Apply modern, deep learning techniques for anomaly detection to identify network intrusions.

Application of Deep Learning and Feature Extraction in Software Defect Prediction

Repo to analyze population genetic data with many different methods

This Repository Contains R-Codes executed on various Datasets in RStudio. I Hope This Repository is very helpful for those who are Willing to build their Career in Data Science, Big Data.

ROS package for creating a terrain cost map using RGBD Camera for locomotion of legged Robots.

A cross compiled Scala.js port of JAMA for JVM, JavaScript, and Scala Native projects.

Machine learning problem sets from Stanford University's Machine Learning course on Coursera

RGB data processing pipeline including auto-white-balance based on principle component analysis (PCA).

PCA(Principle Component Analysis) For Seed Dataset in Machine Learning

Toolkit for Data Science & Statistics

Anomaly Detection using Unsupervised Machine Learning

Machine learning with MATLAB/Octave, coding machine learning algorithms from scratch

Heart disease detection using different classifiers and neural network with feature engineering.

Predict suicide rates using nine ML regression models, GA‑based feature selection, and PCA on public and custom datasets.

An binary classification model based on principle component analysis and fuzzy inference system. It takes brain MRI images and predicts whether the MRI image contains a tumor or not.

This is a 16S amplicon analysis for visualizing microbiome data using QIIME, QIIME2R and Phyloseq. DNA was isolated fom both sediment cores and seabird fecal samples for this analysis.

Contains code and detailed write ups created while taking a class on Data Driven Modeling & Scientific Computation with Professor Nathan Kutz.

Prediction and classification of breast cancer using standard scaling, PCA, and SVM.

Principle Component Analysis Clustering Visualization for Iris dataset

Principle Component Analysis

Python version of exercises in the Coursera course of Machine Learning provided by Stanford University (https://www.coursera.org/learn/machine-learning)

Course work of Multivariate data analysis CH5440

This repository consists of performing Principal Component Analysis (Finding out the principle components of our dataset), using the concept of Eigen Vectors and Eigen Values of the correlation matrix. Also, it also includes the technique of reducing the dimension of the data using Singular Value Decomposition technique and reduce it to the lowest possible dimension.

Principal Component Analysis Let's discuss PCA! Since this isn't exactly a full machine learning algorithm, but instead an unsupervised learning algorithm, we will just have a lecture on this topic, but no full machine learning project (although we will walk through the cancer set with PCA).

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[Completed] Complete framework on multi-class classification covering EDA using x-charts and Principle Component Analysis; machine learning algorithms using LGBM, RF, Logistic Regression and Support Vector Algorithms; as well as Bayesian Optimizer with l1 and l2 regularization for Hyperparameter Tuning.

PCA implementation of on Iris Dataset

:cherries: Giving some examples with the core codes, and details the design and development of the Material UI.

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Permutation Test for Principal Components Analysis