Most beginners rush to build models, but seasoned experts know the real work happens before the training starts. If your data is messy, your advanced algorithms are useless.
This guide breaks down the two biggest enemies of clean data:
Missing Values: These create gaps and bias. You can fix them by removing rows or filling them with the mean, median, or mode.
Outliers: Extreme values that distort reality. Detect them using Z-scores or IQR, then cap or transform them.The Payoff: In real-world projects, proper cleaning can boost model accuracy by 8% or more.
Pro Tip: Always visualize your data first. A simple plot often reveals issues that raw numbers hide.
