DataScience_R_Codes

This Repository Contains R-Codes executed on various Datasets in RStudio. I Hope This Repository is very helpful for those who are Willing to build their Career in Data Science, Big Data.

You will Need Rstudio to Execute all the Codes So Install it first and then Go through the Below Codes. To Download Rstudio, Click Here.

To Begin with the Basics of the Data Science, go through the Practice(Basics) Folder in the Repository.

Practice(Basics)

No.	Name	File
1.	Basics	practice.r
2.	Confidence Interval	Confidence_Interval.r
3.	Probability	Probability.r

Now we will do the Descriptive Statistics Analysis also known as Exploratory Data Analysis(EDA).

Descriptive Statistics - Exploratory Data Analysis(EDA).

No.	Dataset Name	File
1.	Carbon Dioxide(CO2)	Descriptive_Stats_CO2.r
2.	Air Quality	Descriptive_Stats_airquality.r

Now lets Go through Various Algorithms.

1. Hypothesis Testing

No.	Name	File
1.	Hypothesis Testing	Hypothesis Testing.r

2. Linear Regression

A. Simple Linear Regression

No	Name	Dataset	File
1.	Newspaper Data	NewspaperData.CSV	Newspaper_LinearRegression.r
2.	Waist Circumference-Adipose Tissue	WC-AT.csv	WC-AT_LinearRegression.r

B. Multiple Linear Regression

No	Name	Dataset	File
1.	Cars	Cars.csv	Cars_Multi_Linear_Regression.r
2.	Corolla	Toyota_Corolla.csv	Toyota_Multi_Linear_Regression.r

3. Logistic Regression

No	Name	Dataset	File
1.	Claimants	Claimants.csv	Logistic Regression.r

4. Association Rule

No	Name	Dataset	File
1.	Titanic	Titanic.csv	Titanic_Association_Rule.r

5. Principle Component Analysis (PCA) - Combines related Columns

No	Name	Dataset	File
1.	Cat	Cat.jpg	Example1_PCA.r
2.	University	Universities.csv	Universities_PCA.r

6. Clustering - Combining Related Rows

No	Name	Dataset	Heirarchical Clustering	K-Means CLustering
1.	Universities	Univesities.csv	Universities_Heirarchical_Clustering.r	K-Means_Clustering.r

7. Survival Analysis

No	Name	Dataset	File
1.	Unemployment	Survival_Unemployment.csv	Survival_Unemployment.r

Now Lets see Various Supervised Machine Learning Algorithms(Techniques)

1. Decision Tree

There are 2 Techniques in Decision Tree - `Bagging Technique` and `Boosting Technique`

No	Name	File	Bagging	Bagging and Boosting
1.	Example 1	DecisionTree.r	Decision_tree_Bagging.r	Decision_Tree_Bagging_Boosting.r

2. K-Nearest Neighbour(KNN)

No	Name	Dataset	File
1.	Cancer	KNN.csv	K-Nearest_Neighbour.r

3. Random Forest

No	Name	Dataset	File
1.	Iris	Available in R Datasets	random_forest.r

4. Artificial Neural Networks

No	Name	Dataset	File
1.	Concrete	concrete.csv	Concrete_Neural_Network.r

5. Support Vector Machine(SVM)

No	Name	Dataset	File
1.	Letter Data	LetterData.csv	LetterData_Support_Vector_Machine.r

6. Naive Bayes Classifier

No	Name	Dataset	File
1.	SMS Spam	sms_spam.csv	Naive_Bayes_Sms_Spam.r

7. Forecasting Analysis

No	Name	Dataset	Prediction File	File
1.	Amtrak	Amtrak.csv	Predict_new.xlsx	Amtrak_Forecasting.r
2.	Aviation	Aviation.csv	---	Aviation_Exponential_Smooting_Forecasting.r

8. NLP - Natural Language Processing (Text Mining)

There are Two Approaches - `Emotion Mining` and `Sentiment Analysis`.

We require Positive Words and Negative Words for the Analysis.

No	Name	Dataset	File
1.	Emotion Mining	Amazon Nokia Lumia Reviews.txt	Emotion_Mining_Amazon.r
2.	Sentiment Analysis	McD_Small.csv	Sentiment Analysis_McD.r

Web Scraping

If you want to extract the Reviews of a particular Product from Amazon then Run the Below Code in Rstudio.

This Code is Valid only for the Products on Amazon.

The Code Varies from site to site.

install.packages("rvest")
install.packages("XML")
install.packages("magrittr")

library(rvest)
library(XML)
library(magrittr)

# Amazon Reviews #############################
aurl <- "URL of Product Reviews page"
amazon_reviews <- NULL
for (i in 1:10){
  murl <- read_html(as.character(paste(aurl,i,sep="=")))
  rev <- murl %>%
    html_nodes(".review-text") %>%
    html_text()
  amazon_reviews <- c(amazon_reviews,rev)
}
length(amazon_reviews)
write.table(amazon_reviews,"apple.txt",row.names = F)

I have Performed this code for Extracting Reviews of Apple Macbook Air, Do check it Out.

After Going Through the basics, We will Now Perform Algorithms on Different Datasets.

Implementation of Algorithms on Datasets

1. Hypothesis Testing

No.	Name	Problem Statement	Dataset	File
1.	Buyer Ratio	.pptx	BuyerRatio.csv	BuyerRatio.r
2.	Customer Order Form	.pptx	Customer+OrderForm.csv	Customer+OrderForm.r
3.	Cutlet Diameter	.pptx	Cutlets.csv	Cutlet_Hyp_Test.r
4.	Fantaloons	.pptx	Fantaloons.csv	Fantaloons.r
5.	Lab	.pptx	LabTAT.csv	Lab_Hyp_Anova_test.r

2. Linear Regression

A. Simple Linear regression

No.	Name	Problem Statement	Dataset	File
1.	Calories Consumed	.txt	Calories_Consumed.csv	Calories_Simple_Linear.r
2.	Delivery Time Data	.txt	Delivery_Time.csv	Delivery_Simple_Linear_Regression.r
3.	Employee Data	.txt	Emp_Data.csv	Emp_Simple_Linear.r
4.	Salary Data	.txt	Salary_Data.csv	Salary_Simple_Linear.r

B. Multi Linear Regression

No.	Name	Problem Statement	Dataset	File
1.	50 Startup	.txt	50_Startups.csv	50_Startup_Multi_Linear.r
2.	Computer Data	.txt	Computer_Data.csv	Computer_Data_Multi_Linear.r
3.	Computer Data	.txt	ToyotaCorolla.csv	ToyotaCorolla_Multi_Linear.r

3. Logistic Regression

No.	Name	Problem Statement	Dataset	File
1.	Bank	.txt	Bank-Full.csv	Bank_logistic_Regression.r
2.	Credit Card	.txt	Creditcard.csv	Creditcard_Logistic_regression.r

4. Association Rule

No.	Name	Problem Statement	Dataset	File
1.	Books	.txt	Book.csv	Book.r
2.	Groceries	.txt	Groceries.csv	Groceries.r
3.	Movies	.txt	My_Movies.csv	My_Movies.r

5. Clustering

No.	Name	Problem Statement	Dataset	File
1.	Crime Data	.txt	Crime_Data.csv	Crime_Data_Clustering.r
2.	East West Airlines	.txt	EastWestAirlines.xlsx	EastWestAirlines_Cluster.r

6. Principle Component Analysis(PCA)

No.	Name	Problem Statement	Dataset	File
1.	Wine	.txt	Wine.csv	Wine_PCA.r

Supervised Machine Learning Algorithms

1. Decision Tree

No.	Name	Problem Statement	Dataset	File
1.	Company Data	.txt	Company_Data.csv	Company_Data.r
2.	Fraud Check	.txt	Fraud_Check.csv	Fraud_Check.r
3.	Iris	.pdf	Available in R Dataset	Iris_ctree.r

2. Random Forest

No.	Name	Problem Statement	Dataset	File
1.	Company Data	.txt	Company_Data.csv	Company_Data.r
2.	Fraud Check	.txt	Fraud_Check.csv	Fraud_Check.r
3.	Iris	.pdf	Available in R Dataset	Iris.r

3. K-Nearest Neighbour (KNN) Classifier

No.	Name	Problem Statement	Dataset	File
1.	Glass Data	.txt	Glass.csv	Glass.r
2.	Zoo	.txt	Zoo.csv	Zoo.r

4. Artificial Neural Network (NN)

No.	Name	Problem Statement	Dataset	File
1.	50 Startups	.txt	50_Startups.csv	50_Startups.r
2.	Concrete	.txt	Concrete.csv	Concrete.r
3.	ForestFires	.txt	Forestfires.csv	Forestfires.r

5. Support Vector Machine(SVM)

No.	Name	Problem Statement	Dataset	File
1.	Forest Fires	.txt	Forestfires.csv	Forestfires.r
2.	Salary Data	.txt	Salary_Data_Train.csv, Salary_Data_Test.csv	SalaryData.r

6. Naive Bayes Classifier

No.	Name	Problem Statement	Dataset	File
1.	Salary_Data	.txt	SalaryData_Train.csv, SalaryData_Test.csv	SalaryData.r
2.	Sms Data	.txt	Sms_Raw_NB.csv	Sms_Raw_NB.r

7. Forecasting Analysis

No.	Name	Problem Statement	Dataset	File
1.	Airlines Data	.txt	Airlines+Data.xlsx	Airlines+Data.r
2.	Coca Cola Sales	.txt	CocaCola_Sales_Rawdata.xlsx	CocaCola_Sales_Rawdata.r
3.	Plastic Sales	.txt	PlasticSales.csv	PlasticSales.r

8. NLP - Natural Language Processing(Text Mining)

You Require Positive-Words, Negative-Words and Stop-Words for this Analysis.

No.	Name	Problem Statement	Dataset	File
1.	Amazon HP Review	.txt	HP Reviews.txt	Amazon_HP_Reviews.r
2.	IMDB Paatal Lok WebSeries Review	.txt	Paatal_Lok_Reviews.txt	IMDB_Paatal_Lok.r

THANKYOU

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