srushti-9 / credit-card-fraud-detection

The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be fraud. This model is then used to identify whether a new transaction is fraudulent or not. Our aim here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. The data set has 31 features, 28 of which have been anonymized and are labeled V1 through V28. The remaining three features are the time and the amount of the transaction as well as whether that transaction was fraudulent or not.

Credit-Card-Fraud-Detection

The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be fraud. This model is then used to identify whether a new transaction is fraudulent or not. Our aim here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. The data set has 31 features, 28 of which have been anonymized and are labeled V1 through V28. The remaining three features are the time and the amount of the transaction as well as whether that transaction was fraudulent or not.

-->Observations

1. The data set is highly skewed, consisting of 492 frauds in a total of 284,807 observations. This resulted in only 0.172% fraud cases. This skewed set is justified by the low number of fraudulent transactions.
2. The dataset consists of numerical values from the 28 ‘Principal Component Analysis (PCA)’ transformed features, namely V1 to V28. Furthermore, there is no metadata about the original features provided, so pre-analysis or feature study could not be done.
3. The ‘Time’ and ‘Amount’ features are not transformed data.
4. There is no missing value in the dataset.

-->Inferences drawn:

1. Owing to such imbalance in data, an algorithm that does not do any feature analysis and predicts all the transactions as non-frauds will also achieve an accuracy of 99.828%. Therefore, accuracy is not a correct measure of efficiency in our case. We need some other standard of correctness while classifying transactions as fraud or non-fraud.
2. The ‘Time’ feature does not indicate the actual time of the transaction and is more of a list of the data in chronological order. So we assume that the ‘Time’ feature has little or no significance in classifying a fraud transaction. Therefore, we eliminate this column from further analysis.