Fast boosting with AdaBoost and Bandit
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Updated
Sep 19, 2019 - Jupyter Notebook
Fast boosting with AdaBoost and Bandit
Supervised learning and unsupervised in R, with a focus on regression and classification methods.
TP2 de Aprendizado de Máquina (2021/1) no DCC/UFMG.
This repository consists of projects on Big Data and Data Analytics as a part of our curriculum for PGDM- BDA (4th Term) at FORE School of Management, New Delhi.
Implementing random forest models in R with bagging and boosting.
This is Kaggle challenge for house prediction, it has alot of missing values which needs to be cleaned. i have used Regression ,Boosting, and Deep Neural network and tuning them.
Algorithms from scratch to know how the algorithms work.
Using Logistics, Classification, and KNN modelling to predict if a credit card account will default.
It is a subset of variables from a study carried out in 1988 in different regions of the world to predict the risk of suffering a heart-related disease.
We have a data of retail transactions over two year. Apart from data analysis and visualization, a regression model is developed to predict the price of retail items belonging to different categories. Foretelling the Retail price can be a daunting task due to the huge datasets with a variety of attributes ranging from Text, Numbers(floats, integ…
Coursework, Computational Machine Learning II, BSE, Term 2, Class of 2022
Predicting fraudulent transaction using credit card data
Modelos de classificação de risco de crédito usando algoritmos de Métodos Ensemble
AdaBoost algorithm implemented for the Machine Learning course at UFMG.
Тренировки Яндекс 2023 girafe-ai
Complete explanation of Bagging and Boosting with practical implementation
Chapter 8 Assignment Bagging vs Boosting vs. Random Forest
Implementation of popular ensembles methods in C++
Linear and Logistic Regression on Airbnb dataset
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