Walmart Recruiting - Trip Type Classification

This recruitment competition was with Walmart through Kaggle to categorize and classify customers’ trips to their stores - whether it was a daily dinner trip, weekly grocery trip, special holiday gift trip, or for seasonal clothing purchase. In fact, this was a Multi-class Classification Probabilities competition in which the probability of each classification has to be predicted for each trip by a customer and recorded in Test Dataset, after learning from the Train Dataset.

The competition ran from 26-Oct-2015 to 27-Dec-2015 and 1047 members participated in total. Being recruitment competition, forming of teams was not allowed and the number of participants were lower than other ‘featured’ competitions.

Personally, I like recruitment competitions because no code sharing is allowed in the competition’s public forum. That encourages participants to come-up with their own logic and algorithm selection.

In this competition, Multi-class logarithmic loss was used to evaluate the performance of predicted probabilities.


To predict the probabilities, I used Random Forest with 4-fold repeated cross-validation. It is important to set classProbs=true in trainControl object before passing it into RF model for multi-class probabilities.

trcontrol <- trainControl(method = "repeatedcv", number = 4, repeats = 2, verboseIter = FALSE, returnResamp = "all", classProbs = TRUE)

The complete code for Random Forest model can be found in my Github here.

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A good thing with XGBoost algorithm is that it has a built-in mlogloss evaluation metric type that can be passed as a parameter while training the data.

xgbcv <- = list('objective' = 'multi:softprob', 'eval_metric' = 'mlogloss', 'num_class' = noOfClasses), data = trainMatrix, label = Target, nrounds = cv.round, nfold = cv.nfold)

The complete code for my XGBoost model can be found in my Github here.