Stay Safe Online: Predicting Credit Card Fraud with Data Science

Concerned about Credit Card Fraud? Let's Build an ML Model to Tackle the Issue Head-On!

Feel secure with every swipe, thanks to Machine Learning. It watches for any signs of fraud in your transactions, keeping your money safe by spotting suspicious patterns before they cause harm.

Hey there! I'm Pavan Kalyan, diving into my project. Join me as I explore the world of data science!

Introduction To Topic: Credit card fraud happens when someone makes purchases or transactions using your card without your permission. It's like someone stealing your wallet and using your money to buy things. In simple terms, it's illegal and means you end up paying for things you didn't buy.

Objective: My project aims to develop a clever computer program (ML model) that can quickly identify fraudulent credit card transactions. The main goal is to protect both the bank and its customers. For the bank, it means stopping fraudsters in their tracks. And for customers, it means keeping their money safe and ensuring a smooth experience with their credit cards.

Data Collection and Data Preparation: I collected the data for my project from a website called Kaggle. But before getting started, I took some time to clean it up. I checked for any duplicates, made sure nothing important was missing, and looked out for any unusual values. After that, my data was all set and good to go for analysis!

Methodology: I created three models to detect fraud transactions: logistic regression, random forest, and adaboost classifier. I chose logistic regression because it's good at sorting things into two categories (fraud or not fraud). Random forest is great for getting an overall accurate prediction by combining lots of little decisions. And for an extra boost in performance, I used adaboost classifier. It's like having a team of experts working together to make the model even better.

Analysis: Finding the fraud transactions.

In my dataset, there are more fraud transactions than normal ones. Specifically, there are around 9,000 fraud transactions compared to just over 7,000 normal transactions. This means that fraud transactions outnumber normal ones.

Analysis: Gender and Martial statues for fraud transactions.

I noticed that fraud transactions are more common among men than women. This trend holds true for both married and single individuals. Surprisingly, I found that even those categorized as 'unknown' or 'divorced' were affected by fraud, regardless of gender

Analysis: Correlation

In our dataset, I've calculated the correlation between different variables and the outcome. The strongest correlation we found is between the transaction amount and the outcome, with a value of about 0.70. This means that the transaction amount has a significant influence on whether a transaction is classified as normal or fraudulent. Additionally, we observed a smaller correlation between the average income expenditure column and the outcome, indicating some relationship there as well.

Analysis: Transactions count by customer city

In my dataset, fraud and normal transactions seem to occur at similar rates across all cities.

Scores of logistic regression :

My logistic regression model achieved an accuracy of 84%, meaning it correctly identified transactions as normal or fraudulent most of the time. It performed well in identifying both normal (80%) and fraudulent (88%) transactions, with slight room for improvement. However, it incorrectly classified some normal transactions (204) as fraudulent and missed some fraudulent transactions (306).

Scores of random forest:

MY random forest model achieved an accuracy of 89%, indicating strong performance in classifying transactions as normal or fraudulent. It demonstrated high precision (95%) in identifying normal transactions and good recall (82%) for fraudulent transactions. However, it misclassified some normal transactions (64) as fraudulent and missed some fraudulent transactions (273).

Scores of AdaBoost classifier model:

My AdaBoost classifier model achieved an accuracy of 88%, indicating solid performance in distinguishing between normal and fraudulent transactions. It demonstrated high precision (94%) in identifying normal transactions and good recall (82%) for fraudulent transactions. However, it misclassified some normal transactions (78) as fraudulent and missed some fraudulent transactions (265).

Conclusion

Building machine learning models to detect credit card fraud is an essential step towards ensuring the security of financial transactions. Through data collection, preparation, and analysis, we've gained valuable insights into the prevalence and patterns of fraud transactions.

Our logistic regression, random forest, and AdaBoost classifier models have shown promising results in accurately identifying fraudulent transactions, with each model exhibiting its strengths and areas for improvement.

By leveraging these models, financial institutions can enhance their fraud detection capabilities and protect both themselves and their customers from potential financial losses.

Join me on this journey as I explore more exciting projects together and unlock new insights in the world of data science.

Feel free to browse through my other projects for further insights.

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