R and Data Files from my YouTube Channel

"This repository contains implementations of Linear Discriminant Analysis (LDA) algorithms for data mining tasks. Linear Discriminant Analysis is a dimensionality reduction technique used to find a linear combination of features that characterizes or separates classes of data."

Explore facial recognition through an advanced Python implementation featuring Linear Discriminant Analysis (LDA). This repository provides a comprehensive resource, including algorithmic steps, specific ROI code and thorough testing segments, offering professionals a robust framework for mastering and applying LDA in real-world scenarios.

Analyze a dataset on muscular dystrophy and make statistical inferences

Based on customer visiting information to the site the customer sales revenue is predicted using machine learning models stacking

Diabetes detection in patients using different machine learning techniques and comparing the algorithms based on confusion matrix and other metrics.

Participating in Hacktoberfest 2022. Code performing dimensionality reduction on datasets accepted.

Using classification algorithms to predict the geographical origin of an individual.

This project is based on 2 cases studies : Gems Price Prediction and Holiday Package prediction. In the first case study, concepts of linear regression are tested and it is expected from the learner to predict the price of gems based on multiple variables to help company maximize profits. In the second case, concepts of logistic regression and l…

Heart Disease Predictor QDA Framingham Dataset

Exploratoy Data Analysis,Logistic Regression,Penalized Logistic Regression (LASSO), LDA, Decision Trees, Bagging, Random Forest

Various Machine learning algorithms

Autoencoder model implementation in Keras, trained on MNIST dataset / latent space investigation.

In this project we conducted linear discriminant analysis to determine whether a given car is above or below the median mpg.

NUS Pattern Recognition module graded assignments

Data Understanding using- PCA, LDA, tSNE, and UMAP.

Implementation of Machine Learning Algorithms (KNN, Linear, Logistic, SVM, K-Means, Decision Tree, Naive Bayes) from Scratch using Python & Numpy only

LDA(Linear Discriminant Analysis) for Seed Dataset

Analysing different dimensionality reduction techniques and svm