Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.
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Updated
Jan 16, 2023 - Jupyter Notebook
Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.
Using machine learning algorithms to predict credit risk
Final project for Data Mining course (Uniba)
A python class for making machine learning algorithms cost sensitive.
Determine supervised machine learning model that can accurately predict credit risk using python's sklearn library. Python, Pandas, imbalanced-learn, skikit-learn
Credit card transactions fraud detection using classic algorithms
Applying various data engineering techniques into image classification task for KAIST DS801 term project
Imbalanced learning of real world datasets - motor vehicle collisions on public roads in Canada.
Testing 6 different ML models to predict credit risk on loan applications.
Predict credit risk with machine learning models by using different techniques to train and evaluate models with unbalanced classes.
Credit Risk Classification
E2IDS: An Enhanced Intelligent Intrusion Detection System Based On Decision Tree Algorithm
A collection of custom Tensorflow Keras objects mostly for hierarchical multilabel learning and recommender systems
imFTP: Deep Imbalance Learning via Fuzzy Transition and Prototypical Learning (imFTP, Information Sciences 2024)
Multivariate Normal Distribution based Oversampling
Using supervised machine learning to predict credit risk
Supervised Machine Learning project to predict credit risk
An analysis using Machine Learning algorithms to identify credit card risk using a dataset from LendingClub.
Multiple Machine Learning model are compared to determine which one is most accurate at predicting credit card risk.
This repository keeps my solution for Task 1 in the Introduction to Machine Learning course in Innopolis University. The key technics here are data preprocessing and training ANN on highly imbalanced dataset.
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