## Magic: The Gathering Statistics: Clustering the Meta

This project combines two of my passions: Magic: The Gathering and Machine Learning. Let’s see how they mix with this recommender system!

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# Data Analysis

## Magic: The Gathering Statistics: Clustering the Meta

## Statistical Analysis: Pandas and Seaborn on a Kaggle Dataset

## Exploratory Data Analysis: Pandas Framework on a Real Dataset

## How to Run Parallel Data Analysis in Python using Dask Dataframes

## Exploratory Data Analysis with the Pandas Framework and Jupyter Notebooks

This project combines two of my passions: Magic: The Gathering and Machine Learning. Let’s see how they mix with this recommender system!

When doing Statistical Analysis, curiosity and intuition are two of a Data Scientist’s most powerful tools. The third one may be Pandas.

Today we’ll leverage Python’s Pandas framework for Data Analysis, and Seaborn for Data Visualization.Sometimes when facing a Data problem, we must first dive into the Dataset and learn about it. Its properties, its variables’ distributions — we need to immerse in the domain.

Sometimes you open a big Dataset with Python’s Pandas, try to get a few metrics, and the whole thing just freezes horribly. Dask Dataframes may solve your problem.

As a Data Scientist, I spend about a third of my time looking at data and trying to get meaningful insights, the discipline some call exploratory data analysis. These are the tools I use the most. Today we will be looking at two awesome tools, following closely the code I uploaded on this github project. One …

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