61 skills found · Page 1 of 3
YingfanWang / PaCMAPPaCMAP: Large-scale Dimension Reduction Technique Preserving Both Global and Local Structure
machenslab / DPCAAn implementation of demixed Principal Component Analysis (a supervised linear dimensionality reduction technique)
genekogan / OfxTSNEt-SNE dimensionality reduction technique for openFrameworks
TatevKaren / Mathematics Statistics For Data ScienceMathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.
MNoichl / UMAP Examples MammothThe mammoth-dataset, a commonly used example for dimensionality reduction techniques.
michelin / TorchSOMTorchSOM is a PyTorch-based library for training Self-Organizing Maps (SOMs), a model trained in an unsupervised manner, that can be used for clustering, dimensionality reduction and data visualization. It is designed to be scalable and user-friendly.
WangHewei16 / DMCNet For Video Engagement Understanding[Elsevier SOCL'22] Investigate in ML/DL-ensembled models and visualize features by dimension reduction techniques like PCA and t-SNE, measure performance via multiple metrics (e.g., Gini Index, AGF, and AUC).
mhaghighat / GdaGeneralized Discriminant Analysis (GDA) - non-linear feature dimensionality reduction technique
davidecoluzzi / Shape And Action Unit Extraction Of 3D Human Face Meshes By Multilinear Dimensionality ReductionThis work aims to create a model able to discern the parameters of shape and action units from 3D human face meshes. The adopted dataset was acquired by using Kinect and consist of 360 3D representation of human faces. More precisely, 20 different users performed 6 specific facial expressions (happy, sad, scared, angry, disgusted, surprised) by using 3 emphasis degree (low, medium, high). The collected dataset was labelled and then modelled in a three-dimensional tensor. Then, a multilinear dimensionality reduction technique (Higher-order singular value decomposition - HOSVD) was applied to separately extract the face deformation features related to the shape units and the action units. These specific features are finally exploited to independently rebuild the user human face by using much fewer data with respect to the starting dataset, specifically the 83% less, maintaining approximately 90% of variance.
sohailahmedkhan / Phishing Websites Classification Using Deep LearningA detailed comparison of performance scores achieved by Machine Learning and Deep Learning algorithms on 3 different Phishing datasets. 3 different feature selection and 2 different dimensionality reduction techniques are used for comparison.
rahulraghatate / Housing Sale Price Prediction"Buying a house is a stressful thing." We built a model to predict the prices of residential homes in Ames, Iowa, using advanced regression techniques. This model will provide buyers with a rough estimate of what the houses are actually worth. We first analyzed the data to find trends. Then dimensionality reduction was performed on the dataset using PCA algorithm and feature selection module in sklearn package for python 3.5. The final house prices are predicted using linear regression models like Ridge and Lasso. We also utilised advanced regression techniques like gradient boosting using XGBoost library in python 3.5.
fsrt16 / Introduction To Genomic Data Sciences Breast Cancer Detection# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways. ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal: Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.
shukali / Dimensionality Reduction ComparisonA comparison of various dimensionality reduction techniques from scikit-learn. Including PCA, t-SNE, LLE, Hessian LLE, Modified LLE, Isomap, Kernel PCA, Laplacian Eigenmaps, LTSA and (Non-)Metric MDS.
Tinny-Robot / DimSenseDimSense: Empower your machine learning projects with advanced feature selection and extraction techniques. Streamline dimensionality reduction and boost model performance. Your go-to toolkit for intelligent data dimension management.
atduskgreg / OfxPCAOpenFrameworks addon for PCA dimensionality reduction technique
weasteam / Coursera Machine LearningAbout this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
esmucler / OdpcThis package provides functions for computing One-Sided Dynamic Principal Components, a novel multivariate time series dimension reduction technique proposed in Peña, Smucler and Yohai (2019) (https://doi.org/10.1080/01621459.2018.1520117).
saitejabandaru-in / Data Mining AnalyticsData mining and dimensionality reduction techniques using PCA, Correspondence Analysis, and Multivariate Data Analysis on the Iris dataset.
ozlemkorpe / Machine Learning With MatlabA comprehensive MATLAB-based machine learning project covering data preprocessing, dimensionality reduction, supervised and unsupervised learning algorithms, model evaluation, and validation techniques with practical examples.
bcnmedtech / Unsupervised Multiple Kernel LearningThis is an implementation of unsupervised multiple kernel learning (U-MKL) for dimensionality reduction, which builds upon a supervised MKL technique by Lin et al (10.1109/TPAMI.2010.183).
