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Fast Best-Subset Selection Library

Ml Course ⭐ 168

Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

📦 BRoadly Useful Convenient and Efficient R functions that BRing Users Concise and Elegant R data analyses.

Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.

Moderndive ⭐ 81

R package for tidyverse-friendly introductory linear regression

Azureml Regression Example ⭐ 59

This repo contains all the code and data necessary to explore non-linear regression using Azure ML.

Mathematics Statistics For Data Science ⭐ 54

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

The Elements Of Statistical Learning ⭐ 46

This repository contains R code for exercices and plots in the famous book.

Benchmarking and Visualization Toolkit for Penalized Cox Models

Machine Learning ⭐ 35

🌎 I created this repository for educational purposes. It will host a number of projects as part of the process .

Market Mix Modeling ⭐ 26

Market Mix Modeling for an eCommerce firm to estimate the impact of various marketing levers on sales

Models By Example ⭐ 19

By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

A Guide To Machine Learning In R ⭐ 17

A series of articles to get started into the field of Machine Learning with R language

Nls.multstart ⭐ 16

Non-linear regression in R allowing multiple start parameters

Easybench Rs ⭐ 15

A lightweight rust benchmarking library

an R package for beginners

Machine Learning Bookcamp 2022 ⭐ 15

Solutions for the Machine Learning Zoomcamp 2022 by DataTalks.Club.

FAST Change Point Detection in R

📊 Generalized linear regression models with network-regularization in R.

🧲 Multi-step adaptive estimation for reducing false positive selection in sparse regressions

📈 Useful notes and personal collections about statistics.

Introduction_to_gwas ⭐ 11

https://filippob.github.io/introduction_to_gwas/

Data Science courses in R from HarvardX

An R implementation of Models As Approximations

snpnet - Efficient Lasso Solver for Large-scale genetic variant data

Uc Bana7052 ⭐ 9

Course materials for BANA 7052 (Applied Linear Regression) at UC

Machine Learning ⭐ 8

Basic Machine Learning Tutorial

Statisticalmodeling ⭐ 7

Data Science ⭐ 7

EDA and Machine Learning Models in R and Python (Regression, Classification, Clustering, SVM, Decision Tree, Random Forest, Time-Series Analysis, Recommender System, XGBoost)

Functions and code snippets to help save the time!

Efficient boosting methods for linear regression.

Applied Machine Learning ⭐ 6

Applied Machine Learning

Summclust ⭐ 6

R module for cluster specific information (as in the Stata summclust module)

Set of functions to semi-automatically build and test Ordinary Least Squares (OLS) models in R in parallel.

Regression With Python And R ⭐ 5

Regression with Python & R.

Mxnetr Examples ⭐ 5

This project provides a series of MxNetR example for letting readers to get started quickly.

Machine Learning ⭐ 5

Basic ML concepts in Python and R

Predicting Diamond Prices ⭐ 5

Project 1.1 : A diamond distributor has recently decided to exit the market and has put up a set of 3,000 diamonds up for auction. Seeing this as a great opportunity to expand its inventory, a jewellery company has shown interest in making a bid. To decide how much to bid, the company’s analytics team used a large database of diamond prices to build a linear regression model to predict the price of a diamond based on its attributes. You, as the business analysts, are tasked to apply that model t

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