Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
5 (1)
Book Image

Hands-On Data Analysis with Pandas - Second Edition

5 (1)
By: Stefanie Molin

Overview of this book

Extracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.
Table of Contents (21 chapters)
Section 1: Getting Started with Pandas
Section 2: Using Pandas for Data Analysis
Section 3: Applications – Real-World Analyses Using Pandas
Section 4: Introduction to Machine Learning with Scikit-Learn
Section 5: Additional Resources

Hyperparameter tuning with grid search

No doubt you have noticed that we can provide various parameters to the model classes when we instantiate them. These model parameters are not derived from the data itself and are referred to as hyperparameters. Some examples of these are regularization terms, which we will discuss later in this chapter, and weights. Through the process of model tuning, we seek to optimize our model's performance by tuning these hyperparameters.

How can we know we are picking the best values to optimize our model's performance? One way is to use a technique called grid search to tune these hyperparameters. Grid search allows us to define a search space and test all combinations of hyperparameters in that space, keeping the ones that result in the best model. The scoring criterion we define will determine the best model.

Remember the elbow point method we discussed in Chapter 9, Getting Started with Machine Learning in Python, for finding a good...