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Pandas 2.0

Analytics Vidhya

Introduction If you work with programming languages and are familiar with Python, you must have had a brush with Pandas, a robust yet flexible data manipulation and analysis library. It was founded by Wes McKinney in 2008. appeared first on Analytics Vidhya.

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How to avoid the 7 most common mistakes of Big Data analysis

Dataconomy

You could dive into gigabytes or even petabytes of data from any industry and derive meaningful interpretations that may catch even the industry insiders by surprise. When the global financial crisis hit the American market in 2008, few.

Big Data 195
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Applications of Machine Learning and AI in Banking and Finance in 2023

Analytics Vidhya

Introduction Could the American recession of 2008-10 have been avoided if machine learning and artificial intelligence had been used to anticipate the stock market, identify hazards, or uncover fraud? The recent advancements in the banking and finance sector suggest an affirmative response to this question.

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Fundamentals of Python Programming for Beginners

Analytics Vidhya

Introduction If you’ve been in the data field for quite some time, you’ve probably noticed that some technical skills are becoming more dominant, and the data backs this up. Until the release of NumPy in 2005, Python was considered slow for numeric analysis. But Numpy changed that.

Python 285
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t-SNE (t-distributed stochastic neighbor embedding)

Dataconomy

With applications ranging from genomics to image processing, t-SNE helps bridge the gap between intricate data environments and actionable insights. t-SNE was developed by Laurens van der Maaten and Geoffrey Hinton in 2008 to visualize high-dimensional data. What is t-SNE (t-distributed stochastic neighbor embedding)?

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Pascal VOC

Dataconomy

Data analysis: Enabling automated data collection for better insights and decision-making. Benchmarking in object detection Established in 2008, Pascal VOC became a benchmark for comparing different object detection models. Safety: Improving the accuracy of security systems and enhancing public safety measures.

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Netflix Data Analysis using Python

Mlearning.ai

Photo by Juraj Gabriel on Unsplash Data analysis is a powerful tool that helps businesses make informed decisions. In this blog, we’ll be using Python to perform exploratory data analysis (EDA) on a Netflix dataset that we’ve found on Kaggle. df['rating'].replace(np.nan, value_counts()[:20].plot(kind="bar",color="Blue")