For each entry in the dataset, the following is provided:
- Triaxial acceleration from the accelerometer and the approximate acceleration of the body
- Triaxial angular velocity from the gyroscope
- Time and frequency domain variables with 561-feature vector
- Various labels of activity
- An identifier of the subject who was observed
By referring to the following steps, you will learn how to build a multi-class classification using SVMs:
- Let's quickly import all the necessary libraries that you will need in order to implement an SVM with multi-class classification:
In [1]: import numpy as np... import pandas as pd... import matplotlib.pyplot as plt ... %matplotlib inline... from sklearn.utils import shuffle... from sklearn.svm ...
