Walmart recruiting store sales forecasting

Allstate Claim Prediction Challenge (AllState2)

A key part of insurance is charging each customer the appropriate price for the risk they represent.

Risk varies widely from customer to customer, and a deep understanding of different risk factors helps predict the likelihood and cost of insurance claims. The goal of this competition is to better predict Bodily Injury Liability Insurance claim payments based on the characteristics of the insured customer’s vehicle.

Many factors contribute to the frequency and severity of car accidents including how, where and under what conditions people drive, as well as what they are driving.

Bodily Injury Liability Insurance covers other people’s bodily injury or death for which the insured is responsible. The goal of this competition is to predict Bodily Injury Liability Insurance claim payments based on the characteristics of the insured’s vehicle.

Files

Train.cvs

Test.cvs

Data Description

Each row contains one year’s worth information for insured vehicles. Since the goal of this competition is to improve the ability to use vehicle characteristics to accurately predict insurance claim payments, the response variable (dollar amount of claims experienced for that vehicle in that year) has been adjusted to control for known non-vehicle effects. Some non-vehicle characteristics (labeled as such in the data dictionary) are included in the set of independent variables. It is expected that no “main effects” corresponding will be found for these non-vehicle variables, but there may be interesting interactions with the vehicle variables.

Calendar_Year is the year that the vehicle was insured. Household_ID is a household identification number that allows year-to-year tracking of each household. Since a customer may insure multiple vehicles in one household, there may be multiple vehicles associated with each household identification number. "Vehicle" identifies these vehicles (but the same "Vehicle" number may not apply to the same vehicle from year to year). You also have the vehicle’s model year and a coded form of make (manufacturer), model, and submodel. The remaining columns contain miscellaneous vehicle characteristics, as well as other characteristics associated with the insurance policy. See the "data dictionary" (data_dictionary.txt) for additional information.

Our dataset naturally contained some missing values. Records containing missing values have been removed from the test data set but not from the training dataset. You can make use of the records with missing values, or completely ignore them if you wish. They are coded as "?".

There are two datasets to download: training data and test data. You will use the training dataset to build your model, and will submit predictions for the test dataset. The training data has information from 2005-2007, while the test data has information from 2008 and 2009. Submissions should consist of a CSV file. Records from 2008 will be used to score the leaderboard, and records from 2009 will be used to determine the final winner.

Missing feature values have been kept as is, so that the competing teams can really use the maximum data available, implementing a strategy to fill the gaps if desired. Note that some variables may be categorical (e.g. f776 and f777).

The competition sponsor has worked to remove time-dimensionality from the data. However, the observations are still listed in order from old to new in the training set. In the test set they are in random order.

Walmart Recruiting - Store Sales Forecasting
Use historical markdown data to predict store sales
One challenge of modeling retail data is the need to make decisions based on limited history. If Christmas comes but once a year, so does the chance to see how strategic decisions impacted the bottom line.

In this recruiting competition, job-seekers are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and participants must project the sales for each department in each store. To add to the challenge, selected holiday markdown events are included in the dataset. These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact.

Want to work in a great environment with some of the world's largest data sets? This is a chance to display your modeling mettle to the Walmart hiring teams.

This competition counts towards rankings & achievements. If you wish to be considered for an interview at Walmart, check the box "Allow host to contact me" when you make your first entry.

You must compete as an individual in recruiting competitions. You may only use the provided data to make your predictions.

Files

stores.csv

features.csv.zip

test.csv.zip

train.csv.zip

Data Description

You are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains a number of departments, and you are tasked with predicting the department-wide sales for each store.

In addition, Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data.

stores.csv

This file contains anonymized information about the 45 stores, indicating the type and size of store.

train.csv

This is the historical training data, which covers to 2010-02-05 to 2012-11-01. Within this file you will find the following fields:

· Store - the store number

· Dept - the department number

· Date - the week

· Weekly_Sales - sales for the given department in the given store

· IsHoliday - whether the week is a special holiday week

test.csv

This file is identical to train.csv, except we have withheld the weekly sales. You must predict the sales for each triplet of store, department, and date in this file.

features.csv

This file contains additional data related to the store, department, and regional activity for the given dates. It contains the following fields:

· Store - the store number

· Date - the week

· Temperature - average temperature in the region

· Fuel_Price - cost of fuel in the region

· MarkDown1-5 - anonymized data related to promotional markdowns that Walmart is running. MarkDown data is only available after Nov 2011, and is not available for all stores all the time. Any missing value is marked with an NA.

· CPI - the consumer price index

· Unemployment - the unemployment rate

· IsHoliday - whether the week is a special holiday week

For convenience, the four holidays fall within the following weeks in the dataset (not all holidays are in the data):

Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labor Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13