Book Image

Data Science Projects with Python - Second Edition

By : Stephen Klosterman
Book Image

Data Science Projects with Python - Second Edition

By: Stephen Klosterman

Overview of this book

If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects. You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.
Table of Contents (9 chapters)

Exploring the Financial History Features in the Dataset

We are ready to explore the rest of the features in the case study dataset. First set up the environment and load data from the previous exercise. This can be done using the following snippet:

import pandas as pd
import matplotlib.pyplot as plt #import plotting package
#render plotting automatically
%matplotlib inline
import matplotlib as mpl #additional plotting functionality
mpl.rcParams['figure.dpi'] = 400 #high resolution figures
import numpy as np
df = pd.read_csv('../../Data/Chapter_1_cleaned_data.csv')


The path to your CSV file may be different depending on where you saved it.

The remaining features to be examined are the financial history features. They fall naturally into three groups: the status of the monthly payments for the last 6 months, and the billed and paid amounts for the same period. First, let's look at the payment statuses. It is convenient to break these out...