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67 lines
3.0 KiB
R
67 lines
3.0 KiB
R
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##################### STEP1 - Connect to DB and read data ####################
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#Connection string to connect to SQL Server named instance
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connStr <- paste("Driver=SQL Server; Server=", "MYSQLSERVER",
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";Database=", "Tutorialdb", ";Trusted_Connection=true;", sep = "");
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#Get the data from SQL Server Table
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SQL_rentaldata <- RxSqlServerData(table = "dbo.rental_data",
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connectionString = connStr, returnDataFrame = TRUE);
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#Import the data into a data frame
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rentaldata <- rxImport(SQL_rentaldata);
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#Let's see the structure of the data and the top rows
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# Ski rental data, giving the number of ski rentals on a given date
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head(rentaldata);
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##################### STEP2 - Clean and prepare the data ####################
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#Changing the three factor columns to factor types
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#This helps when building the model because we are explicitly saying that these values are categorical
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rentaldata$Holiday <- factor(rentaldata$Holiday);
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rentaldata$Snow <- factor(rentaldata$Snow);
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rentaldata$WeekDay <- factor(rentaldata$WeekDay);
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#Visualize the dataset after the change
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str(rentaldata);
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##################### STEP3 - train model ####################
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#Now let's split the dataset into 2 different sets
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#One set for training the model and the other for validating it
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train_data = rentaldata[rentaldata$Year < 2015,];
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test_data = rentaldata[rentaldata$Year == 2015,];
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#Use this column to check the quality of the prediction against actual values
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actual_counts <- test_data$RentalCount;
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#Model 1: Use rxLinMod to create a linear regression model. We are training the data using the training data set
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model_linmod <- rxLinMod(RentalCount ~ Month + Day + WeekDay + Snow + Holiday, data = train_data);
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#Model 2: Use rxDTree to create a decision tree model. We are training the data using the training data set
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model_dtree <- rxDTree(RentalCount ~ Month + Day + WeekDay + Snow + Holiday, data = train_data);
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#################### STEP4 - Predict using the models ########################
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#Use the models we just created to predict using the test data set.
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#That enables us to compare actual values of RentalCount from the two models and compare to the actual values in the test data set
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predict_linmod <- rxPredict(model_linmod, test_data, writeModelVars = TRUE, extraVarsToWrite = c("Year"));
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predict_dtree <- rxPredict(model_dtree, test_data, writeModelVars = TRUE, extraVarsToWrite = c("Year"));
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#Look at the top rows of the two prediction data sets.
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head(predict_linmod);
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head(predict_dtree);
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#################### STEP5 - Compare models ########################
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#Now we will use the plotting functionality in R to viusalize the results from the predictions
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#We are plotting the difference between actual and predicted values for both models to compare accuracy
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par(mfrow = c(2, 1));
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plot(predict_linmod$RentalCount_Pred - predict_linmod$RentalCount, main = "Difference between actual and predicted. rxLinmod");
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plot(predict_dtree$RentalCount_Pred - predict_dtree$RentalCount, main = "Difference between actual and predicted. rxDTree");
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