This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.

Implements Naive Bayes and Gaussian Naive Bayes Machine learning Classification algorithms from scratch in Python.

Linear discriminant Analysis(LDA) for Wine Dataset of Machine Learning

Gaussian Naive Bayes and logistic regression for bank note authentication

Optimising parameters for multiple machine learning algorithms using grid search cv

Elixir library for machine learning

PCA(Principle Component Analysis) For Wine dataset in ML

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

Kmeans and Hierarchical clustering for Seed-dataset in Machine Learning

Kmeans and HCA clustering Visualization for WINE dataset in machine learning.

Dog Race Classifier by Image

This project on placement prediction integrates machine learning with database management using MySQL for user authentication. The project involves data preprocessing, feature engineering, and the implementation of supervised learning techniques to train the model.

Linear discriminant Analysis clustering Visualization for IRIS dataset

Machine Learning Examples for Beginners

Assignments of the course

LDA(Linear Discriminant Analysis) for Seed Dataset

To Detect Sepsis Disease using six Classifiers on clinical data

Prediction of the outcome of football matches of European teams using various ML models. The features include many in-game statistics and team ratings to predict the result.

Implementation of Gaussian Naive Bayes classification algorithm in Python using Pandas, NumPy and Scikit-Learn

This research constitutes an attempt to assess the dry spell patterns in the northern region of Ghana, near Burkina Faso. We aim to develop a model which by exploiting satellite products overcomes the poor temporal and spatial coverage of existing ground precipitation measurements. For this purpose 14 meteorological stations featuring different …