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

ML based Smart Crop Recommendation System with Disease Identification, utilizing CNNs. It aids farmers in selecting crops, managing diseases, and boosts productivity by integrating weather and geolocation APIs.

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.

Machine Learning Algorithms

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.

LDA(Linear Discriminant Analysis) for Seed Dataset

Linear discriminant Analysis clustering Visualization for IRIS dataset

Machine Learning Examples for Beginners

Assignments of the course

A prediction model that uses genetic data for disease classification. Data is extracted from a DNA microarray which measures the expression levels of large numbers of genes simultaneously.

To Detect Sepsis Disease using six Classifiers on clinical data