The Mulan Framework with Multi-Label Resampling Algorithms

A Machine learning model that detects Fraud Credit Card Transactions over a data set of anonymized credit card transactions labeled as fraudulent or genuine.

Built a model using XGBoost that predicts the chances of Attrition of an employee working at IBM with 84% Precision.

Predicting the status (acquired, open or closed) of a company using Crunchbase data

Fake review detection in Yelp dataset

In class Kaggle competition on predicting bankruptcy of a firm

Credit card fraud is a burden for organizations across the globe. Specifically, $24.26 billion were lost due to credit card fraud worldwide in 2018, according to shiftprocessing.com. In this project, our goal was to build an effective and efficient model to predict fraud. We analyzed a real-world dataset that contained a list of government relat…

Unbalanced data classification

This is a classification problem to detect or classify the fraud with label 0 or 1. Class with label 1 means fraud is detected otherwise 0. The biggest challenge is to handle the imbalanced data set.

Trying to solve a imbalanced little data in text sentiment analysis

Using machine learning methods to predict COVID-19 diagnoses in the Swiss population.

AmExpert 2019 - Machine Learning Hackathon

This repo is about Machine Learning and Classification

Algorithms used to confirm whether a celestial body is a planet or not.

Using the Kaggle dataset of credit card fraud detection, I have applied the techniques of both undersampling (with Autoencoders) and oversampling (SMOTE) to predict the credit card default.

This project aims to predict credit risk using various ensemble machine learning techniques. I have also tried to handle imbalance by using various sampling methods.

Official implementation of CVPR2020 paper "Deep Generative Model for Robust Imbalance Classification"

Déploiement d'une API Flask du modèle de classification déployée sur Heroku (OpenClassrooms | Data Scientist | Projet 7)

This project is about detecting fraudulent credit card transactions. The dataset tends to be highly imbalanced, with less than 0.2% of the observations labelled as fraudulent. To address this issue we have to take into account the bank's objective (maximizing precision or recall) and restrictions. The performance and efficiency of many classific…

Contained in this repository are the Jupyter notebooks that contain the scripts used in this project. Examples include: exploratory data analysis, creation of training, validation and test data sets, and CNN model development and data extraction.