infographic for regression

Regression/1-Tools/README.md

@@ -64,7 +64,9 @@ According to their [website](https://scikit-learn.org/stable/getting_started.htm
 
 > 🎓 **[Model Fitting](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py)** in the context of machine learning refers to the accuracy of the model's underlying function as it attempts to analyze data with which it is not familiar. **Underfitting** and **overfitting** are common problems that degrade the quality of the model as the model fits either not well enough or too well. This causes the model to make predictions either too closely aligned or too loosely aligned with its training data. An overfit model predicts training data too well because it has learned the data's details and noise too well. An underfit model is not accurate as it can neither accurately analyze its training data nor data it has not yet 'seen'.
 
-TODO: Infographic to show underfitting/overfitting like this https://miro.medium.com/max/1525/1*TzMis7bbuaU1OE2q64hnbg.png
+![overfitting vs. correct model](images/overfitting.png)
+
+> Infographic by [Jen Looper](https://twitter.com/jenlooper)
 
 > 🎓 **Data Preprocessing** is the process whereby data scientists clean and convert data for use in the machine learning lifecycle.