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EDA on SuperStore Dataset Using Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Table of Contents Introduction Working with dataset Creating loss dataframe Visualizations Analysis from Heatmap Overall Analysis Conclusion Introduction In this article, I am going to perform Exploratory Data Analysis on the Sample Superstore dataset.

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The 6 best ChatGPT plugins for data science 

Data Science Dojo

ChatGPT plugins can be used to extend the capabilities of ChatGPT in a variety of ways, such as: Accessing and processing external data Performing complex computations Using third-party services In this article, we’ll dive into the top 6 ChatGPT plugins tailored for data science.

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How To Learn Python For Data Science?

Pickl AI

Summary: Python for Data Science is crucial for efficiently analysing large datasets. Introduction Python for Data Science has emerged as a pivotal tool in the data-driven world. Key Takeaways Python’s simplicity makes it ideal for Data Analysis. in 2022, according to the PYPL Index.

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Fine-Tuning Legal-BERT: LLMs For Automated Legal Text Classification

Towards AI

Whether you’re a data scientist aiming to deepen your expertise in NLP or a machine learning engineer interested in domain-specific model fine-tuning, this tutorial will equip you with the tools and insights you need to get started. For this tutorial, we’ll download and prepare the dataset using Hugging Face’s datasets library.

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How Exploratory Data Analysis Helped Me Solve Million-Dollar Business Problems

Towards AI

Photo by Luke Chesser on Unsplash EDA is a powerful method to get insights from the data that can solve many unsolvable problems in business. In the increasingly competitive world, understanding the data and taking quicker actions based on that help create differentiation for the organization to stay ahead!

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Building an End-to-End Machine Learning Project to Reduce Delays in Aggressive Cancer Care.

Towards AI

This article seeks to also explain fundamental topics in data science such as EDA automation, pipelines, ROC-AUC curve (how results will be evaluated), and Principal Component Analysis in a simple way. One important stage of any data analysis/science project is EDA. Exploratory Data Analysis is a pre-study.

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Teaching with DrivenData Competitions

DrivenData Labs

We give recommendations and examples below, with instructors of college or graduate level data science or applied statistics courses in mind. Variations: For practice with data wrangling, students can find, download, and prepare data for analysis as part of the assignment. Difficulty: All skill levels.