This repository contains an implementation of the Decision Tree algorithm from scratch using various impurity methods such as Gini index, entropy, misclassification error, etc.
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Updated
May 16, 2023 - Jupyter Notebook
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 Decision tree as a predictive(supervised) learning model. The implementation uses ID3 algorithm and also the Information Gain Heuristic and Variance Impurity Heuristic.
Some algorithmic implementations of single/batch perceptrons also with k-NN
Small library for classification and clustering in Java
Implementation of decision tree from scratch
Common Machine Learning Algorithms
The assignments introducing the concepts of Machine Learning written with sklearn library of python.
Decision Tree, Random Forest and AdaBoost implementation from scratch.
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.
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.
Some common simple data mining algorithm
Predict survival outcomes from the 1912 Titanic disaster(Kaggle)
This is a tutorial on tree model theory, including decision trees, boosting, bagging and random forests. An example of using decision trees and random forests in shown is python.
Implementation of Decision Tree CART algorithm
Predicting number of bikes rented during any hour or day, based on the day of the week, weather condition and etc.
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