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

Application of Deep Learning and Feature Extraction in Software Defect Prediction

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

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.

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

Principle Component Analysis

Using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on same dataset and analyzing the best one

PCA applicator on a vector.

unsupervised learning to compress images and reconstruct images

Datamining concepts

Principal component analysis (PCA)

import datasets, perform exploratory data analysis, scaling & different models such as linear or logistic regression, decision trees, random forests, K means, support vectors etc.

Dimensionality reduction and Face classification using K-NN and PCA

This Git repository contains a machine learning algorithm for DiabetesAI, which predicts the likelihood of type 2 diabetes. The algorithm takes input from various features such as glucose levels, BMI, and age, and uses a predictive model to generate a probability score for diabetes diagnosis.