Data Processing

Data processing and preparation is the essential groundwork that transforms raw data into a format machine learning models can actually use, often taking 60-80% of a data scientist's time. Raw data is typically messy: features have different scales, categories are text-based, and outliers distort patterns. Proper preparation can improve model accuracy by at least 5%, making it one of the highest-impact steps in the entire pipeline.

Normalization rescales numerical features to comparable ranges, like squeezing age (0-100) and income (0-100,000) into the same 0-1 or -1 to 1 scale using techniques like Min-Max or Z-score standardization. This prevents features with larger magnitudes from dominating the model. Outliers are extreme values that can skew predictions; they may be removed, capped, or kept depending on whether they represent errors or genuine rare events. Binning converts continuous numbers into discrete buckets, like grouping ages into "child," "adult," and "senior". This can capture non-linear patterns and make data more interpretable.

One-hot encoding turns categorical variables (like "red," "blue," "green") into binary columns where each category gets its own 0/1 indicator, since models can't process text directly. Feature crossing combines two or more features (like "country × language") to capture interaction effects that individual features miss. Finally, sparse vector encoding efficiently stores data where most values are zero (common after one-hot encoding with many categories), saving memory and computation by only storing non-zero entries.