Hands-on with OpenAI’s API
#>
#> Attaching package: 'jsonlite'
#> The following object is masked from 'package:purrr':
#>
#> flatten
#> Warning in readLines("admin/secrets/openai_api_key.txt"): incomplete final line
#> found on 'admin/secrets/openai_api_key.txt'
#> $id
#> [1] "chatcmpl-9oeDA1hNY73HHqKaCv7jSuZ8hytnA"
#>
#> $object
#> [1] "chat.completion"
#>
#> $created
#> [1] 1721858000
#>
#> $model
#> [1] "gpt-3.5-turbo-0125"
#>
#> $choices
#> index message.role
#> 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.
#> logprobs finish_reason
#> 1 NA stop
#>
#> $usage
#> $usage$prompt_tokens
#> [1] 19
#>
#> $usage$completion_tokens
#> [1] 211
#>
#> $usage$total_tokens
#> [1] 230
#>
#>
#> $system_fingerprint
#> NULL