Beautifully visualize any 3D dataset in the browser.
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Updated
Aug 11, 2018 - JavaScript
Beautifully visualize any 3D dataset in the browser.
Used Tf-Idf approach to extract important keywords from query. Applied Kmeans clustering over Document-Term-Matrix and Doc2vec vectors using gensims. Tried to cluster keywords using Kmeans and t-Sne approach. Here i put the notebooks , you can make changes as per your needs.
ML reproducibility report for the paper "Visualizing Data using t-SNE" (van der Maaten, 2008)
Clustering single-cell RNA-seq data using Gaussian Mixture Models and K-means from embeddings created through PCA, t-SNE and UMAP.
Machine learning demonstration on various datasets
Animations of how perplexity affects t-distributed stochastic neighbour embedding for dimensionality reduction.
A Classification problem, given text review to determine whether the review is +ve or -ve using various ML algorithms. Here we are finding the model that peforms well using AUC as a metric.
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Visualisation exploring handwriting styles using convolutional autoencoders and clustering
This repo contains code and presentations used for ad-hoc analyses regarding liver cell toxicology and development. It also contains analyses on human and mice immune cells.
Vous êtes consultant pour Olist, une solution de vente sur les marketplaces en ligne. Olist souhaite que vous fournissiez à ses équipes d'e-commerce une segmentation des clients qu’elles pourront utiliser au quotidien pour leurs campagnes de communication.
Cluster patients in a Myopia study using unsupervised machine learning
MLTeam: Apply LIME for t-SNE
Case Summary Perform Principal component analysis and perform clustering using first 3 principal component scores (both Heirarchical and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining…
T-Distributed Stochastic Neighbor Embedding Library
Genome Network Ala Neural Network
Machine Learning Models on classifying malicious code files into 9 given classes of malware. Models based on XgBoost, RandomForest, Logistic Regression etc.
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