Machine Learning with Python - Part 1: Spotify EDA

In this series, we'll explore machine learning with Python by building a classifier to determine whether or not we might like a song based on its attributes, which are provided by the Spotify API. We'll use an existing data set from Kaggle to explore and implement various classifiers.

In Part 1, we'll get set up with Python by starting a new Jupyter notebook and perform some basic Exploratory Data Analysis on the song dataset to begin to examine its structure.

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Throughout the entire series, we'll:

- Perform Exploratory Data Analysis (EDA) in a Jupyter Notebook using Pandas, Numpy, matplotlib, and other commonly-used libraries

- Build a Decision Tree classifier using scikit-learn

- Build a Random Forest classifier using scikit-learn

- Build an Artificial Neural Network classifier using Keras

Links:

Link to the dataset on Kaggle:
https://www.kaggle.com/geomack/spotifyclassification

Intro track is adapted from "despondency" by Fog Lake, used with permission from the artist. Go check out his music, it's fantastic:
https://foglake.bandcamp.com/

Anaconda:
https://www.continuum.io/downloads

Видео Machine Learning with Python - Part 1: Spotify EDA канала Wes Doyle