88 Hands-on with OpenAI’s API

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#> [1] "chatcmpl-9oeDA1hNY73HHqKaCv7jSuZ8hytnA"
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#> [1] "gpt-3.5-turbo-0125"
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#> 1     0    assistant
#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                message.content
#> 1 1. Define the problem: Clearly understand and define the problem you are trying to solve using data science techniques.\n\n2. Data collection: Gather relevant data from various sources that can help in solving the problem.\n\n3. Data cleaning and preparation: Clean the data by removing any missing values, outliers, or irrelevant information, and prepare the data for analysis.\n\n4. Exploratory data analysis: Explore the data to understand patterns, trends, and relationships within the dataset.\n\n5. Feature engineering: Create new features or transform existing features to improve the performance of the model.\n\n6. Model selection: Choose the appropriate machine learning model that best fits the problem and the data.\n\n7. Model training: Train the chosen model on the training data.\n\n8. Model evaluation: Evaluate the model's performance using different metrics and techniques such as cross-validation.\n\n9. Model tuning: Fine-tune the model by adjusting hyperparameters to improve performance.\n\n10. Deployment: Once the model is trained and evaluated, deploy it in a production environment to start making predictions on new data.
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