🕵️‍♂️ Credit Card Fraud Detection

📄 Project Overview

Objective: Perform data cleaning and Exploratory Data Analysis (EDA) on a dataset of choice. Dataset: Credit Card Fraud Detection (Kaggle).

I chose this dataset to analyze financial anomalies, which aligns with my interest in Blockchain Security and Fintech.

📊 Key Findings

• High Imbalance: The dataset is heavily skewed, with frauds accounting for only 0.17% of transactions.
• Transaction Patterns: Fraudulent transactions are generally small in amount (mostly < $100) to avoid triggering bank security alarms.
• Correlations: The heatmap reveals that specific anonymized features (V17, V14, V12) have a strong negative correlation with the 'Class', making them key indicators for fraud.

🛠 Technologies Used

• Python (Pandas, NumPy)
• Seaborn & Matplotlib (For visualizations like Heatmaps and Histograms)
• Jupyter Notebook

🚀 How to View

Click on the .ipynb file above to view the code and the output graphs directly in GitHub.