Anomaly detection using unsupervised, semi-supervised, and supervised machine learning methods
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Updated
Nov 29, 2022 - Jupyter Notebook
Anomaly detection using unsupervised, semi-supervised, and supervised machine learning methods
Data Science Assessment from HLA
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.
A Machine learning model that detects Fraud Credit Card Transactions over a data set of anonymized credit card transactions labeled as fraudulent or genuine.
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
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…
This repo is about Machine Learning and Classification
Using machine learning methods to predict COVID-19 diagnoses in the Swiss population.
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.
Introductory code snippets which deals with the basics of data science and machine learning which you can rely on anytime
In this repository, we implement Targeted Meta-Learning (or Targeted Data-driven Regularization) architecture for training machine learning models with biased data.
AmExpert 2019 - Machine Learning Hackathon
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.
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