4. Training Classification Models

Activity 4.01: Training and Visualizing SVM Models with Scikit-learn

Solution:

1. Create a new Jupyter notebook.
2. In the first cell, add the following lines of code to load the libraries we'll be using and set up our plot environment for the notebook:

   import numpy as np
   import datetime
   import time
   import os
   import matplotlib.pyplot as plt
   %matplotlib inline
   import seaborn as sns
   %config InlineBackend.figure_format='retina'
   sns.set() # Revert to matplotlib defaults
   plt.rcParams['figure.figsize'] = (8, 8)
   plt.rcParams['axes.labelpad'] = 10
   sns.set_style("darkgrid")

3. In the next cell, enter the following code to print the date, version numbers, and hardware information:

   %load_ext watermark
   %watermark -d -v -m -p \
   requests,numpy,pandas,matplotlib,seaborn,sklearn

   You should get the following output:

   Figure 4.19: Output of loading all the required libraries

4. Load the preprocessed Human Resource...