AI News, R: Getting Started with Data Science

R: Getting Started with Data Science

This short tutorial will not only guide you through some basic data analysis methods but it will also show you how to implement some of the more sophisticated techniques available today.

We will look into traffic accident data from the National Highway Traffic Safety Administration and try to predict fatal accidents using state-of-the-art statistical learning techniques.

If you open this file in RStudio, you can see the code is stored in “cells”“ or 'chunks” like this: You can also enter the code in the cells directly at the R command prompt.

The following code snippet will take care of downloading the data to a temporary file, and extract the file we are interested in, “PERSON.TXT”, from the zipfile.

The column INJSEV_IM contains imputed values for the severity of the injury, but there is one value that might complicate analysis - level 6 indicates that the person died prior to the crash.

Regardless of the way you cleanup this data, we will most assuredly want to drop the column INJ_SEV, as it is the non-imputed version of INJSEV_IM and is a pretty severe data leak - there are others as well.

Don't be alarmed if this cell block takes quite a bit of time to run - the data is of non-negligible size.

Additionally the ridge classifier is running several times to compute an optimal penalty parameter, and the gradient boosting classifier is building many trees in order to produce its ensembled decisions.

First the linear model: Then the GBM: trainy &lt;- traindf\$INJSEV_IM gbm_formula &lt;- as.formula(paste0('INJSEV_IM ~ ', paste(colnames(traindf[, -response_column]), collapse = ' + '))) gbm_model &lt;- gbm(gbm_formula, traindf, distribution = 'bernoulli', n.trees = 500, bag.fraction = 0.75, cv.folds = 5, interaction.depth = 3) print('Started fitting LASSO') And finally, we make a decison tree: Now we can make predictions.

Now we can make predictions using our trained models: testx_dm &lt;- data.matrix(testdf[, -response_column]) predictions_lasso &lt;- predict(lasso_model, newx = testx_dm, type = 'response', s = 'lambda.min')[, 1] predictions_ridge &lt;- predict(ridge_model, newx = testx_dm, type = 'response', s = 'lambda.min')[, 1] predictions_dtree &lt;- predict(dtree_model, testdf[, -response_column]) We can now assess model performance on the test set.

In order to avoid overfitting you will want to separate some of the data and hold it in reserve for when you evaluate your models - some of these models are expressive enough to memorize all the data!

Of course, data science is more than just gathering data and building models - it's about telling a story backed up by the data.

When it is late at night, are there more convertibles involved in crashes than other types of vehicles (this one involves looking at a different dataset with the GES data)?

R for Data Science

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