Walmart-Trip-Type-Classification - Kaggle Competition

(https://kaggle2.blob.core.windows.net/competitions/kaggle/4654/media/walmart_triptypes640.png)]

Improve Walmart’s segmentation process by classifying the customer trip types based on the product purchased in that visit, so that it creates the best shopping experience for every customer.

Dataset information!

• Kaggle competition - https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
• Number of records - 647,054 with 38 trip types

Challenges

• Each record was representing an item instead of a visit. We grouped the records by their visit number to classify the trip.
• Records with missing values - removed the records with NULL, Blank values
• Dummy variables(Categorical) - Weekday; Converted qualitative values to quantitative. Eg: Monday =1, Tuesday =2.
• Duplicate department labels. Eg: “MENSWEAR” and“MENS WEAR”.

Inferences and assumptions

• Trip Type Class 3 - Financial Services, impulse merchandise.
• Trip Type Class 5 - Pharmacy, Personal care
• Trip Type Class 36 - Personal care, beauty, pharmacy
• Trip Type Class 37 - Produce,grocery dry,Meat, Dairy
• Trip Type Class 38 - Dairy, bread, breakfast foods,grocery dry

Approach

• Initial model were Decision Trees (40%), KNN(61%), Random forest(54.77%). Analysis produced low accuracies for all three models.
• We performed feature engineering.
• • Multidimensional data (several rows per customer visit).
• • From the data visualization, the trip type is more dependent on department description. Hence feature engineering from long to wide format.
• • From 7 dimensions to 71 dimensions.
• • Added individual departments as additional columns.
• • Aggregated total number of products bought on each visit.
• • Aggregated total number of products bought in each department on each visit.
• • Added a Return column representing product returns.
• • Dimensions of resulting data: 15195 x 71.

Result after feature engineering

Model	Training accuracy	Testing accuracy
Random Forest	98%	88%
KNN (k=5)	89%	86%
Linear Discriminant Analysis	86%	86%
Support Vector Machines	90%	89%
Logistic Regression	91%	90%

Fine tuning

• 71 features can be reduced and sparsity can be handled using fine tuning.
• PCA - Experimented with different number of components: 1, 5, 10, 25, 30,70, yielding the same accuracy for both train and test sets.
• Recursive Feature Elimination - Experimented ranking different number of features: 10, 20, 30, 40, 60 and found that models produced a very low testing and training accuracy - underfitting
• L1 Regularization - Performed Cross-Validated Logistic Regression to get best (C) lambda [1,1,74,15,1] for classes[3,5,36,37,38].
• This selected 63 important features for all classes.

Conclusion

• Transforming the raw data into features influenced the accuracy of the model on unseen data.
• Feature engineering and L1 Regularization helped fine tune the Logistic Regression model.
• PCA and Recursive Feature Selection failed to improve the selected models.