Supervised learning and unsupervised in R, with a focus on regression and classification methods.
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Updated
Oct 16, 2020 - R
Supervised learning and unsupervised in R, with a focus on regression and classification methods.
An R package that makes xgboost models fully interpretable
This project compares multiple bagging and boosting methods for anomaly detection for the Gecco challenge.
Decision Trees, Bagging and Boosting
Work I did for a group project that built machine learning models for heart disease datasets.
Problem Sets and Final Exam for Texas A&M ECMT 670: Machine Learning in Econometrics
Capstone project for my Master's degree. In it, I developed some machine learning models to predict the heat of formation for materials containing 1–3 components.
Predicting barbell movement efficiency from accelerometer measurements
Boosted regression trees for multivariate, longitudinal, and hierarchically clustered data.
Analysis of Sales Information of Orange Juice Using Tree-based Methods
Statistical machine learning
This project has the aim to analyze the Heart Disease dataset to build a classifiers to predict whether people have heart disease or not.
Parkinson’s disease classification using speech signal features; comparison of various multiclass classification algorithms
This is where I'll post my machine learning templates that I've created
Introduction to three machine learning models using the programming language R
Data Analytics and Machine Learning in R. Linear-regression, Logistic-regression, Hierarchical-clustering, Boosting, Bagging, Random-forests, K-means-clustering, K-nearest-neighbors (K-N-N), Tree-pruning, Subset-selection, LDA, QDA, Support Vector Machines (SVM)
This project aims to analyze the heart failure dataset to build a classifier that identifies the most important factors and allows predicting death from heart failure.
Applying predictive models on "Framingham Heart Study" dataset.
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