Here we have fully implemented a number of algorithms related to machine learning

The programs of certificate course on "Artificial Intelligence and Machine Learning" conducted at CDAC for DRDO by Tushar B. Kute.

Implementing 2 basic classification algorithms: K-Nearest Neighbor (KNN) and Perceptron Learning Algorithm (PLA) to predict the likelihood of customers subscribing to term deposits. The implementation process from manual calculation based on mathematical formulas to utilizing libraries.

Our project focuses on using machine learning classification algorithms to develop a breast cancer detection system. We gathered a diverse dataset and applied preprocessing techniques, feature selection, and various classification algorithms to train and evaluate our models.

This project is dedicated to implementing various machine learning algorithms from scratch to gain a deeper understanding of how they work.

Analisis KNN, Random Forest dan Boosting Algorithm.

Machine learning analysis & visualisation of cellular spatial point patterns

Book Recommender System - 5 approaches

This project and exercises were made for the Models in credit and operational risk course at the AGH UST in 2022. All provided methods are a result of my work after hours, when I was solving given tasks (topics).

This is an implementation of KNN Classifier on glass.csv, with EDA and GridSearchCV

A basic implementation of K-nearest-neighbour in Rust

The aim of this project to predict whether the product from an e-commerce company will reach on time or not. This project also analyzes various factors that affect the delivery of the product as well as studies the customer behavior.

A MapReduce-Based k-Nearest Neighbor Approach for Big Data Classification on Apache Spark

This repository describes the eassessment for CCT College Dublin's HDip Strategic Thinking subject. The project uses Kaggle heart stroke data to analyze risk factors before contracting a health plan. Applies project management methodologies and implements KNN, SVM, and Random Forest classification.

SDN networks (Software Defined Networking ) are exposed to new security threats and attacks, especially Distributed Denial of Service (DDoS) attacks. For this aim, we have proposed a model able to detect and mitigate attacks automatically in SDN networks using Machine Learning (ML)

A KNN classifier from sklearn was trained and used to predict the category of a customer for a telecomms company, based on demographic info. Best hyperparameter K was = 6, with an accuracy score of 0.41 on the test data.

A dataset from the UCI Machine Learning Repository used for classifying gamma and hadron events recorded by the MAGIC Gamma Telescope, suitable for binary classification problems in machine learning.

Face Recognition Attendance System Using KNN And OPENCV

Face Recognition Using several dimensionality reduction techniques along with KNN as a classification algorithm

Gender Analysis in Video Games Using Machine Learning