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Data Science Projects with Python - Second Edition

You're reading from  Data Science Projects with Python - Second Edition

Product type Book
Published in Jul 2021
Publisher Packt
ISBN-13 9781800564480
Pages 432 pages
Edition 2nd Edition
Languages
Author (1):
Stephen Klosterman Stephen Klosterman
Profile icon Stephen Klosterman

Table of Contents (9) Chapters

Preface
1. Data Exploration and Cleaning 2. Introduction to Scikit-Learn and Model Evaluation 3. Details of Logistic Regression and Feature Exploration 4. The Bias-Variance Trade-Off 5. Decision Trees and Random Forests 6. Gradient Boosting, XGBoost, and SHAP Values 7. Test Set Analysis, Financial Insights, and Delivery to the Client Appendix

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')

Note

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...

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