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Credit Card Fraud Detection
By Awantik Das, Edyoda
What is Credit Card Fraud Detection
● It is important that credit card
companies are able to recognize
fraudulent credit card transactions so
that customers are not charged for
items that they did not purchase.
● Similar application is used in credit
providers end as well.
Objective
Technology
Stack
● Cassandra
● Scikit / TensorFlow /
PySpark
● Kafka
● Flask
Kafka
● Horizontally, distributed & scalable
streaming platform.
● Highly Available
● Commodity hardware
Cassandra
● Horizontally, distributed scalable storage.
● Highly available
● Commodity Hardware
Solution
Understanding Dataset
● This dataset presents transactions that occurred in two days, where we have 492
frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive
class (frauds) account for 0.172% of all transactions.
● Due to confidentiality issues, we cannot provide the original features and more
background information about the data. Features V1, V2, ... V28 are the principal
components obtained with PCA, the only features which have not been
transformed with PCA are 'Time' and 'Amount'.
● Feature 'Time' contains the seconds elapsed between each transaction and the
first transaction in the dataset. The feature 'Amount' is the transaction Amount,
this feature can be used for example-dependant cost-sensitive learning. Feature
'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
Study Materials
● Complete Data Science Code - https://github.com/zekelabs/data-science-complete-tutorial
● Credit Card Project Code - https://github.com/zekelabs/ai-project-fraud-detection
● Credit Card Project Videos - https://www.edyoda.com/course/1432
● More AI courses - https://www.edyoda.com/category/artificial-intelligence
Edyoda Journey
Contact
Awantik Das
awantik@edyoda.com
www.edyoda.com

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AI Fraud Detection - by Edyoda

  • 1. Credit Card Fraud Detection By Awantik Das, Edyoda
  • 2. What is Credit Card Fraud Detection ● It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. ● Similar application is used in credit providers end as well.
  • 4. Technology Stack ● Cassandra ● Scikit / TensorFlow / PySpark ● Kafka ● Flask
  • 5. Kafka ● Horizontally, distributed & scalable streaming platform. ● Highly Available ● Commodity hardware
  • 6. Cassandra ● Horizontally, distributed scalable storage. ● Highly available ● Commodity Hardware
  • 8. Understanding Dataset ● This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. ● Due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. ● Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
  • 9. Study Materials ● Complete Data Science Code - https://github.com/zekelabs/data-science-complete-tutorial ● Credit Card Project Code - https://github.com/zekelabs/ai-project-fraud-detection ● Credit Card Project Videos - https://www.edyoda.com/course/1432 ● More AI courses - https://www.edyoda.com/category/artificial-intelligence