This repository contains an implementation of the Decision Tree algorithm from scratch using various impurity methods such as Gini index, entropy, misclassification error, etc.

Implementation of the decision forest algorithm SysFor, a forest of high accuracy decision trees.

Implementation of Decision tree as a predictive(supervised) learning model. The implementation uses ID3 algorithm and also the Information Gain Heuristic and Variance Impurity Heuristic.

Simple Parts of Speech (POS) tagger along with text to speech feature

Some algorithmic implementations of single/batch perceptrons also with k-NN

Evolution is program that shows possibilities of genetic programming. Evolution was created for "Junior Tech University", which is project of czech college ČVUT.

Small library for classification and clustering in Java

decision tree algorithm

Performance analysis of Decisions Trees, Boosting & Bagging, KNN, Neural Network and Linear Regression algorithms. Over two Data Sets (meant-to-be) very different in nature and volume.

machine learning algorithm

Decision Tree, Random Forest and AdaBoost implementation from scratch.

Homeworks for Machine Learning Course at MIPT

Kernel for MNIST competition in kaggle

Implementation of decision tree algorithm to classify email send by which sender. And calculating accuracy of the classifier as well as plotting the training data set.

code_for_lecture

These are all my assignments from Statistical Analysis with R (a course I took at Bryant University). The assignments include data visualization, data cleaning and manipulation, modeling, creating functions and loops, and making packages. The major packages used are "ggplot2", "caret", "rpart", and "rattle", although there are others used as well.