine learning.
Definition of Done:
[ ] Python script successfully loads dummy_data.csv into a Pandas DataFrame.
[ ] All rows with missing (NaN) values are dropped.
[ ] The cleaned DataFrame is printed to the terminal.
[ ] Code is pushed to a new branch (e.g., feature/ml-dummy-data) and a PR is opened.