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In the current AI/ML landscape, language models have taken center stage - with demonstrations like ChatGPT. Although ChatGPT is an NLP model at its core, it has shown promise for different data analysis tasks as well.
In this article, we will explore how ChatGPT can be useful for data exploration, preprocessing, descriptive analytics, predictive modeling, as well as natural language processing and enhancing explainability.
Data exploration and pre-processing are the first step in any data analysis project. These stages include summarizing key statistics and patterns, finding missing values, and detecting outliers and anomalies in the dataset, followed by data-cleaning and preprocessing, among others.
ChatGPT is useful for summarizing data sets, finding out what the patterns are in your data and how that addresses or helps make clear what the structure of the dataset looks like.
Given a dataset, ChatGPT can calculate basic summary statistics like mean, median, mode or standard deviation. This gives users an immediate idea of both the central tendency and dispersion present within their data.
Alternatively, ChatGPT can detect missing values and recommend how to deal with them using imputation techniques or flagging those rows for review.
Moreover, ChatGPT can compare data points to expected ranges or distributions and, as such, is able to distinguish outliers and anomalies. These insights are aimed at assisting users in deciding whether to delete these anomalies or study them deeper.
ChatGPT can even suggest data cleaning techniques like normalization/standardization, or encoding for categorical variables which are an important step in preprocessing a dataset before analyzing it using deep learning algorithms.
Descriptive analytics refers to the process of summarizing and interpreting historical data into meaningful patterns - facts that we wish to describe. This is where ChatGPT overperforms, as with the help of EDA (exploratory data analysis), it creates visualizations or reports.
ChatGPT can quickly generate and interpret different plots, like histograms, bar charts, scatterplots, or box plots. These visualizations help users know the distribution of data and variables, the relationships among them, and any anomalies in that particular dataset.
In addition, following correlation analysis using ChatGPT can help identify possible predictors or influential attributes.
In addition, ChatGPT can write full-fledged reports and presentations that summarise the findings of data analysis. Including key stats, visualizations and interpretations in these reports, can help stakeholders better understand the results and make data driven decisions.
Predictive modeling is a technique that began way back in the 1600s using historical data to predict future events or trends. ChatGPT can help with feature engineering and selection, and build machine learning models if you include input data sets for model creation or evaluations.
Feature engineering is the process of creating new features or transforming existing features to increase model performance, as it plays an important role in predictive modeling.
By identifying appropriate characteristics that the data and problem selection would suggest, ChatGPT proposes a blueprint for users to improve their models. Besides that, ChatGPT helps in feature selection by indicating the most important variables for the predictive task.
ChatGPT can also assist in selecting suitable algorithms and parameters used for building, and training models with machine learning.
It is not intended as machine learning model training but could help a reader understand the strengths and weaknesses of different algorithms, for example linear regression, decision trees or neural networks.
Not just that, ChatGPT can also help us evaluate model performance by suggesting correct metrics to use, like accuracy or precision recall, squared error, and explaining the results.
ChatGPT is optimized around natural language processing (NLP), the core that it was built upon, and understandably, it excels in this field. Using ChatGPT, you can check text information like client audits or web-based media posts and then investigate gathered outcomes.
One of the popular NLP tasks is sentiment analysis, which can be done with ChatGPT to understand sentiments and emotions from certain text data.
Businesses can use sentiment analysis of customer reviews to gauge how satisfied customers are with the service and see where they could improve. Moreover, ChatGPT is capable of identifying essential entities, topics, and themes from the unstructured text, which helps make better sense of it.
ChatGPT could also summarize and extract insights from big text corpora, meaning it can digest millions of pieces of information more easily. A feature that is extremely helpful for literature reviews, market research or customer feedback analysis.
Conversational Q&A is one of the strong points for ChatGPT. This is just one feature among many that makes it a powerful tool for improving explainability and explaining complex concepts.
ChatGPT can give information about data, methodology used for analysis and generated results that help put everything into context. Users may choose to ask ChatGPT for simplified explanations of statistical concepts, machine learning algorithms or initial processing data techniques.
This makes it easier for the layman to understand complex concepts and engage in well informed problem solving.
In addition, to all of the above analysis, ChatGPT can also provide next steps and recommendations. From recommending the need for more data collection, alternative analysis to actions based on your findings, ChatGPT can take you through all of this.
Though ChatGPT is quite helpful for basic data analysis tasks, we should not be ignoring its limitations. There are already more specific tools for data analysis, such as Pandas or NumPy through scikit-learn, TensorFlow, etc., and ChatGPT cannot replace these.
Dedicated software and libraries should be used for advanced data analysis tasks. On the other hand, ChatGPT has less ability to use real-time data (it can take certain static information into account during inference but cannot actually look up new external information at neural-net level).
You need to cross-verify the suggestions/interpretations made by ChatGPT with relevant domain expertise and ensure that the final analysis satisfies the needed specifications.
ChatGPT worked quite well as a general-purpose tool to perform data analysis ranging from basic exploration and preprocessing through predictive modelling to solving NLP problems.
It provides summary statistics about your data and the research design you are using to test a hypothesis. That said, for less traditional tasks and more specialized ones, it is still essential to have reliable old data analysis tools and libraries.
When used alongside dedicated data analysis software - for the bits that need a deeper dive or number-crunching - their collective powers can help make minds sharper, capable of doing more with discernibly less.
