Predict Bike Sharing demand in Washington using Python and Sci-Kit Learn.

At the moment machine learning is crossing the chasm, from early adopters to early majority. Even though concepts of this field can be traced back to the 50s, big breakthroughs have been made in the last five years.

Apart from that, there are many machine learning libraries available, especially for data science languages like Python and R. That is one of the reasons why this field is more opened for general use.

In this lecture, we will see how we can use one of those libraries in Python – Sci-Kit Learn. We will focus on issues that data scientist can face when presented with the problem. For this purpose, we will use the Bike-Sharing Demands dataset.

This dataset is containing the data generated by the Capital Bikeshare program in Washington, D.C. In essence, Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city.

We will use this historical and weather data, machine learning, Sci-Kit Learn and Python to predict future bike-sharing demands for this city.


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Very interesting talk, and good entry level material(I think?) to a very complex topic. Real-world example that was easy to follow. The speaker came well-prepared, but has to work a bit on his energy and tone while presenting, to keep up the focus of the audience, but I'm sure that will get better with more talks presented. Looking forward to see more from the speaker!