This project aims to explore and analyze card transaction data to gain insights into fraud transactions and create predictive models. The project is divided into four main parts:
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Card Transaction Data Exploration: In this phase, we perform exploratory data analysis (EDA) to understand the dataset's structure, identify patterns, and visualize key insights.
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Transaction Variable Creation: This step involves creating new variables or features from the existing data that could potentially improve the predictive power of our models.
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Transaction Feature Selection: Here, we select the most relevant and informative features from the dataset to build our predictive models. This process helps in reducing complexity and improving model performance.
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Transaction Models: In this final stage, we develop predictive models based on the selected features to predict and recognize future fraudulent transactions. We evaluate the performance of different models and choose the best one for deployment.
To run this project, you need to have Python and the required libraries(mentioned in Jupyter Notebook) installed. Clone this repository and execute the script in the order in the description to start exploring and modeling card transaction data.