The goal of this project is to identify students at risk of dropping out the school

成信助手相关源代码（成信好老师，成信贴吧大数据，成信曝光台，成信新闻）

Color Quantization using K-Means(Machine learning)

Detecting Fake User Profiles using k-Means and Local Outlier Factor

Repository for the "Fundamentos del Aprendizaje Automático" subject

Detects human activity by supervised and unsupervised methods same as SVM, Logistic Regression, Neural Networks, K-means, GMM and compares results

Analyse de données et classification supervisée et non supervisée

Recommendation of similar images to the given image using ResNet50, K-Means and cosine similarity.

Full Flask - BI application used for segmentation - machine learning

Applied unsupervised learning techniques on demographic and spending data for a sample of German households.

This course content includes that Classification(Supervised Method:),Clustering(Unsupervised Method),Association Analysis.

Facilitating deep phenotyping by automating the screening of case studies & the comparison of patient similarities (through clustering). This can be used to get an understanding of the underlying pathophysiology for a rare genetic disorder.

Customer loyalty program for E-commerce. Feel free to access the report in the link below.

A Flask web application that extracts dominant colors from uploaded images.

K-means Clustering implementation on random points in 2D plane

Applying data mining techniques to a set of documents to determine meaningful relationships between them.

Penerapan K-Means Clustering pada Javascript

The project performs data analysis and machine learning modeling using a Covid-19 dataset from New York City. The purpose is to analyze trends in Covid-19 cases, deaths, and hospitalizations, and predict hospitalization counts using regression models.

Customer Segments - Machine Learning Nanodegree from Udacity