Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Overview

Credit-Card-Fraud-Detection

Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score Different Kernels are used to detect whether a transaction is normal or fraud. The goals of this project are:

  1. Understand the distribution of the data provided
  2. create a 50/50 split dataset of Fraud and Non-Fraud transactions
  3. Determine the classifier to be used and finding out which has the highest accuracy
  4. Create a Neural network and compare the efficiency to our best classifier
  5. Understand common mistakes made with an imbalanced dataset. Imbalanced dataset requires f-1 score, confusion matrix or precision/recall score
Owner
Thines Kumar
Aspiring Data Scientist with experience in blockchain technology and solar energy!
Thines Kumar
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