# R-Language

The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac.

In this machine learning language, the major part is the supervised and unsupervised method which helps in data analysis, sorting big data and predicting the data and according to the predicted data and actual data, it gives the graphical representation.

The prediction is based on the decision tree which is coming under the supervised method and based on the predicted value the linear, multiple and logistic regression operation is performed. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

## What will you learn???

● What is R LANGUAGE
● How is R language related to BIG DATA
● What we can do with the R language
● Different functions of R Language
1. Decision Trees
2. Random Forest
3. Classification
4. Regression
5. Linear Regression
6. Multiple Regression
7. Graphical Representation
8. K-NN Algorithm

## Course Content

Session 1: Theory & Software Distribution
● Briefly Explanation on BIG DATA And R
● How Big Data and R Language Related To Each Other
● What We Can Do from R Language
● How R Language Being Important Now
● R and R Studio Software Distribution
● Installation of R Software and R Studio Installation
● Explanation on R data type, R operator, R variables, R functions, R loops, R vectors, R matrix, R lists

Session 2: Performing some tasks with R Studio
● Operations with vectors, matrices, loops, arithmetic, logic, etc.
● Algebra Review
1. Mean, Median, Mode
2. Matrices and Vectors
4. Matrix Vectors Multiplication
● Creating dataset in excel and how to import in R studio
● Explanation of Supervised and Unsupervised
● Decision Tree

1. Explanation of classification and decision tree on it.
2. Explanation on regression and decision tree on it.

Session 3: Introduction of Random Forest, Regression
● Random Forest method
● Explanation of Regression
● Practical on

1. Linear Regression
2. Multiple Regression
● Different types of plotting:
1. Pie Chart
2. Bar Chart
3. Line Graphs
4. Histograms
5. Box plotting

Session 4: Introduction of the K-NN algorithm
● How K-NN algorithms works
● Perform K-NN on different datasets
● Competition

## Note For Certification

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