The "breathing k-means" algorithm with datasets and example notebooks
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Updated
Nov 10, 2021 - Jupyter Notebook
The "breathing k-means" algorithm with datasets and example notebooks
Final year project experimenting with clustering and topological data analysis of scRNA-seq data using Python and R across two Jupyter notebooks
3 notebooks covering Classification, Clustering Analysis and Frequent Pattern Mining in the scope of Data Mining lectures in Marmara University.
In this Python notebook, we explore how K-Means can be used for customer segmentation to gain a competitive advantage and improve a business's bottom line.
In this repository, I have displayed some of the datasets I've worked upon.
In this notebook, I used unsupervised machine learning algorithms (K-Means and K-Plane) to cluster times series data.
Jupyter Notebooks exploring Machine Learning techniques -- regression, classification (K-nearest neighbour (KNN), Decision Trees, Logistic regression vs Linear regression, Support Vector Machine), clustering (k-means, Hierarchical Clustering, DBSCAN), sci-kit learn and SciPy -- and where it applies to the real world, including cancer detection, …
Machine learning Python notebooks based on the ML Course assignments
Some sample jupyter notebooks of machine learning, including my project
This repo contains notebooks performing clustering and classification on documents from the FUNSD dataset
K-Means clustering visualisation using the p5js library and semi-detailed explanation in a Jupyter Notebook
Datasets for this notebook consists of credit card usage behavior of customers with 18 behavioral features. Segmentation of customers can be used to define marketing strategies.
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