10 Questions
What is the problem statement for the project?
The problem statement is to predict fraudulent credit card transactions using machine learning models.
Where was the dataset collected from?
The dataset was collected from the Kaggle website.
How many transactions are there in the dataset?
The dataset contains a total of 284,807 transactions.
Why is it important to handle the imbalance in the dataset before model building?
It is important to handle the imbalance in the dataset because there are only 492 fraudulent transactions out of 284,807, making it highly imbalanced.
Why is credit card fraud detection using machine learning considered a necessity in the banking industry?
Credit card fraud detection using machine learning is considered a necessity in the banking industry due to the rise in digital payment channels and the increasing number of fraudulent transactions.
What is the total number of transactions in the dataset?
284,807
Why is handling the imbalance in the dataset important before model building?
To prevent overfitting of the machine learning models
What is the estimated worldwide banking fraud amount by 2020 according to Nilson report?
$30 billion
Why is retaining high profitable customers a primary business goal for many banks?
To enhance trust and credibility
What is the source of the dataset used for credit card fraud detection in this project?
Kaggle Website
Explore the application of machine learning models to predict fraudulent credit card transactions using customer-level data. Analyze a dataset of 284,807 transactions collected from a research collaboration between Worldline and the Machine Learning Group. Dive into the challenges and techniques of credit card fraud detection.
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