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season

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In this notebook, we will use scikit-learn to perform a decision tree based classification of weather data. The file daily_weather.csv is a comma-separated file that contains weather data. This data comes from a weather station located in San Diego, California. The weather station is equipped with sensors that capture weather-related measurements such as air temperature, air pressure, and relative humidity. Data was collected for a period of three years, from September 2011 to September 2014, to ensure that sufficient data for different seasons and weather conditions is captured. Let's now check all the columns in the data.

  • Updated Oct 12, 2018
  • Jupyter Notebook

This is the first application of the (admittedly little) amount of Machine Learning content that I know. I aim to initially create a small programme that can guess whether a made up stat-line would be more likely to be performed by a certain player (using the most recent 2019 NBA finals stats), and then build out a a system that would use data from 1950 - 2017 to do the same thing (with better accuracy).

  • Updated Feb 27, 2020
  • Python

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