UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.

Mathematical & 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.

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Includes top ten must know machine learning methods with R.

A new simple and efficient software to PCA and Cluster For popolation VCF File

Python package that provides a full range of functionality to process and analyze vibrational spectra (Raman, SERS, FTIR, etc.).

Predicting solar energy using machine learning (LSTM, PCA, boosting). This is our CS 229 project from autumn 2017. Report and poster are included.

PYTHON E POSTGRESQL - EXTRACT TRANSFORM LOAD - ETL - DADOS PÚBLICOS DA RECEITA FEDERAL DO BRASIL - RFB, INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA - IBGE E AGÊNCIA NACIONAL DO PETRÓLEO, GÁS NATURAL E BIOCOMBUSTÍVEIS - ANP - PYTHON E POSTGRESQL

This repository contains the code and datasets for creating the machine learning models in the research paper titled "Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach"

PCA and DBSCAN based anomaly and outlier detection method for time series data.

2020 Spring Fudan University Data Mining Course HW by prof. Zhu Xuening. 复旦大学大数据学院2020年春季课程-数据挖掘（DATA620007）包含数据挖掘算法模型：Linear Regression Model、Logistic Regression Model、Linear Discriminant Analysis、K-Nearest Neighbour、Naive Bayes Classifier、Decision Tree Model、AdaBoost、Gradient Boosting Decision Tree(GBDT)、XGBoost、Random Forest Model、Support Vector Machine、Principal Component Analysis(PCA)

This program allow you to extract some features from pcap files.

Principal Component Anlaysis (PCA) in PyTorch.

Python+Rust implementation of the Probabilistic Principal Component Analysis model

This RNAseq data analysis tutorial is created for educational purpose

Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.

Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"

CUDA C implementation of Principal Component Analysis (PCA) through Singular Value Decomposition (SVD) using a highly parallelisable version of the Jacobi eigenvalue algorithm.

Dimensionality reduction

No description provided.

The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.

A comprehensive approach for stock trading implemented using Neural Network and Reinforcement Learning separately.

Here we have fully implemented a number of algorithms related to machine learning

R package PCAtest for evaluating the statistical significance of PCA analysis, selecting number of significant PC axes, and testing the contributions of the variables to those PCs.

Quantative Trading, building a trading strategy by generating alpha, optimizing a portfolio.

Augmenting the Interpretability of GraphCodeBERT for Code Similarity Tasks

No description provided.

This repo contains auto encoders and decoders using keras and tensor flow. It shows the exact encoding and decoding with the code part.

Code to support: "pcaReduce: hierarchical clustering of single cell transcriptional profiles"

A Python package implementing several statistical process control methods