5. Model Validation and Optimization

Activity 5.01: Hyperparameter Tuning and Model Selection

Solution:

1. Create a new Jupyter notebook and load the following libraries:

   import pandas as pd
   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'] = (9, 6)
   plt.rcParams['axes.labelpad'] = 10
   sns.set_style("darkgrid")
   %load_ext watermark
   %watermark -d -v -m -p \
   numpy,pandas,matplotlib,seaborn,sklearn

2. Load the preprocessed Human Resource Analytics dataset by running the following code:

   df = pd.read_csv('../data/hr-analytics/hr_data_processed_pca.csv')
   df.columns

   This displays the following output:

   Figure 5.10: The columns of hr_data_processed_pca.csv

3. Select the features to include in the model and perform a train-test split on the data...