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Data exploration is an important step in dataanalysis that extracts key insights using multiple steps such as filtering, sorting, grouping, etc. However, this process is generally interactive and requires the user to manually explore the data, making the process time-consuming and necessitating domain expertise.
This eliminates the need for data scientists to manually develop specific ML models for each use case, and therefore democratizes AI access, benefitting even small manufacturers. Agents like PandasAI come into play, running this code on high-resolution time series data and handling errors using FMs. a challenge-level question).
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