Preprocessing: Preparing Data for Consumption

Data Preprocessing refers to the process of cleaning and transforming data before it's analyzed or used to train AI models.

In simple terms, it's about making raw data, which might be messy or incomplete, clean and consistent.

Data can have the following issues:

• Missing Values: When parts of the data are absent

• Duplicate Values: When the same data is included multiple times

• Inconsistent Data: When data formats are not uniform

Here's how you can handle missing values, ensure consistency, and remove duplicates in a JSONL dataset.

Copy
{"name": "John Doe", "age": "30", "city": "New York"}
{"name": "Jane Smith", "age": 40, "city": "Los Angeles"}
{"name": "Jim Brown", "city": "Chicago"}
{"name": "John Doe", "age": "thirty", "city": "New York"}

⬇

Copy
{"name": "John Doe", "age": "30", "city": "New York"}
{"name": "Jane Smith", "age": 40, "city": "Los Angeles"}
{"name": "Jim Brown", "age": 0, "city": "Chicago"}  // Replace missing age with 0
{"name": "John Doe", "age": "thirty", "city": "New York"}

⬇

Copy
{"name": "John Doe", "age": 30, "city": "New York"}
{"name": "Jane Smith", "age": 40, "city": "Los Angeles"}
{"name": "Jim Brown", "age": 0, "city": "Chicago"}
{"name": "John Doe", "age": 30, "city": "New York"}  // Convert 'thirty' to the number 30

⬇

Copy
{"name": "John Doe", "age": 30, "city": "New York"}
{"name": "Jane Smith", "age": 40, "city": "Los Angeles"}
{"name": "Jim Brown", "age": 0, "city": "Chicago"}
// Removed duplicate "John Doe", "30", "New York"

When creating additional training datasets for fine-tuning, it is crucial to pre-process the data meticulously.