螺旋熵减理论
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Updated
Jun 12, 2024 - Objective-C
螺旋熵减理论
螺旋熵减系统
This project focuses on analyzing credit risk to predict the likelihood of default on loans. It leverages various data analysis and machine learning techniques to assess borrower risk.
This repository gives you access to the CLIMATEREADY survey dataset containing thermal comfort votes during the 2021 and 2022 heatwave periods in Pamplona, Spain, as well as other relevant parameters self-reported by surveyees (e.g. occupant characteristics and behaviour, key building/dwelling characteristics, sleep problems, heat-related symptoms)
Analysis to optimize services & resident satisfaction in senior living facilities by segmenting population based on characteristics & behaviors.
This repository contains all the updates, code, and documentation related to ClassiPyGRB.
Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.
clustering of night time satellite images and depicting them by use of different colors
Conducted Data Mining utilizing Unsupervised Machine Learning K-means Clustering technique to analyze employee absenteeism data. The goal was to uncover hidden trends to better understand absenteeism causes and propose targeted solutions for mitigation.
Stylometry approach detecting writing patterns and changings using NLTK, XML-roBERTa, Gensim topic modelling and unsupervised-PCA learning
DINOv1 implementation in Pytorch
Customer Segmentation using Kmeans, than used Random Forest for prediction about new customers
ChunkeyBert is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings for unsupervised keyphrase extraction from long text documents.
This repository contains the code and analysis for a research project aimed at enhancing social media impact for food safety security organizations. The project focuses on understanding text tone preferences based on user demographics using machine learning techniques.
Project to demonstrate various clustering algorithms for customer segmentation.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Unsupervised learning. My Natural Language Processing project on Topic Extraction and Text Clustering.
This overview to machine learning offers a summary of its background, fundamental definitions, applications, and currently available challenges.
This script performs KMeans clustering and trains a neural network to predict heart disease, including data preprocessing, clustering visualization, and model evaluation.
Training machine learning models through supervised learning, utilizing PCA for visualization, and employing the k-means clustering algorithm for unsupervised learning on unlabelled data.
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