Data Processing

Data processing is essential groundwork that consumes 60-80% of a data scientist's time, transforming raw data into a format AI models can use. This can significantly improve the final accuracy of trained models. Key processing techniques are normalization, outlier handling, binning, one-hot encoding, feature crossing, and sparse vector encoding.