https://github.com/tirthajyoti/r-stats-machine-learning

Misc Statistics and Machine Learning codes in R
https://github.com/tirthajyoti/r-stats-machine-learning

classification clustering decision-trees hypothesis-testing k-means machine-learning nearest-neighbors neural-network principal-component-analysis r random-forest regression statistics support-vector-machines

Last synced: 2 months ago
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Misc Statistics and Machine Learning codes in R

• Host: GitHub
• URL: https://github.com/tirthajyoti/r-stats-machine-learning
• Owner: tirthajyoti
• License: mit
• Created: 2018-09-02T05:29:41.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-03-20T23:41:41.000Z (almost 4 years ago)
• Last Synced: 2024-11-02T03:32:44.288Z (3 months ago)
• Topics: classification, clustering, decision-trees, hypothesis-testing, k-means, machine-learning, nearest-neighbors, neural-network, principal-component-analysis, r, random-forest, regression, statistics, support-vector-machines
• Language: R
• Homepage:
• Size: 2.02 MB
• Stars: 41
• Watchers: 4
• Forks: 30
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## Please feel free to [add me here on LinkedIn](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7/) if you are interested in data science and like to connect.

# Statistics and Machine Learning R scripts 

Misc Machine Learning and statistical analysis code examples in R

## Packages used/demonstrated

* [caret](caret.r-forge.r-project.org)

* [rattle](https://cran.r-project.org/web/packages/rattle/vignettes/rattle.pdf)

* [randomForest](https://cran.r-project.org/web/packages/randomForest/randomForest.pdf)

* [rpart](https://cran.r-project.org/web/packages/rpart/rpart.pdf)

* [dplyr](https://cran.r-project.org/web/packages/dplyr/dplyr.pdf)

* [ggplot2](https://cran.r-project.org/package=ggplot2/ggplot2.pdf)

* [corrplot](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html)

* [factoextra](http://www.sthda.com/english/wiki/factoextra-r-package-easy-multivariate-data-analyses-and-elegant-visualization)

* [glmnet](https://cran.r-project.org/web/packages/glmnet/glmnet.pdf)

* [MASS](https://cran.r-project.org/web/packages/MASS/MASS.pdf)

* [mgcv](https://cran.r-project.org/web/packages/mgcv/index.html)

* ... and some more...

## Supervised learning (Regression and Classification)

* Linear regression

* Poisson regression



* Stepwise selection method 

* LASSO, Ridge, and Elastic Net regularization methods



* Residual analysis

* Spline regression



* Logistic regression



* Support vector machine



* k-Nearest Neighbor

* Decision Tree

* Random Forest



* Feedforward neural network

## Unsupervised learning

* k-means Clustering



* Principal Component Analysis (PCA)



## Statistics/Data wrangling

* Missing data imputation

* Demo of Central Limit Theorem'

* Outlier detection using Grubb's test

* **Cu**mulative **Sum** (CUSUM) for change detection

* Demo of Hypothesis shopping (*why you should be suspicious of p-Values*)