Preprocessing Data for Seamless Consumption

Data preprocessing refers to the process of cleaning and transforming data before analyzing it or training an AI model.

Simply put, it involves making raw data, which may be disorganized or incomplete, clean and consistent.

Datasets can contain the following issues:

• Missing values: Cases where some data is absent

• Duplicate values: Instances where the same data occurs multiple times

• Inconsistent data: Situations where the data format is not uniform

Without preprocessing, training an AI model might lead the model to learn from incorrect data, resulting in erroneous predictions.

You can handle missing values, transform data formats consistently, and remove duplicates in a dataset like the following:

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

⬇

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{"name": "John Doe", "age": "30", "city": "New York"}
{"name": "Jane Smith", "age": 40, "city": "Los Angeles"}
{"name": "Sam Brown", "age": 0, "city": "Chicago"}  // Age missing, replaced 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": "Sam Brown", "age": 0, "city": "Chicago"}
{"name": "John Doe", "age": 30, "city": "New York"}  // Converted 'thirty' to 30

⬇

Copy
{"name": "John Doe", "age": 30, "city": "New York"}
{"name": "Jane Smith", "age": 40, "city": "Los Angeles"}
{"name": "Sam Brown", "age": 0, "city": "Chicago"}
// "John Doe", "30", "New York" was duplicate and hence removed

As shown, meticulous preprocessing is crucial when creating an additional learning dataset for fine-tuning.