Hands-on with OpenAI’s API
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#> Attaching package: 'jsonlite'
#> The following object is masked from 'package:purrr':
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#> flatten
#> Warning in readLines("admin/secrets/openai_api_key.txt"): incomplete final line
#> found on 'admin/secrets/openai_api_key.txt'
#> $id
#> [1] "chatcmpl-ArVgcTJRXyoHezFUhTE3N63Ytyg3A"
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#> $object
#> [1] "chat.completion"
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#> $created
#> [1] 1737316550
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#> $model
#> [1] "gpt-3.5-turbo-0125"
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#> $choices
#> index message.role
#> 1 0 assistant
#> message.content
#> 1 1. Define the problem: Identify the objectives and goals of the project, including the specific questions you want to answer or the problems you want to solve.\n\n2. Data collection: Gather relevant data from multiple sources, including structured and unstructured data, and ensure it is clean and accurately labeled.\n\n3. Data preprocessing: Clean, preprocess, and manipulate the data to ensure it is in a format suitable for analysis. This may include handling missing values, outliers, and scaling variables.\n\n4. Exploratory data analysis (EDA): Explore the data to understand patterns, relationships, and trends, and gain insights that will inform your modeling approach.\n\n5. Feature engineering: Create new features or transform existing features to enhance the performance of your models.\n\n6. Model selection: Choose appropriate algorithms and models based on the nature of the problem and the data available. Consider factors such as interpretability, accuracy, speed, and scalability.\n\n7. Model training: Split the data into training and testing sets, and train the selected models using the training data.\n\n8. Model evaluation: Evaluate the performance of the models using metrics such as accuracy, precision, recall, and F1 score. Use cross-validation techniques to ensure robustness.\n\n9. Model tuning: Fine-tune the hyperparameters of the models to optimize their performance and prevent overfitting.\n\n10. Deployment: Deploy the final model into production and monitor its performance in real-world settings. Iterate on the model as needed based on feedback and new data.
#> message.refusal logprobs finish_reason
#> 1 NA NA stop
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#> $usage$prompt_tokens
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#> $usage$completion_tokens
#> [1] 300
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#> $usage$total_tokens
#> [1] 319
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#> $usage$prompt_tokens_details$cached_tokens
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#> $usage$prompt_tokens_details$audio_tokens
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#> $usage$completion_tokens_details
#> $usage$completion_tokens_details$reasoning_tokens
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#> $usage$completion_tokens_details$audio_tokens
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#> $usage$completion_tokens_details$accepted_prediction_tokens
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#> $usage$completion_tokens_details$rejected_prediction_tokens
#> [1] 0
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#> $service_tier
#> [1] "default"
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#> $system_fingerprint
#> NULL