There are 1 repository under xgboost-classifier topic.
Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.
Pseudo-labeling for tabular data
Here are the codes for the "Deep Forest classifier for wetland mapping using the combination of Sentinel-1 and Sentinel-2 data" paper.
Algerian Forest Fire Prediction
Neural Ocean is a project that addresses the issue of growing underwater waste in oceans and seas. It offers three solutions: YoloV8 Algorithm-based underwater waste detection, a rule-based classifier for aquatic life habitat assessment, and a Machine Learning model for water classification as fit for drinking or irrigation or not fit.
Machine Learning in Python to assess fire risk in satellite imagery and environmental conditions.
Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models
This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD).
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.
The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidates more likely to have the visa certified.
A machine learning model to predict whether a customer will be interested to take up a credit card, based on the customer details and its relationship with the bank.
This research goal is to build binary classifier model which are able to separate fraud transactions from non-fraud transactions.
The aim of this study is to predict how likely individuals are to receive their H1N1 flu vaccine. We believe the prediction outputs (model and analysis) of this study will give public health professionals and policy makers, as an end user, a clear understanding of factors associated with low vaccination rates. This in turn, enables end users to systematically act on those features hindering people to get vaccinated.
Telco Churn Analysis and Modeling is a comprehensive project focused on understanding and predicting customer churn in the telecommunications industry. Utilizing advanced data analysis and machine learning techniques, this project aims to provide insights into customer behavior and help develop effective strategies for customer
Created Hate speech detection model using Count Vectorizer & XGBoost Classifier with an Accuracy upto 0.9471, which can be used to predict tweets which are hate or non-hate.
Predict the operational status of waterpoints to help the Tanzanian Government provide more clean water to its population using a Machine Learning Classifier
Supervised ML- Built a Multi-Class classification model to find the relation between features of a mobile phone(RAM, Internal Memory etc) and its selling price. Model will predict the price range indicating how high the price is.
Example notebooks to produce the models used in the SexEst web application.
Webapp that predict whether a claim is a fraudulent or not by asking user to put a csv file as mention in schema.json.
This is an end-to-end project that focuses on predicting credit card default using machine learning techniques. The project includes data validation,data preprocessing, model training, evaluation, and deployment.
AI-powered Instagram bot for precise gender targeting using XGBoost and OpenAI ADA, with 91% accuracy at just $0.001 per 1000 queries. Automates follows/unfollows from user lists or photo likes, and checks follow-backs with randomized human-like actions. Ideal for influencers and marketers aiming for targeted engagement.
This repository contains five mini projects covering several main topics in Data Mining, such as data preprocessing, clustering and classification.
This is the repository to generate synthetic tabular data when the tabular data has imbalance in some feature.
Classification Modeling: Probability of Default
Classification
Loan Prediction using machine learning
use the data set and run the ipynb file
CS5228 Kaggle Inclass Competition: Predicting if Income > 50K
This code is going to be used to scrape the UCPD Daily Incident page at a pre-determined frequency and store the incidents on a generic JSON data-store.
Tech Layoffs & Job Postings - Dashboard and Prediction Models
Android malware detection using machine learning.
Multi - Output Multi-Class Classification problem, Job-Type, and Job-Category Prediction
XGBoost is an open-source machine learning library that provides efficient and scalable implementations of gradient boosting algorithms. It is known for its speed, performance, and accuracy, making it one of the most popular and widely-used machine learning libraries in the data science community.
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language.
PSO Fuzzy XGBoost Classifier Boosted with Neural Gas Features on EEG Signals in Emotion Recognition