Data-Nash
The YouTuber compares Google's new language model, Bard, with ChatGPT-4 in terms of handling data science tasks, such as coding, data cleaning, and time-series forecasting. Bard provides fuller datasets and quicker answers, while ChatGPT-4 has more traffic and gives answers line by line. Both models suggest that 50 rows are considered a good starting point for datasets. The YouTuber uses the Supermarket Sales dataset and finds that GPT-4's code for cleaning and prediction works better than Bard's. Bard is useful for customer segmentation and optimized pricing/profitability analysis. The YouTuber faces errors in using Bard for data visualization and report generation, while GPT-4 provides good recommendations for the report.
In this section of the video, the YouTuber describes their experiment comparing Google's new language model, Bard, with ChatGPT-4 in terms of their handling of data science tasks. The YouTuber compares the two models in terms of how well they handle coding, simple data science coding projects, and time series forecasting using generic data sets with location sales dates. The YouTuber finds that Bard is quicker than ChatGPT-4 in giving answers and provides fuller data sets, but ChatGPT-4 has more traffic and gives the answers line by line. Additionally, both Bard and ChatGPT-4 suggest that 50 rows is considered a good starting point for data sets, depending on the model's complexity and the amount of noise in the data. Overall, the YouTuber's experiment is a simple comparison of the two models and their capabilities in handling data science tasks.
In this section, the YouTuber compares the data frames provided by Google's GPT-4 and Bard and finds that GPT's data frame is longer and already in daytime format. The YouTuber decides to use the Supermarket Sales data set from Kaggle and asks both GPT-4 and Bard for suggestions on what to do with the data. Both suggest identifying trends and customer segmentation, with Bard adding in optimized pricing/profitability analysis. The YouTuber then proceeds to ask both for suggestions on how to clean the data set and both suggest removing duplicates, standardizing the data, and converting it into an easy-to-analyze format. However, when trying to execute Bard's cleaning code, it converts the data frame to a series instead of keeping the data type intact. On the other hand, GPT's cleaning code is better, removing duplicates, potential outliers, and offering placeholder suggestions. Finally, both GPT-4 and Bard suggest ways to predict sales, with GPT's code working better and Bard being able to read and confirm its correctness.
In this section of the video, the user tries to use the Google Bard language model to generate code for data visualization and report generation. However, they faced some errors, and the Bard model was closer to following the word limits. But GPT-4 gave good recommendations for the report. The user continues to mention that they are a data science newbie looking to become an elite data scientist one day and invite viewers to check out their channel for more content.
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