Exercise: Loading the Case Study Data in a Jupyter Notebook

Let's load the data in our first Jupyter notebook.

We'll cover the following

Loading the case study data using pandas
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Loading the case study data using pandas

Now that we’ve learned about Jupyter notebooks, the environment in which we’ll write code, and pandas, the data wrangling package, let’s load the data of the case study in our Jupyter notebook. We’ll use pandas within this notebook to load the case study data and briefly examine it.

Note: A Jupyter notebook is provided at the end of this lesson. If you want to create a new Jupyter notebook locally please refer to the appendix.

Perform the following steps to complete the exercise:

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Introduction

Data Exploration and Cleaning

(Challenge) Exploring Remaining Financial Features in Dataset

Introduction to scikit-learn and Model Evaluation

(Challenge) Logistic Regression and Precision-Recall Curve

Details of Logistic Regression and Feature Extraction

(Challenge) Logistic Regression Model and Coefficients

The Bias-Variance Trade-Off

(Challenge) Cross-Validation and Feature Engineering

Decision Trees and Random Forests

(Challenge) Cross-Validation Grid Search with Random Forest

Gradient Boosting, XGBoost, and SHAP Values

(Challenge) XGBoost and SHAP Explanation for Case Study Data

Test Set Analysis, Financial Insights, and Delivery to the Client

(Challenge) Deriving Financial Insights

Appendix

Exercise: Loading the Case Study Data in a Jupyter Notebook

Loading the case study data using pandas