Financial distress prediction from Kaggle
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Updated
Jul 28, 2025 - Jupyter Notebook
Financial distress prediction from Kaggle
Built and evaluated several machine learning algorithms to predict credit risk.
Different Techniques to Handle Imbalanced Data Set
A company managing tourist real estate aims to offer a transfer service. This project uses logistic regression to analyze customer data and identify profiles most likely to purchase the service, enabling targeted early notifications to increase conversion and improve customer experience.
RNN-based security patch identification with oversampling samples. This is an extension code in the MILCOM'21 paper "PatchRNN: A Deep Learning-Based System for Security Patch Identification".
Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.
SOUL: Scala Oversampling and Undersampling Library. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021000868
Addressing highly imbalanced datasets in credit card fraud detection: Four balancing techniques are explored: Random Upsampling, Random Downsampling, SMOTE, and ADASYN, aiming to enhance predictive model performance by improving minority class representation
A curated set of implementations, experiments, and notes exploring algorithms and techniques in machine learning.
Cervical Cancer risk factor prediction.
A question-answering project focused on handling imbalanced datasets through advanced oversampling techniques.
Sprint 6, Task 1
osman: OverSampling by Deep Generative Models A pip package which oversamples class imbalance binary data by Deep Generative Models.
Oversampling-Guided Search for Evolutionary Multiobjective Optimization
Using Python to build and evaluate several machine learning models to predict credit risk.
This example shows how to configure the Analog-to-Digital Converter (ADC) to trigger a conversion on a specific event. The Timer/Counter type A (TCA) overflow is used to trigger the ADC sample accumulation and ADC result is transmitted through USART.
Classification model that will help the bank improve its services so that customers do not renounce their credit cards
Predicted credit risk using resampling models, SMOTEENN algorithm and Ensemble classifiers.
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