You are in charge of implementing machine learning models to classify waveforms into one out of
3 possible classes from a set of 40 continuous input features.
These input features are corrupted by noise.

The dataset has been partitioned for you in 2 files, used respectively for training and testing your models:
- A training set with 4000 examples, available here: Waveform_train.csv
- A test set with 1000 examples, available here: Waveform_test.csv
Those .csv
files should be read and loaded in data frames:
import pandas as pd train_df = pd.read_csv("Waveform_train.csv") test_df = pd.read_csv("Waveform_test.csv")
The first 40 columns correspond to the input features, named x_1
to x_40
and the last column, denoted labels
, encodes the class label of each example.
In this task, we will evaluate how support vector machines fitted on the training set can be used to predict the class labels of the test examples.
SVMs are implemented in Python in sklearn.svm.SVC.