Topic: smote-oversampler Goto Github
Some thing interesting about smote-oversampler
Some thing interesting about smote-oversampler
smote-oversampler,In this analysis, I will demonstrate how PCA works in different tasks and how much time and resources we save in our daily analysis.
User: ali-unlu
smote-oversampler,The aim of this post is to identify and visualize factors that contribute to customer churn of a travel company.
User: ali-unlu
smote-oversampler,Supervised Machine Learning Project
User: annette-blackburn
smote-oversampler,Data preparation, statistical reasoning and machine learning are used to solve an unbalanced classification problem. Different techniques are employed to train and evaluate models with unbalanced classes.
User: ashley-green1
smote-oversampler,Machine learning for credit card default. Precision-recalls are calculated due to imbalanced data. Confusion matrices and test statistics are compared with each other based on Logit over and under-sampling methods, decision tree, SVM, ensemble learning using Random Forest, Ada Boost and Gradient Boosting. Easy Ensemble AdaBoost classifier appears to be the model of best fit for the given data.
User: ava33343
smote-oversampler,Built a model to determine the risk associated with extending credit to a borrower. Performed Univariate and Bivariate exploration using various methods such as pair-plot and heatmap to detect outliers and to monitor the behaviour and correlation of the features. Imputed the missing values using KNN Imputer and implemented SMOTE to address the imbalanced data. Trained the model using KNN, Decision Trees, Logistic Regression and Random Forest to achieve the best accuracy of 93%.
User: ayushtyagi1610
smote-oversampler,solution https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud. Xgboost is an efficient method of gradient boosting that makes a random initial prediction then calculates similarity scores and gain to build the trees and decrease the gap between the actual value and the predicted value.Gridsearch was used to get the best parameters tuning.
User: bahey1200022
Home Page: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
smote-oversampler,using machine learning to assess credit risk
User: baileerice
smote-oversampler,Supervised Machine Learning and Credit Risk
User: cbrito3
smote-oversampler,Data analysis, visualization and prediction for the prevention of heart disease using ML models
User: christingo
smote-oversampler,This repository contains the resources and codebase for a research project aimed at predicting breast cancer cases using data from the KNUST hospital.
User: davidnart90
smote-oversampler,This project predicts hotel booking cancellations using Machine Learning techniques, benefiting both travelers and hotels.
User: divyasudagoni
smote-oversampler,We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.
User: douguot
smote-oversampler,Extract data provided by lending club, and transform it to be useable by predictive models.
User: ed12rivera
smote-oversampler,This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.
User: edaaydinea
smote-oversampler,Kyphosis disease prediction using Fully Connected Neural Networks (FCNNs) model and XGBoost model with GridSearchCV
User: farhanateli
smote-oversampler,Handling Imbalanced Data Sets
User: hands-on-fraud-analytics
smote-oversampler,Chapter 12: Data Preparation for Fraud Analytics
User: hands-on-fraud-analytics
smote-oversampler,The goal is to create a model predicting the grade of an essay
User: karl0706
smote-oversampler,This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.
User: kaushikjas10
Home Page: https://doi.org/10.1016/j.soildyn.2022.107662
smote-oversampler,Testing 6 different machine learning models to determine which is best at predicting credit risk.
User: kenner82
smote-oversampler,Survival prediction using Four Different kind of algorithms and optimizing the dataset using PCA and SMOTE
User: leohrithik
smote-oversampler,This project uses different techniques to train and evaluate models with unbalanced classes using credit card dataset to predict low-risk and high-risk credit cards.
User: lidajav
smote-oversampler,A Deep Learning analysis to predict success of charity campaigns
User: ljd0
smote-oversampler,The objective of this analysis was to use machine learning models to accurately predict credit risk.
User: lsuantah
smote-oversampler,Battery analysis project
User: maengjulie
smote-oversampler,This project uses web scraping to download song text and uses Natural Language Processing (NLP) to predict an artist based a line of song text
User: mfriebel
smote-oversampler,Here is the repository for sharing jupyter notebooks discussed in 'AI-1402' class.
User: minashirinchi
smote-oversampler,Using the imbalanced-learn and Scikit-learn libraries to build and evaluate machine learning models.
User: mmsaki
smote-oversampler,
User: moindalvs
smote-oversampler,使用比赛方提供的脱敏数据,进行客户信贷流失预测。
User: mstao-68
smote-oversampler,Course Dropout Prediction, Datathon Spring'24
User: namansnghl
smote-oversampler,Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
User: nicoserrano
smote-oversampler,A two-stage predictive machine learning engine that forecasts the on-time performance of flights for 15 different airports in the USA based on data collected in 2016 and 2017.
User: nive927
smote-oversampler,
User: olatohun
smote-oversampler,Scrape and analyse customer review data to uncover findings for British Airways
User: preethiangelstephen01
smote-oversampler,Determine supervised machine learning model that can accurately predict credit risk using python's sklearn library. Python, Pandas, imbalanced-learn, skikit-learn
User: ramya-ramamur
smote-oversampler,improving correct classification of class with less representation
User: rud-ninja
smote-oversampler,Data preparation, Statistical reasoning, Machine Learning
Organization: rutgers-data-science-bootcamp
smote-oversampler,Future Ready Talent Project Submission.Using Azure ML Studio to predict the income of individuals, based on their age, race, education, residence city, etc. Used the adult census dataset
User: sar-go
Home Page: https://gallery.azure.ai/Experiment/Predict-Adult-Income-best-model-Predictive-Exp
smote-oversampler,This project is about credit card fraud detection using Random Forest Classifier.
User: sid966
smote-oversampler,Multi-class Classification - License Status Prediction
User: sidessh
smote-oversampler,HackerEarth Machine Learning challenge: Of Genomes And Genetics
User: toan01-uet
smote-oversampler,Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, we needed to employ different techniques to train and evaluate models with unbalanced classes. Jill asks us to use imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling
User: utsavchaudharygithub
smote-oversampler,Data analysis, visualization and prediction for the prevention of heart disease
User: xdiste
smote-oversampler,Predicts if a patient will show up at a scheduled appointment based on certain features.
User: zarich12
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