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Join date: May 13, 2022

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As a student I have spent countless hours writing coding tutorials, completing quizzes, and working my way up the Kaggle leaderboard. During this time I noticed that there is a huge disconnect between teaching how to code and teaching how to find your data. In this post I want to show you how to start using data from Kaggle and learn data science while exploring code with notebooks. This post is a collection of my favorite exercises and tools to find your data. The R-Squared Kaggle Metric This week I noticed that R-squared is a metric used by several Kaggle teams to evaluate the quality of their models. The problem is that it is not immediately obvious how to calculate this metric. You can find instructions to calculate R-squared here, but all of the math is done by the author. The best way to learn this metric is to implement it yourself by taking data from the Kaggle leaderboard. A common technique to check if your implementation is correct is to compare the Kaggle leaderboard leaderboard. Here is a code challenge: Take the top 5 scoring teams (those in the orange bracket) from the 2016 data set. Analyze and find an R-squared for each team’s best model. Compare each team’s leaderboard rankings to the actual R-squared of their model. Are the teams that had the highest R-squared in their models on the top of the leaderboard? If you are having trouble with this challenge, here is a pastebin with a basic implementation of the R-squared metric. The data set for this challenge can be downloaded here. By downloading the data set you will be able to calculate R-squared on your own, without any programming knowledge. At first it is going to be tough to find any patterns in your data. But as you continue to practice and repeat this exercise you will see how this metric can be applied. Machine Learning Algorithms with Jupyter Notebooks I started teaching machine learning algorithms using the iPython Notebook platform. At first I used RStudio to write Jupyter Notebooks for my data science assignments. But as my assignments grew in size and complexity, I needed something better. This week I discovered the Plotly Notebooks feature of Jupy

 

 


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