Python code interpreter with o1 and GPT-4o

An AI data scientist can transform raw datasets, even unsanitized ones, into trained supervised machine learning models, demonstrated through a specific use case involving the Titanic disaster dataset from Kaggle. Using OpenAI's o1 and GPT-4o models along with E2B's Code Interpreter SDK, the AI generates and executes a plan to clean the data, train a decision tree model, and produce a performance visualization. The process involves uploading the dataset to a secure cloud sandbox, generating a detailed code plan with o1-mini, consolidating the code into a single block using GPT-4o-mini, and executing the code through the E2B Code Interpreter. The final outcome is a trained decision tree model capable of predicting passenger survival, with the entire methodology and example code available on GitHub for further exploration.

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