Book Image

Hands-On Data Analysis with Pandas

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas

By: Stefanie Molin

Overview of this book

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with 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 powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able 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. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
Table of Contents (21 chapters)
Free Chapter
1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications - Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Making Better Predictions - Optimizing Models

In the previous chapter, we learned how to build and evaluate our machine learning models. However, we didn't touch upon what we can do if we want to improve their performance. Of course, we could try out a different model and see if it performs better—unless there are requirements that we use a specific method for legal reasons or in order to be able to explain how it works. We want to make sure we use the best version of the model that we can, and for that, we need to discuss how to tune our models.

This chapter will introduce techniques for optimization of machine learning model performance using scikit-learn, as a continuation of the content in Chapter 9, Getting Started with Machine Learning in Python. Nonetheless, it should be noted that there is no panacea. It is entirely possible we try everything we can think...