Pearson Correlation

Learn to calculate and plot the Pearson correlation.

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Calculating the Pearson correlation using pandas
Interpreting the correlation plot
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Calculating the Pearson correlation using pandas

Now we are ready to create our correlation plot. Underlying a correlation plot is a correlation matrix, which we must calculate first. Pandas makes this easy. We just need to select our columns of features and response values using the list we just created and call the corr() method on these columns. As we calculate this, note that the type of correlation available to us in pandas is linear correlation, also known as Pearson correlation. Pearson correlation is used to measure the strength and direction (that is, positive or negative) of the linear relationship between two variables:

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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

Pearson Correlation

Calculating the Pearson correlation using pandas