HenryPaik1/WalmartTripTypeClassification

kaggle Walmart-Trip Type Classification Data Analysis: 38가지 TripType 분류

Trip Type Classification

• Kaggle: https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
• Use gradient boosting
• Key features: ** Department > FinelineNumber > Upc**

Data Fields

• TripType - a categorical id representing the type of shopping trip the customer made. This is the ground truth that you are predicting. TripType_999 is an "other" category.
• VisitNumber - an id corresponding to a single trip by a single customer
• Weekday - the weekday of the trip
• Upc - the UPC number of the product purchased
• ScanCount - the number of the given item that was purchased. A negative value indicates a product return.
• DepartmentDescription - a high-level description of the item's department
• FinelineNumber - a more refined category for each of the products, created by Walmart

Key concept

PFA: Principal Feature Analysis

• PFA is a kind of feature selection(http://www.ifp.illinois.edu/~qitian/e_paper/icip02/icip02.pdf)
• Use the weight of PC as coordinate for each original feature
• Implement K means Clustering and find centroid
• Calculate euclidean distance between each centroid and elements
• Select n_features which are close to each centroid

Code

• join_dataframe.ipynb: overview of a couple of dataframes which construct train data set
• make_dataframe.ipynb: explain details about how to create dataframes in join_dataframe.ipynb