Machine Learning analysis for an imbalanced dataset. Developed as final project for the course "Machine Learning and Intelligent Systems" at Eurecom, Sophia Antipolis

Unbalanced data classification

Develop a neural network model which classify cars, trucks and cats, while dealing with imbalanced dataset. In addition, generate an adversarial image that designed to deceive the trained model.

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.

This notebook shows how the f1 metric differs accuracy on imbalanced data. The heart disease dataset from kaggle is used (https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease).

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

Classification Ml problem. The goal of this project is to build a model that borrowers can use to help make the best financial decisions.(Customer will experience financial delincy in the next two years))

Local Feature Weight kNN combined Local kNN and Feature weighted kNN.

This was a comprehensive project completed as part of the Data Science PG Programme. This covers classification algorithms over a dataset collected on health/diagnostic variables to predict of a person has diabetes or not based on the data points. Apart from extensive EDA to understand the distribution and other aspects of the data. Pre-processi…

In class Kaggle competition on predicting bankruptcy of a firm

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…

Classification project - dealing with imbalanced dataset

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

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

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.

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

The following project aims at detecting the fraudulent credit card transactions while applying the various ML concepts right from Data Preparation, Feature Extraction, Model Validation, Hyper-param Tuning to Evaluation.

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

Fake review detection in Yelp dataset

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.