Web Scraping to Data Visuals with GPT-4: An Introductory Tutorial

Web Scraping to Data Visuals with GPT-4: An Introductory Tutorial.

Overview

Author(s): John Loewen, PhD

Originally published on Towards AI.

From website to charts and maps in less than 15 minutes

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DallE-2 image: impressionist painting in oil colors of a person analyzing a bar chart on a computer screen

The ability to extract, process, and visualize data from the web is a skill that’s increasingly in demand.

GPT-4, with its suite of plugins, offers a seamless solution to this challenge.

Here, I use a practical, real example to walk you through the process of using GPT-4 to scrape renewable energy data from a web page, and then to visualize it with charts and maps.

Let’s get to it!

First off, the process that we are undertaking from beginning to end:

Data Extraction: Start by identifying your… Read the full blog for free on Medium.

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Published via Towards AI

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AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.