WALMART RECRUITING – STORE SALES FORECASTING Chaoyi Liu, Yuqing Lu, Haoran Wu Rajendran, Goutham

The dataset we choose   ·Store - the store number  ·Date - the week  ·Temperature - average temperature in the region  ·Fuel Price - cost of fuel in the region  ·MarkDown1-5 - data is only available after Nov 2011,  ·CPI - the consumer price index  ·Unemployment - the unemployment rate  ·IsHoliday - whether the week is a special holiday week  provided parameters may affect weekly sales, but did not provide weekly sales.   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  provided sales data of 45 stores with up to 99 departments in more than 421,000 records, and didn’t sum each store’s weekly sales up.

Then we integrated two datasets  So initially, we integrated these two massive tables into one that has everything we need with 6,435 records like this:  Store  Date  Temperature  Fuel_Price  MarkDown1-5  CPI  Unemployment  IsHoliday  Weekly_Sales We decide to divide the whole 6,435 records equally into 5 groups each contain 1,287 records by quinquesection from small to big like this: Mark asDescription Level 1DMore than $ 0.00 Level 2C More than $ 497, Level 3B More than $ 748, Level 4A More than $ 1,056, Level 5S More than $ 1,414,343.53

Neural Network Model It is for complicated prediction problems Visualization or understanding of the rules are not needed Accuracy is very important

Result Learning Rate / Training Cycles = 0.03/2000 Accuracy = 70.61% true Dtrue Strue Ctrue Btrue A class precision pred. D % pred. S % pred. C % pred. B % pred. A % class recall 78.32%68.09%76.92%65.00%65.67% It is easy to find out that Accuracy achieve 70.61% when Learning Rate is 0.03 and will increase as well as Training Cycles increasing

Neural Network Weights Node 1Node 2Node 3Node 4Node 5 Node 6 Node 7Node 8Node 9Node 10 Store Date Temperature Fuel_Price MarkDown MarkDown MarkDown MarkDown MarkDown CPI Unemployme nt IsHoliday Bias Hidden Layer :

Class 'S'Class 'A'Class 'B'Class 'C'Class 'D' Node Node Node Node Node Node Node Node Node Node Threshold Output:

Naïve Bayes Accuracy = 18.63% true Dtrue Strue Ctrue Btrue A class precisio n pred. D % pred. S % pred. C % pred. B % pred. A % class recall % 0.00% Why Naïve Bayes performances “idiot” on this sample? Because variable Store, Data to IsHoliday are independent on each other, so: P(Store,Date,Temperature, … & IsHoliday)=P(Store)*P(Date)*…..*P(IsHoliday) Due to so many numbers in columns Store, Date, … IsHoliday that do not repeat, the probability of each Variables is too small. So P(Store)*P(Date)*…..*P(IsHoliday) will be far lower than 1/6435. This means the probability of sales basing such a model is infeasible.

When K = 1, Accuracy = 26% true Dtrue Strue Ctrue Btrue A class precision pred. D % pred. S % pred. C % pred. B % pred. A % class recall 32.11%37.84%15.58%15.00%25.17% When K = 10, Accuracy = 29.03% true Dtrue Strue Ctrue Btrue A class precision pred. D % pred. S % pred. C % pred. B % pred. A % class recall 35.78%50.98%15.50%28.92%6.00% K-NN

Conclusion  MarkDown 1 to 5 has the highest weight as 16 which mean it really makes an enormous impact on the sales. Promotion will increase weekly sales remarkably.  Fuel price and temperature also makes a positive impact, higher price makes higher sales.  CPI and Unemployment rate having a heavy negative impact on the prospects of sales. The higher CPI and unemployment rate, the less weekly sales.  Holidays affect weekly sales slightly. I think customers don’t care whether today is holiday or not, the only reason they buy items is promotion.