Machine learning algorithm to classify music by metedata

Today, we'll be examining data compiled by a research group known as The Echo Nest. Our goal is to look through this dataset and classify songs as being either 'Hip-Hop' or 'Rock' - all without listening to a single one ourselves. In doing so, we will learn how to clean our data, do some exploratory data visualization, and use feature reduction towards the goal of feeding our data through some simple machine learning algorithms, such as decision trees and logistic regression. Hopefully with musical features we would be able to classify music genres with an acceptable accuracy.

• Load the Dataset from two various sources, examine the data and understand the features.
• Merge into a single Dataset. with required features and targetLabels, "Hip-Hop"/"Rock".
• Normalize data using StandardScalerto decrease bias.
• Utlilize PCA to enhance model interpretibility and performence.
• Develop Decision Tree and Logisitic Regression to predict the desired class.
• Undersampling to overcome class imbalances.
• Apply Cross Validation to evaluate which model generalize better for the given dataset.