#Kmeans-clustering
Showing 16 of 16 repositories tagged #kmeans-clustering, ranked by stars
Code for Tensorflow Machine Learning Cookbook
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.
Streaming Anomaly Detection Solution by using Pub/Sub, Dataflow, BQML & Cloud DLP
A simple machine learning framework written in Swift 🤖
An approach to document exploration using Machine Learning. Let's cluster similar research articles together to make it easier for health professionals and researchers to find relevant research articles.
This is a computer vision project that utilizes object detection algorithms to analyze football matches videos by finding the position of players, ball and referees on the football pitch and finding out to which team each player belongs.
David Mackay's book review and problem solvings and own python codes, mathematica files
Repository containing introduction to scikit-learn to provide hands-on problem solving experience for all the methods and models learnt in MLT.
🤖 Python implementations of some of the fundamental Machine Learning models and algorithms from scratch with interactive Jupyter demos and math being explained.
Some Data Science examples using Groovy
A Repository Maintaining My Summer Internship Work At Datalogy As A Data Science Intern Working On Customer Segmentation Models Using Heirarchical Clustering, K-Means Clustering And Identifying Loyal Customers Based On Creation Of Recency, Frequence, Monetary (RFM) Matrix.
:snake: Data Science Boot-Camp : UC San DiegoX
👨 K-Means Clustering Customer Segmentation is an interactive Streamlit app that uses machine learning to group customers by income and spending habits. It helps businesses target marketing, personalize offers, and gain insights with easy retraining, visualizations, and modular code.
A fun study of some heuristics for the Travelling Salesman Problem.