AIM

Develop a model for predicting fraudulent transactions for a financial company and use insights from the model to develop an actionable plan.

DATASET

https://www.kaggle.com/datasets/miznaaroob/fraudulent-transactions-data

CONTENT

Data for the case is available in CSV format having 6362620 rows and 10 columns.

WHAT I HAD DONE

First I imported all the required libraries and dataset for this project. Then I did some EDA to find which mode of transaction results into most fraudulent transactions. Then I worked throught to treat any inconsistency in the data. Then I proceeded to build the model. I worked two different models and compared results from both to select mode appropriate one for this project. First I used a logistic regression model to classify Fraudulent and Non fraudulent transactions. Next I worked with Random Forest classifier model to amp up the accuracy which resulted in some improvement from the previous LR moel. At the end I observed an accuracy of 99.97.

MODELS USED

The models are:

1. Logistic Regression
2. Random Forest Classifier

HOW TO RUN

Upload kaggle api key file and fraud_transaction_detection.ipynb file on colab and just run the code.

LIBRARIES NEEDED

• Opendatasets (for downloading the dataset)
• Pandas - for data analysis
• Numpy - for data analysis
• matplotlib - for data visualization
• seaborn - for data visualization
• itertools - for data analysis

CONCLUSION

I was successfully able to find the most accurate model to detect fraudlent transactions.

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