Data preprocessing

We can’t build anything useful on top of a messy dataset. It’s like trying to paint a wall without sanding it first. Data preprocessing means cleaning and shaping the data so she—the AI—doesn’t trip over the rough edges.

Normalization

Do this so numbers don’t bully each other. One feature might run from 0 to a million, while another only nudges between 0 and 1. Without scaling, she’ll focus on the loud one and ignore the quiet one. Normalization evens things out. Everything lands in the same range, and she pays attention fairly.

Tokenization

Text looks simple until we remember she doesn’t read words like we do. Tokenization is chopping sentences into manageable pieces: words, characters, or subwords. “Can’t” becomes “can” and “’t.” That way she has building blocks instead of a vague blob of letters. It’s just giving her Lego bricks instead of a lump of plastic.

Missing values

Real datasets have holes. Sometimes the value was lost, sometimes never collected. She can’t guess if we don’t patch them. Options are simple: drop the rows, fill them with averages, or insert placeholders. None is perfect, but leaving blanks is worse. Think of it as plugging leaks before she sets sail.

Why it matters

We spend hours here so she can spend minutes learning. It feels dull, but skipping it guarantees trouble later. Clean, normalized, tokenized data means fewer surprises and better results.