GitHub topics: imblearn

Repositories

sorna-fast/fraud-detection

Predicting transaction fraud using classification problems such as Guardian Boosting as well as user interfaces using Streamlite

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RicardoRobledo/Malicious_Server_Hack_Detection

Predictive model to detect malicious hacking patterns in banking servers. Utilizes advanced Machine Learning techniques such as SMOTE, Gradient Boosting, and probability calibration to predict attacks befor

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PauloMPPatricio/projeto_challenge_telecomx-br_parte-2

Projeto Challenge TelecomX-BR_Parte-2 - Formação Data Science do programa ONE - Oracle Next Education em parceria com a Alura.

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yamneg96/fraud-detection-code

A complete pipeline to detect fraudulent transactions using e-commerce and credit card data.

Language: Python - Size: 2.93 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

mattiacurri/CorporateCreditRatingPrediction

Corporate Credit Rating Prediction System - Knowledge Engineering Project - A.A. 2023-2024

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Emeraldugbeyide93/Undersampling-and-Oversampling-techniques-for-imbalanced-datasets

Beginner friendly project focusing on dataset imbalances using the oversampling and under sampling techniques

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mahnoorsheikh16/Credit-Card-Default-Prediction

This project focuses on predicting whether a customer will default on their credit card payment in the upcoming month. Utilizing historical transaction data and customer demographics, the project employs various machine learning algorithms to distinguish between risky and non-risky customers for better credit risk management.

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Vergosss/Decision_Theory_2023_2024_Project

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ferkuellar/Trading_bot

Este proyecto es un sistema completo de señales de trading en criptomonedas utilizando Binance Spot, Machine Learning y alertas por Telegram. Diseñado para ejecutarse en tiempo real y enviar señales basadas en análisis técnico, VSA y modelos predictivos.

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ChaitanyaC22/Fraud_Analytics_Credit_Card_Fraud_Detection

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

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alessandrosocc/Machine-Learning-Project-2022

Final project for the Machine Learning course at the University of Cagliari in 2022. Analysis of a dataset, use of Machine Learning techniques with Oversampling and Undersampling techniques. Final report with the results obtained.

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Fedesgh/Building_Credit_Risk_Classifier_Using_Bagging_Kneighbors

Problem statment about modeling target vector and attempt to improve metrics

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Fedesgh/Asteorid_RandomForest_Classifier

Classifier model trained with unbalanced dataset ready for deployment

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paocarvajal1912/Supervised_Credit_Risk_Classification

Uses Logistic Regression and various machine learning techniques to train and evaluate models with imbalanced classes applied to identify the creditworthiness of borrowers.

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NaveenKumarMaurya/datascience-project-portfolio

Portfolio of my data science projects which i have completed for learning, skill development .

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varmatilak22/Credit_Score_Prediction

This project predicts credit scores ('Good', 'Standard', 'Poor') using a streamlined ML pipeline. It includes data extraction, cleaning, and preprocessing. Key techniques are Mutual Information for feature selection, PCA for dimensionality reduction, and XGBoost for accurate and efficient model training, ensuring reliable and robust predictions.

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varmatilak22/Spam_SMS_Classification

This project aims to develop a machine learning model to classify SMS messages as spam or not spam. The project encompasses the entire pipeline from data collection and preprocessing to model training, evaluation, and deployment using Streamlit for an interactive user interface.

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manjit-baishya-datascience/Spam-Email-Detection

This project demonstrates how to build a spam detection system using Natural Language Processing (NLP) and machine learning techniques.

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pouriaSameti/EFQM-Internship-projects

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Amber-Abuah/Amazon-Rating-Predictor

MultinomialNB classifier for predicting Amazon review ratings.

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AntarMukhopadhyaya/Fraud-Warden

Fraudulent Credit Transaction detection system using SMOTE, Random Forest Classifier and Streamlit

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GenTaylor/Traffic-Accident-Analysis

Traffic Accident Analysis using python machine learning

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AntwanW/Credit_Risk_Analysis

In this analysis, I will be using several supervised machine learning models to predict credit risk on loan data.

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BishwanathKumarPanda/Parkinson-Disease

Parkinson’s disease is a progressive disorder that affects the nervous system and the parts of the body controlled by the nerves. Symptoms are also not that sound to be noticeable. Signs of stiffening, tremors, and slowing of movements may be signs of Parkinson’s disease.

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viniciusds2020/ml_balaceamento_allknn

Este repositório contém um código de Machine Learning que utiliza o algoritmo AllKNN do pacote imblearn para realizar o balanceamento de dados.

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shinho123/23.11.10-1st-Korean-Society-of-Industrial-Engineers

2023년 11월 대한산업공학회(UNIST) : 다중 역할 경험을 고려한 게임 유저 이탈 예측: 롤 게임을 중심으로, 1저자

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mathewsrc/AWS-Machine-Learning-Engineer-Capstone

This project aims to train three classification models (LogisticRegression, DecisionTree and RandomForest) using AWS SageMaker to classify customer churn from a database obtained from kaggle.com.

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christopher-w-murphy/Notes-on-Decision-Trees-and-Random-Forests

These are my notes for the interview prep workshop I led on Random Forests

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pratik-choudhari/Intent-classification-using-python

Imbalanced Intent classification model with deployment

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LaurentVeyssier/Starbucks_case_study_Udacity_Data_Science

Case study from UDACITY Data Scientist Nanodegree

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apethani21/aus-rain-prediction

Binary classification project on trying to predict whether or not it will rain the next day using weather features, for various locations across Australia.

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alaa-aleryani/Credit_Risk_Classification

We used various techniques to train and evaluate a model based on loan risk. We used a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.

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Shipra-09/Project-Vehicle-Insurance

This Github repository contains cross selling of health insurance customers on vehicle insurance product. We have to predict whether a customer would be interested in Vehicle Insurance or not by building a ML model. Exploring Insights/Inferences by performing EDA on the given project data. Finding the high accuracy

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saurabhwankhede022/Data-Science-With-Project-s

Data Science With Project's

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saikrishnabudi/Random-Forests

Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

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Dhrumil-Zion/Sentiments-Prediction-Using-NLP

Predicting customer sentiments from feedbacks for amazon. While exploring NLP and its fundamentals, I have executed many data preprocessing techniques. In this repository, I have implemented a bag of words using CountVectorizer class from sklearn. I have trained this vector using the LogisticRegression algorithm which gives approx 93% accuracy. I have found out the top 20 positive and negative feedback words from thousands how feedbacks. Also after processing this much I have automated the whole process with one function so that it can be used as generic for many machine learning algorithms. I have also tested another algorithm called DummyClassifier which gives an accuracy of around 84%. After that, I have executed the famous algorithm which is TF-IDF for NLP. I have combined TF-IDF with LogisticRegression which gives almost 93% accuracy but deep insights. Also, while working with data has solved the problem of imbalanced data through RandomOverSampler class from imblearn library.

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nickjlupu/Credit-Risk

Supervised scikit-learn machine learning models using several sampling techniques.

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xinguanca/MLproject_creditcardfraud

My first machine learning project.

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SzymonWilczewski/bank-client-classification-ai

Project for "Computational intelligence" course

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egorumaev/2023-cirrhosis-outcomes

Прогнозирование исхода лечения пациентов с циррозом печени

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egorumaev/2023-ods-turnstiles

Идентификация посетителя в зависимости от характерного времени его прохода на территорию организации

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egorumaev/2023-telekom-customers-churn

Прогнозирование оттока клиентов оператора связи

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kirankirank/imbalenced-data

imbalanced data

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pratiksha2712/Customer-Churn-Analysis-and-Prediction

Data analysis and ML Modelling

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sharmaroshan/Fraud-Detection-in-Insurace-Claims

This is a very Important part of Data Science Case Study because Detecting Frauds and Analyzing their Behaviours and finding reasons behind them is one of the prime responsibilities of a Data Scientist. This is the Branch which comes under Anamoly Detection.

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alecngo/cervical-cancer-project

Deploy SVM, Random Forest, and Streamlit Package to make a web app to early detect Cervical Cancer

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pranshu1921/Personalised-Cancer-Diagnosis

Predict the effect of genetic mutations in cancer tumors and classify them based on text clinical literature.

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ats-tandjoeng7/Credit_Risk_Analysis

Application of various supervised Machine Learning techniques to solve a real-world case study.

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TrilokiDA/Credit-Card-Fraud-Detection

Synthetic Financial Datasets For Fraud Detection

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earlyann/Stock_predictions

Using and comparing Support Vector machine, Random Branch Forest and Easy Ensemble algorithms to predict if a stock will have a positive or negative annual return.

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cp-PYFOREST/PYFOREST-ML

Utilizing machine learning to examine deforestation rates in the undeveloped region of Paraguay's Chaco

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suyinwb/Credit_Risk_Analysis

Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company, you’ll oversample the data using the RandomOverSampler and SMOTE algorithms, and undersample the data using the ClusterCentroids algorithm. Then, you’ll use a combinatorial approach of over- and undersampling using the SMOTEENN algorithm.

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nnilayy/Classification-Notebook

Data Science Classification General Notebook

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jianninapinto/Bandersnatch Fork of BloomTech-Labs/BandersnatchStarter

This project implements a machine learning model using Random Forest, XGBoost, and Support Vector Machines algorithms with oversampling and undersampling techniques to handle imbalanced classes for classification tasks in the context of predicting the rarity of monsters.

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Priyanshusinhaa/CreditCardFraudDetection

Notebook represents the process of fraud detection using past data.

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MirrasHue/Intro-to-AI

An assignment from my Introduction to Artificial Intelligence course, in which we had to treat the datasets, train some models for classification and adjust their parameters

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SayamAlt/Fraudulent-Transactions-Prediction

Successfully trained a machine learning model which can predict whether a given transaction is fraud or not.

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rewatevijaykumar/sensor-fault-detection

The Air Pressure System (APS) is crucial for heavy duty vehicles, utilizing compressed air to break pads and slow down the vehicle. The aim of this binary classification project is to minimize unnecessary repair costs by identifying component failure in APS, using Python, FastAPI, Machine Learning Algorithm, Docker and MongoDB.

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Sajid030/fraud_detection

In this repository I have trained my machine learning model to detect whether there was fraud or not in your online transaction.

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vsdcarneiro/Projeto-Integrado-PUC

Trabalho de Conclusão de Curso apresentado ao Curso de Especialização em Inteligência Artificial e Aprendizado de Máquina, como requisito parcial à obtenção do título de Especialista.

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shanuhalli/Assignment-Random-Forest

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jpcadena/cancer-classification

Breast cancer classification project.

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binaryvexjuiit/Detecting-Facial-Diseases-Through-Neural-Networks

Facial skin disease detection using Neural Networks

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shayleaschreurs/Machine-Learning-Trading-Bot

Our goal was to create a ML bot that analyzes real time trading data to determine the most opportune times buy and sell stock

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guilhermedom/pyspark-horsepower-multilinear-regression

PySpark for multiple linear regression on car horsepower using SMOTE for data augmentation.

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digvijaytaunk/aps-fault-detection-with-deployment

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prohogiy90/ds-model-of-the-behavior-of-the-SberAvtopodpiska-customers

This repository contain my final projekt on the Data science Skillbox school on the topic: "Development of a machine learning algorithm to predict the behavior of customers of the "SberAvtopodpiska"

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ngfelixxx/Bank-Account-Machine-Learning-Model

Performing Feature Engineering, Hyperparameter Tuning , and Classification Algorithms.

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schatzederwelt/toxic_comments_detection

Автоматическое выявление токсичных комментариев

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shayleaschreurs/Supervised_Learning_Regression_Model

Module 12 - Using the imblearn , I'll use a logistic regression model to compare 2 versions of a dataset. First, I’ll use the original data. Next, I’ll resample the data by using RandomOverSampler. In both cases, I’ll get the count of the target classes, train a logistic regression classifier, calculate the balanced accuracy score, generate a con

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