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All Projects → anishsingh20 → Statistical-Learning-using-R

anishsingh20 / Statistical-Learning-using-R

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This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

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r
7636 projects

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machine-learning-algorithmsstatistical-learningdatasciencedata-analysislogistic-regressionregularizationdecision-treespredictive-modelingpolynomial-regressionclustering-algorithmsvm-classifierk-nnboostinggeneralized-additive-modelssupervised-machine-learningbaggingdepth-interpretationdiscriminant-anlaysis

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Statistical-Learning-Techniques

This is a Statistical Learning repository which will consist of various Learning algorithms and their implementation in R and their in depth interpretation. Below are the links to the implementation and their in-depth explanation of the learning algorithms in R. All the documents below contain the under-lying mathematical concepts explained with respect to a simple case study in R.

Topics Covered :

  1. Supervised Learning

  2. Model Selection techniques - AIC, BIC, Mallow's Cp , Adjusted R-squared , Cross validation error.

  3. Shrinkage Methods and Regularization techniques - Ridge Regression , LASSO, L1 norm, L2 norm.

  4. Non-linear Regression and parametric models

  5. Non-parametric model - K-nearest neighbor algorithm

  6. Tree based Modelling - Decision Trees

  7. Bayesian Modelling technique : Naive Bayes algorithm.

  8. Ensemble learning - Random Forests, Gradient Boosting , Bagging.

  9. Re-sampling methods and Cross Validation

  10. Unsupervised learning

First Document published on Polynomial Regression Tecnhiques which adds Non linearities to the Model and makes the model learn Non linear Functions.

http://rpubs.com/anish20/polynomialRegression

Second Document on step by step Implementation of Cubic Splines and Smoothing Splines in R and how they transform the variables by adding Truncated Basis functions b(X) and how it is better from Polynomial Regression, to learn Non linear Functional Mappings from X(Predictors) to Y(Target)

http://rpubs.com/anish20/Splines

Third Document on implementing Generalized Additive Models in R and their overview

http://rpubs.com/anish20/GeneralizedAdditiveModelsinR

Fourth document on Implementing Decision Trees in R using the 'tree' package

http://rpubs.com/anish20/decisionTreesinR

Fifth Article which Explains the concept of Random Forests and how to Implement it in R

http://rpubs.com/anish20/RandomForests

Article on Support Vector Machine in R

http://rpubs.com/anish20/svmR

Article on Radial Kernel Support vector classifier

http://rpubs.com/anish20/radialSVM

Article on implementing PCA in R and the maths behind it.

http://rpubs.com/anish20/PCA

Article which explaines K-means clustering algorithm and its implementation in R

http://rpubs.com/anish20/k-means

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