Machine Learning with Python – Part 2: Decision Tree
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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 2, we’ll create a Decision Tree classifier and visualize it using graphviz, pydotplus, scipy, and matplotlib! I’ll speak briefly about the advantages and disadvantages of Decision Tree classifiers.
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Throughout this 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
Link to the dataset on Kaggle: