Fix python snippets formatting & update ROC image

2-Regression/4-Logistic/README.md

@@ -135,6 +135,7 @@ Then, `ColumnTransformer` is used to combine multiple encoders into a single ste
 
 ```python
     from sklearn.compose import ColumnTransformer
+    
     ct = ColumnTransformer(transformers=[
         ('ord', ordinal_encoder, ordinal_features),
         ('cat', categorical_encoder, categorical_features)
@@ -347,7 +348,7 @@ plt.show()
 ```
 Using Matplotlib, plot the model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. "ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis." Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly:
 
-![ROC](./images/ROC.png)
+![ROC](./images/ROC_2.png)
 
 Finally, use Scikit-learn's [`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) to compute the actual 'Area Under the Curve' (AUC):