Day 6: Data Preprocessing — Cleaning the Messy Reality of Enterprise Data

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Real-world enterprise data is rarely ready for machine learning. Whether you are analyzing console output from high-performance networking hardware, such as troubleshooting transceiver EEPROM data on a Mellanox SN2100 switch, or aggregating daily trading volumes for Indian REITs and InVITs, the raw data will be full of errors, gaps and anomalies. (These are only examples to illustrate data problems.) Data preprocessing is the engineering step that turns that chaotic raw information into the clean, numeric format that algorithms need. "Garbage in, garbage out" is the first rule of AI: a model c

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