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 :

Supervised Learning
Model Selection techniques - AIC, BIC, Mallow's Cp , Adjusted R-squared , Cross validation error.
Shrinkage Methods and Regularization techniques - Ridge Regression , LASSO, L1 norm, L2 norm.
Non-linear Regression and parametric models
Non-parametric model - K-nearest neighbor algorithm
Tree based Modelling - Decision Trees
Bayesian Modelling technique : Naive Bayes algorithm.
Ensemble learning - Random Forests, Gradient Boosting , Bagging.
Re-sampling methods and Cross Validation
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 which explaines K-means clustering algorithm and its implementation in R

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

anishsingh20/Statistical-Learning-using-R