ML AI DL

implementation of some ml algorithms

Data Science Project

Worked on detecting illicit transactions in the Ethereum Transactions dataset by increasing our dataset size, and with little tolerance to missing fraudulent transactions.

Sirius.AI Research Programme (Spring 2024), DataBarrels Team. Blockchain AML.

In this project a model was build to identify and predict fraudulent credit card transactions.

Fraud Detection of a 6 million row dataset using AWS and Spark

This repository contains some of my machine learning notebooks I created on Kaggle

HarvardX Data Science Certificate: Choose Your Own Project

Fraud risk is everywhere, but for companies that advertise online, click fraud can happen at an overwhelming volume, resulting in misleading click data and wasted money.

A collection of machine learning mini-projects.

This repo has a notebook that I worked on for making a fraud detection model. The dataset was Highly imbalanced, so i used random undersampling to balance the data.

(CLASSIFICATION): This Dataset contains Information about 39221 transaction. After Trying/Training models like Naive Bayes/Decision Tree etc. Finally I was able to get 100% accuracy with Random Forest Classification as it was able to Segregate 0(non Fraudulent) & 1(fraudulent) accurately in leaf Nodes, based on 'Entropy' Criterion.

Detect payment transaction fraud using feature engineering and traditional and deep Machine Learning models.

Solution of the issue of classifying banking transactions (detecting fraudulent transactions)

Predicting the genuity of a transaction based on anonymized credit card transaction data.

Data Science for Business at KU Leuven

In this exercise, K-means and Random Forest algorithms are employed to address segmentation and fraud detection scenarios. [ES]