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Exercise: Exploring and Cleaning the Data

Learn to explore and clean the data for predictive modeling.

We'll cover the following...

Examining features and data quality
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Thus far, we have identified a data quality issue related to the metadata: we had been told that every sample from our dataset corresponded to a unique account ID, but found that this was not the case. We were able to use logical indexing and pandas to correct this issue. This was a fundamental data quality issue, having to do simply with what samples were present, based on the metadata. Aside from this, we are not really interested in the metadata column of account IDs: these will not help us develop a predictive model for credit default.

Examining features and

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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: Exploring and Cleaning the Data

Examining features and