  The rise of big data has amplified the need for efficient, user-friendly
automated machine learning (AutoML) tools. However, the intricacy of
understanding domain-specific data and defining prediction tasks necessitates
human intervention making the process time-consuming while preventing full
automation. Instead, envision an intelligent agent capable of assisting users
in conducting AutoML tasks through intuitive, natural conversations without
requiring in-depth knowledge of the underlying machine learning (ML) processes.
This agent's key challenge is to accurately comprehend the user's prediction
goals and, consequently, formulate precise ML tasks, adjust data sets and model
parameters accordingly, and articulate results effectively. In this paper, we
take a pioneering step towards this ambitious goal by introducing a
ChatGPT-based conversational data-science framework to act as a "personal data
scientist". Precisely, we utilize Large Language Models (ChatGPT) to build a
natural interface between the users and the ML models (Scikit-Learn), which in
turn, allows us to approach this ambitious problem with a realistic solution.
  Our model pivots around four dialogue states: Data Visualization, Task
Formulation, Prediction Engineering, and Result Summary and Recommendation.
Each state marks a unique conversation phase, impacting the overall user-system
interaction. Multiple LLM instances, serving as "micro-agents", ensure a
cohesive conversation flow, granting us granular control over the
conversation's progression. In summary, we developed an end-to-end system that
not only proves the viability of the novel concept of conversational data
science but also underscores the potency of LLMs in solving complex tasks.
Interestingly, its development spotlighted several critical weaknesses in the
current LLMs (ChatGPT) and highlighted substantial opportunities for
improvement.
