R and Data Files from my YouTube Channel

Various Machine learning algorithms

The projects are part of the graduate-level course CSE-574 : Introduction to Machine Learning [Spring 2019 @ UB_SUNY] . . . Course Instructor : Mingchen Gao (https://cse.buffalo.edu/~mgao8/)

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

Applying different machine learning algorithms on PCGA Prostate Cancer Gene Dataset for Feature Selection, Dimensional Reduction and Classification and Regression

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

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.

Continuation of my machine learning works based on Subjects....starting with Evaluating Classification Models Performance

Implementation of a sketch‐recognition pipeline inspired by Google’s Quick, Draw!. Includes data preprocessing and feature‐engineering scripts, three Bayesian classifiers alongside Logistic Regression, SVM, K-NN and XGBoost baselines, and an RNN model.

"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."

No description provided.

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

NUS Pattern Recognition module graded assignments

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

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

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

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

Machine learning algorithms from scratch in python.

Analyze a dataset on muscular dystrophy and make statistical inferences

No description provided.

Implementation of Fisher Linear Discriminant Analysis in Python

An advanced ML Project classifying 6 human activities using smartphone sensor data. It leverages Linear Discriminant Analysis (LDA) to compress 562 features into just 5 dimensions with maximal class separation, achieving 98% accuracy using Support Vector Machines (SVM). Includes interactive visualizations of activity clusters.

Heart Disease Predictor QDA Framingham Dataset

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 linear discriminant analysis are tested. One has to predict if the customer will purchase the holiday package to target the relevant customer base. Skills and Tools Linear Regression, Logistic Regression, Linear discriminant Analysis

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

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

Sketchify-A-Quick-Draw is a drawing classifier that uses machine learning to recognize hand-drawn sketches. It helps users identify and categorize their artwork quickly, making it a useful tool for artists and hobbyists alike.