LLMs for data curation and preparation
How LLMs can be utilized to perform a basic step in the data preprocessing of a process log
In today's data-driven landscape, the emergence of LLMs is revolutionizing the way we perform data management, curation, and analytics. These models have not only transcended traditional boundaries but also offered a complete overhaul in how data tasks are approached. This is particularly evident through their ability to assist with SQL queries and coding for data processing.
Data curation and preparation have often been hampered by the complexity and technical nature of SQL queries and data manipulation scripts. However, LLMs are changing the game by enabling users to generate queries or data processing code through straightforward natural language instructions.
In this example, we'll explore how LLMs can be utilized to perform a basic step in the data preprocessing of a process log. Specifically, we aim to encode a certain column of categorical data, a usual preprocessing step for machine learning models input. To demonstrate the flexibility of the process, we'll provide the LLM with the dataset and inquire about the potential preprocessing steps that could be undertaken:
You: Can you identify any pre-processing steps necessary in this dataset?
ChatGPT: The dataset appears to track activities related to some process, possibly related to customer service or task management, with each record containing information about a specific activity within a trace of actions. Based on the first few rows of the dataset, the following pre-processing steps could be necessary:
Date and Time Conversion: The start and end columns are in string format. For any time series analysis or operations requiring date/time arithmetic, these should be converted to a datetime format.
Categorical Data Encoding: Columns such as activity, CCgroup, Center, Contractores, FSresult, OpenWO, Type, and VariantId contain categorical data. Depending on the analysis or model to be used, you may need to encode these categorical variables into numerical form, using methods like one-hot encoding or label encoding.
Handling Missing Values: It's important to check for missing values and decide on a strategy to handle them, whether by imputation, removal, or some other method, depending on their impact and the proportion of missing data.
Normalization or Standardization of Numerical Values: If the dataset will be used for machine learning, the numerical values (e.g., index, duration) may need to be normalized or standardized, especially if you're using algorithms sensitive to the scale of data, such as distance-based algorithms.
Duplicate Records Check: Check for and decide how to handle any duplicate records, as they could skew analysis results or model training.
Outlier Detection and Handling: Outliers in data can significantly affect the outcomes of data analysis and predictive modeling. Identifying and appropriately handling outliers is important.
Consistency Checks: Verify the consistency of data, for example, ensuring that start dates are always before end dates, and that there are no negative durations.
et's perform some basic checks for missing values, duplicate records, and possible outliers to further refine the necessary pre-processing steps. ​​
The basic checks on the dataset reveal that:
There are no missing values in any of the columns, which means we don't need to handle missing data.
There are no duplicate records in the dataset, indicating uniqueness across all rows.
The numerical columns (index, duration) show a wide range of values, especially for duration where the maximum value is 5399 (presumably seconds) compared to a 75th percentile value of 126 seconds. This suggests the presence of outliers in the duration data that may need to be addressed.
Given these insights, the pre-processing steps recommended earlier remain relevant, with additional emphasis on analyzing and potentially handling outliers in the duration column. Depending on your specific analysis or modeling goals, handling outliers could involve removing them, applying transformations to reduce their impact, or investigating these cases separately to understand their nature. ​
Wow! That’s a lot of information. Impressively, the analysis even tackled some initial steps for us, identifying that there are no missing values or duplicates in the dataset. Next, we'll leverage ChatGPT's capabilities to perform one-hot encoding on the 'Center' column:
You: Please encode (with one-hot encoding) the values of the column 'Center'. Give me a python script for that task as well.
ChatGPT: The one-hot encoding for the 'Center' column has been successfully applied. Here's a Python script that demonstrates how you can perform this task:
This script first loads your dataset, applies one-hot encoding to the 'Center' column with pd.get_dummies(), and then joins the encoded columns back to the original dataset. You can adjust the file paths according to your needs.
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The process is remarkably straightforward, demonstrating the convenience of requesting not just analyses but also the direct application of transformations, such as one-hot encoding. While it's possible to request the transformed dataset as a CSV file directly, I prefer to run the provided code locally. This approach offers me more control and understanding of the process. This example, albeit simple, illustrates the ease with which we can now perform complex data transformations, making sophisticated data preprocessing tasks more accessible than ever.