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This project focuses on the detection of credit card fraud using various data science and machine learning techniques. The dataset includes a record of credit card transactions over a specific period, with the goal of accurately identifying fraudulent activities. 🚀✨
Parser for the Dutch ABN AMRO bank and credit card transactions
This is my final project for my internship at EISystems Technologies. I have used two ML algorithms and tried my hands-on. Also, the final report is included.
The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be fraud. This model is then used to identify whether a new transaction is fraudulent or not.
This repository contains my beginner-level Power BI dashboards, created during my learning journey in data visualization. Each project focuses on building interactive reports, exploring key Power BI features, and applying DAX formulas for insights and calculations. It reflects my progress in learning data modeling, relationships, and performance op
Built a machine learning pipeline to detect fraudulent credit card transactions using real-world data (1M+ rows). Explored fraud patterns, validated key hypotheses, and optimized models like KNN, Random Forest, and SVM with 98%+ recall. Strong focus on data insights & impact.
This project performs a comprehensive analysis of credit card transaction data using Python, Pandas, and NumPy.
his repository contains a Streamlit dashboard for visualizing fraud detection insights based on credit card transaction data.
ML model developed using European credit card transaction data to identify suspicious activities.
This repository features code for a fraud detection model achieving 100% accuracy in identifying fraudulent credit card transactions. Utilizing transaction data from Jan 2019 to Dec 2020, the model employs RandomForestClassifier, assessing features like credit card numbers, transaction amounts, and merchant information.
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