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https://www.analyticsvidhya.com/blog/2015/08/common-machine-learning-algorithms/ https://www.datascience.com/blog/introduction-to-forecasting-with-arima-in-r-learn-data-science-tutorials https://techietweak.wordpress.com/2016/05/18/r-data-frame-the-one-data-type-for-predictive-analytics/ Linear Regression Model: TestDataset <- read.csv("D:\\Dinesh\\AI\\TestDataset.csv") TrainingDataset <- read.csv("D:\\Dinesh\\AI\\TrainingDataset.csv") TrainingDataset TestDataset date <- as.Date(TrainingDataset$dteday) TrainingDataset <- cbind(TrainingDataset,date) sapply(TrainingDataset, class) date <- as.Date(TestDataset$dteday) TestDataset <- cbind(TestDataset,date) sapply(TestDataset, class) lmModel <- lm(cnt ~ date + season + yr + mnth + holiday + weekday + workingday + weathersit + temp + atemp + hum + windspeed + casual + registered, data = TrainingDataset) glmModel <- glm(cnt ~ date + season + yr + mnth + holiday + weekday + work...

Find installed R Packages in SQL/R Process sp_execute_external_script @language = N'R', @script = N'OutputDataSet <- data.frame(installed.packages())' Install Additional R Packages https://msdn.microsoft.com/en-in/library/mt591989.aspx  Revolution R Enterprise inside SQL Server EXECUTE   sp_execute_external_script @language = N'R' ,@script = N' salesData <- InputDataSet fit <- lm(SalesAmount ~ Year + Quarter, data = as.data.frame(salesData)) data2017 <- data.frame(Year=2017, Quarter=1:4) sales2017 <- predict(fit, newdata=data2017) style <- c(rep(1,14), rep(2,4)) salesForecast <- data.frame(data2017, sales2017) library(sqldf) OutputDataSet <- sqldf("select Quarter, Year, SalesAmount from salesData UNION select Quarter, Year, sales2017 from salesForecast") ' ,@parallel = 1 ,@input_data_1 = N'select d.CalendarQuarter Quarter, CalendarYear Year, sum(f.SalesAmount) SalesAm...