Credit Card Fraud Detection

• 

  Accuracy

• 

  Receiver Operating Characteristic Curve for the model

• 

  f1 score

• 

  Summary of the model

Inspiration

Our project was inspired by the increasing prevalence of credit card fraud and the need for more robust detection methods.

What it does

Our project utilizes advanced machine learning algorithms to detect credit card fraud in real-time, safeguarding financial transactions.

How we built it

We collected a large dataset of credit card transactions, pre-processed the data, and trained our machine learning model using various algorithms.

Challenges we ran into

Dealing with imbalanced data and ensuring data privacy were significant challenges we encountered during the project.

Accomplishments that we're proud of

We successfully developed a practical solution for detecting credit card fraud and gained valuable insights into machine learning and financial data analysis.

What we learned

Throughout the project, we learned about different machine learning techniques, the importance of data privacy, and the complexities of financial transaction data analysis.

What's next for Credit Card Fraud Detection

In the future, we plan to further refine our model, integrate it into financial systems, and explore ways to improve detection accuracy while minimizing false positives.

Built With

• analytics
• data
• matplotlib
• model
• numpy
• pandas
• python
• scikit-learn
• seaborn
• sklearn
• standardscaler