Title: Fraud Detection Using Linear Regression Model

The dataset comprises credit card transactions over two days, with 492 instances of fraud out of 284,807 transactions, making the dataset highly imbalanced. The positive class (frauds) represents only 0.172% of all transactions. The dataset includes numerical features resulting from PCA transformation, with 'Time' and 'Amount' being the only non-transformed features. 'Time' indicates the seconds elapsed since the first transaction, while 'Amount' denotes the transaction amount. The 'Class' feature serves as the response variable, taking a value of 1 for fraud and 0 otherwise.

For data analysis and prediction, a linear regression model is employed. The model identifies highly correlated variables with the 'Class' feature and predicts the correct number of fraudulent and non-fraudulent transactions based on these correlations. This approach aims to enhance fraud detection by leveraging linear regression's predictive capabilities on the given dataset.