Principal Component Analysis method of dimension reduction for feature vectors of higher space to a lower feature space

Customer Segmentation Analysis Based on Products Purchased Using the K-Means Method

PCA using Python

Simple application to visualize the result of the PCA

Predicting that the patient is suffering from Heart Disease or Myocardial Infarction (MI) based on various parameters.

Using Principal Component Analysis to compress images

Application of the statistical method principal component analysis for studying the thickness of a Silicon wafer.

We used Kernel PCA in this non linear dataset using both Python and R

An autoencoder for deep independence learning and distribution tying.

Machine Learning course projects

PCA for images

A deep convolutional network made of stacked feature extractors

Hi, this is my very first school project in python so please, don't be too harsh! This work was realized by Nicolas Darbois and me.

2021-4Q 機械学習 (Tokyo Tech)

A simple encryption tool

A.I. and Machine Learning notebooks: Using Supervised Learning, Unsupervised Learning, Re-enforcement Learning to solve Classification, Clustering and Regression problems

An image compression app built on Flask using Python. Containerized with Docker.

Implemented Principal Component Analysis to rank players who participated in the Indian Premiere League - 2012 based on the paper- "An Introductory Application of Principal Components to Cricket Data" by Ananda B.W. Manage & Stephen M. Scariano of Sam Houston State University.

A Python library of 'old school' machine learning methods such as linear regression, logistic regression, naive Bayes, k-nearest neighbors, decision trees, and support vector machines.

Feature importance refers to a measure of how important each feature/variable is in a dataset to the target variable or the model performance. It can be used to understand the relationships between variables and can also be used for feature selection to optimize the performance of machine learning models.