✅ Comment “Design” to get detailed resources on Preprocessing in your DM. Most systems don’t struggle because of complex models. They struggle because the input is messy. This reel explains preprocessing in simple terms: • You start with raw data. It’s incomplete, inconsistent, and noisy. • Some values are missing. Some are duplicated. Some are just wrong. • If you feed this directly into your system, results become unreliable. • So you clean the data. Remove duplicates. Handle missing values. • You normalize formats so everything follows the same structure. • You transform data into a usable shape. Encoding, scaling, tokenizing. • You filter noise so the signal becomes clearer. That’s the core idea. Preprocessing isn’t glamorous. But it determines whether your system works or quietly fails. In machine learning, better preprocessing often beats a more complex model. In backend systems, structured inputs reduce bugs downstream. Now you’re not just building features. You’re shapin...