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Credit Card Fraud Detection.ipynb

@@ -3097,7 +3097,7 @@
     "- how cheap would a false positive be? <br><br>\n",
     "- Would we keep the Ada Boost Classifier with the **best performance in detecting frauds**, while also detecting a lot of false positives or should we use the Random Forest Classifier, who also performed pretty well identifiying frauds (82.50% recall) and reduced the number of false positives (0.02% of genuine transactions flagged as fraud). But that would also imply in a larger number of fraudsters getting away with it and customers being mistakenly charged... <br><br>\n",
     "\n",
-    "These questions and a deeper understading of how the business work and how we want to approach the solution of a problem using machine learning are fundamental for a decision taking process to choose whether or not if we're willing to deal with a larger number of false positives to detect the largest amount of frauds as possible. <br><br>\n",
+    "These questions and a deeper understading of how the business works and how we want to approach solving a problem using machine learning are fundamental for a decision-making process to choose whether or not if we're willing to deal with a larger number of false positives to detect the largest amount of frauds as possible. <br><br>\n",
     "\n",
     "Thank you so much!<br><br>\n",
     "*Luís Fernando Torres*"